Robotic kitting has attracted considerable attention in logistics and industrial settings. However, existing kitting methods encounter challenges such as low precision and poor efficiency, limiting their widespread applications. To address these issues, we present a novel kitting framework that improves both the precision and computational efficiency of complex kitting tasks. Firstly, our approach introduces a fine-grained orientation estimation technique in the picking module, significantly enhancing orientation precision while effectively decoupling computational load from orientation granularity. This technique combines an SO(2)-equivariant network with a group discretization operation to preciously predict discrete orientation distributions. Secondly, we develop the Hand-Tool Kitting Dataset (HTKD) to evaluate different solutions in handling orientation-sensitive kitting tasks. This dataset comprises a diverse collection of hand tools and synthetically created kits, which reflects the complexities of real-world kitting scenarios. Finally, a series of experiments is conducted to evaluate the performance of the proposed method. The results demonstrate that our approach offers an excellent balance between success rates and computational efficiency in high-precision robotic kitting tasks.

This paper presents a learning based motion planning method for robotic manipulation, aiming to solve the asymptotically-optimal motion planning problem with nonlinear kinematics in a complex environment. The core of the proposed method is based on a novel neural network model, i.e., graph wasserstein autoencoder (GraphWAE) network, which is used to represent the implicit sampling distributions of the configuration space (C-space) for sampling-based planning algorithms. Through learning the implicit distributions, we can guide the planning process to search or extend in the desired region to reduce the collision checks dramatically for fast and high-quality motion planning. The theoretical analysis and proofs are given to demonstrate the probabilistic completeness and asymptotic optimality of the proposed method. Numerical simulations and experiments are conducted to validate the effectiveness of the proposed method through a series of planning problems from 2D, 6D and 12D robot C-spaces in the challenging scenes. Results indicate that the proposed method can achieve better planning performance than the state-of-the-art planning algorithms. Note to Practitioners—The motivation of this work is to develop a fast and high-quality asymptotically optimal motion planning method for practical applications such as autonomous driving, robotic manipulation and others. Due to the time consumption caused by collision detection, current planning algorithms usually take much time to converge to the optimal motion path especially in the complicated environment. In this paper, we present a neural network model based on GraphWAE to learn the biasing sampling distributions as the sample generation source to further reduce or avoid collision checks of sampling-based planning algorithms. The proposed method is general and can be also deployed in other sampling-based planning algorithms for improving planning performance in different robot applications.

When a mobile robot is required to perform tasks in the unknown and complex environment, it is critical to have the ability of dynamic obstacle avoidance. However, conventional deep deterministic policy gradient (DDPG) for collision-free navigation can only perceive a fixed number of dynamic obstacles, and thus it cannot adapt to the stochastic working scenario. To overcome the limitation, an improved DDPG algorithm is proposed in this study. It is an exploration to implement the DDPG with long short-term memory (LSTM) network-based encoder to achieve dynamic obstacle avoidance for the mobile robot in the stochastic working scenario, which can encode the variable number of obstacles into a fixed-length representation. Specifically, to facilitate the LSTM network-based encoder, one safe processing rule is designed to guarantee the entire information of the observable obstacles can be represented completely. The LSTM network-based encoder takes the latest environment information of observable obstacles by employing the safe processing rule and generates the fixed length state vector. In addition, continuous state space for mobile robot and obstacles, as well as reward function and action space are designed. Both simulations and experiments are conducted and the results verify that the improved DDPG algorithm can achieve collision-free trajectory with multiple dynamic obstacles well. It helps to reduce the path distance and motion time effectively.

In our daily life, there are many objects represented by cylindrical shapes and ellipsoids. The tops of these objects are formed by elliptic shape primitives. Thus, it is available for a robot to manipulate these objects by ellipse detection. In this work, we propose a novel approach to generating ground truth for training the model based on domain randomization. Using synthetic data generated in this manner, we build an end-to-end deep neural network with a detection backbone and then, combine multiple branches archived from the backbone for sharing the multiple-scale features; further, after employing active rotation filters, the features pass through the region proposal net to form the prediction branches of the box, orientation regression, and object classification; finally, these branches are fused to do ellipse detection, allowing robotic manipulations of cylinders and ellipsoids. To demonstrate the capabilities of the proposed detector, we show the comparison results with the state-of-the-art detector on synthetic and public datasets. The proposed model for ellipse detection and data generation pipeline based on domain randomization in a simulation are evaluated by a series of robotic manipulations implemented in real application scenarios. The results illustrate a high success rate on real-world grasp attempts despite having only been trained on a synthetic dataset. (A video of some robotic experiments is available on YouTube: https://youtu.be/Ueg1XSI2S98).

We propose a parallel massage robot with compliant joints based on the series elastic actuator (SEA), offering a unified force-position control approach. First, the kinematic and static force models are established for obtaining the corresponding control variables. Then, a novel force-position control strategy is proposed to separately control the force-position along the normal direction of the surface and another two-direction displacement, without the requirement of a robotic dynamics model. To evaluate its performance, we implement a series of robotic massage experiments. The results demonstrate that the proposed massage manipulator can successfully achieve desired forces and motion patterns of massage tasks, arriving at a high-score user experience.

A robust finite-time control (FTC) framework using continuous terminal sliding-mode control (SMC) and high-order sliding-mode observer (HOSMO) is discussed to realize the trajectory tracking of flexible-joint robots in this article. Control performances of the robots always suffer from unknown matched and mismatched time-varying disturbances. Traditional SMC exists with a chattering phenomenon and cannot cope with mismatched time-varying disturbances due to its inherent structure property. For this reason, two HOSMOs are devised to estimate the time-varying disturbances on the link and motor side, respectively. Then, by fusing the states and disturbance estimations into a novel terminal sliding-mode surface, a continuous robust FTC scheme is developed. The proposed control strategy can not only handle both matched and mismatched time-varying disturbances but also obtain a finite-time convergence performance. The rigorous finite-time stability analysis of the closed-loop system under the proposed control method is guaranteed. The results are illustrated to verify the effectiveness and robustness of the proposed design approach.

Robotic grasp in complex open-world scenarios requires an effective and generalizable perception. Estimating object’s pose is needed in a variety of practical grasping scenarios. Here we present a novel approach of pose estimation of textureless and textured objects. The algorithm utilizes a single RGB-D image to exploit depth invariant, oriented point pair feature as well as local contextual sensitivity in cluttered environments. To enhance the performance of the voting process and improve learning efficiency, we employ a global pruning algorithm that reduces the risk of overfitting and simplifies the structure of decision trees after compensating for the complementary information among multiple trees by optimizing a designed global objective function. Finally, we also refine the pose obtained from the above stage. The proposed approach of estimating 6-D (degree of freedom) poses of textured and textureless objects is evaluated on publicly available data sets against the recent works under various conditions. It illustrates that our framework is superior to these recent works. Further, we perform extensive qualitative experiments of robotic grasp to illustrate the proposed approach can be applied to practical scenarios.Note to Practitioners—This article is motivated by the problem of the pose estimation of textured and textureless objects in clutter environments. It is difficult for conventional works to address the issue of estimating textured or textureless objects’ poses in such scenarios. We considered that a novel system should be able to obtain the 6-D poses of objects. Therefore, we investigate the combined use of multiple split functions with different characteristics. Learning the model based on Hough forests always cost much computational resource; therefore, we construct a novel pruned Hough forest for solving this issue. Through the comparison and robotic grasp verifications, the behavior of our system can be used in practical applications. In future, we will deploy the proposed system in robotic assembling tasks.

Task space beyond 2D planes brings challenges to the traditional automated guided vehicle (AGV) on goods delivery. In this paper, a novel self-balancing platform is designed for the stable stair climbing of AGV. While the AGV is moving in a constructional site, this mechanism enhances the AGV to handle heavy payload or even carry another operating robot, through keeping them horizontal to the ground over time. In the design, the two degree-of-freedom (DOF) of this mechanism allows the platform to rotate around two mutually perpendicular axes. The rotation ranges are [-15°, 15°] and [0°, 40°], respectively. Through the feedback of an inertial measurement unit (IMU), the self-balancing control can be achieved. The demonstration show that the workspace not only allows the mechanism to reduce the vibration acting on the carried goods when the AGV is driving on uneven terrain but also allows the platform to maintain the center of gravity (CG) while the AGV is climbing.

A robust trajectory tracking control problem for robots with flexible-joint (FJ) is presented in this work. Aiming to obtain a better trajectory tracking performance and reject the adverse impacts of unknown matched and mismatched time-varying disturbances on FJ robotic systems, a robust finite-time control approach based on a terminal sliding mode control (TSMC) method and two finite-time observers (FTOs) is proposed. First, the FTOs are utilized to estimate the matched disturbance and the mismatched disturbance, respectively. With the aid of these estimations, a novel terminal sliding surface is constructed. Then, the proposed robust TSMC scheme is developed, which possesses a property against the matched and mismatched time-varying disturbances in a finite-time. The results are given to verify the effectiveness of the proposed control approach.

Traditional offline programming or teach pendant-based methods limit the collaboration capabilities of users and robots to cope with complex and changing industrial tasks. To address efficient robot manipulation in high-mix and low-volume tasks, especially for skillful tasks involving both trajectory and force control requirements, fast robot teaching and skill transferability are critical. Compared to manually dragging a heavy robot, or programming a trajectory by complex calculations, we believe that robots can efficiently learn skills from direct teaching through telemanipulation, and improve the skills based on optimization with sensory feedback. In aerospace engine, maintenance, repair, and operations, the surfaces of aerospace components are required to be masked by tapes. We propose a fast and intuitive telemanipulation-based method to teach a robot these masking skills and compare the performance of the proposed method with teach pendant-based methods among several users. This study aims to prove the efficiency and intuitiveness of the telemanipulation-based method proposed herein for enabling a robot to learn skillful and complex manipulation tasks.

Quality assessment during postconstruction of buildings is an indispensable procedure in construction industry. This paper describes the design and development of a quality inspection and assessment robot (QuicaBot) that can autonomously scan the entire room using cameras and laser scanners to pick up building defects, such as hollowness, crack, evenness, alignments, and inclination. A robotic system consisting of four types of sensors and a mobile platform as well as the corresponding five types of assessment algorithms is proposed. To the best of our knowledge, this paper is the first attempt to have a complete robotic system for postconstruction quality assessment of buildings. The aim of the developed system is twofold: first, to systematize the manual inspection work through automation resulting in more reliable and objective inspection reports, and then, to speed up the inspection process resulting in a more efficient end product. Based on our experimental on-site tests, the developed novel robot takes only half of the manual inspection time when inspecting the same room. We have also observed that the autonomous assessment results have better inspection accuracy when compared to manual assessments. Last but not least, the results provided by QuicaBot have more consistent measurement accuracy when compared to a manual assessor. Motivated by the initial successful on-site tests and as being a practical mechatronic system illustrating how sensing, sensor fusion, and actuation can be integrated to achieve an intelligent system for building defects assessment, we believe that the QuicaBot-like robots are going to become an integral part of construction industry in the near future.

The ten papers in this special section aim gather the latest research and development works on design, sensing, and intelligent control of robotics and mechatronics systems resulting from the emerging information sharing and design technologies.

Robotic manipulation employs the object detection in images to create a scene awareness and locate an object's pose. In dynamic scenarios, fast multiobject detection and tracking are crucial. Many objects commonly found in household and industrial environments are represented by cylindrical shapes. Thus, it is available for robots to manipulate them through the real-time detection of elliptic shape primitives formed by the circular tops of these objects. We devise an efficient algorithm of the detection of elliptic shape primitives, which in turn enables robust and real-time robotic manipulations of such objects. The proposed algorithm incorporates the information of elliptic edge curvature, splits complex curves into arcs, classifies the arcs into different quadrants of a candidate elliptic shape, determines the quality of arc selection for ellipse fitting, and then retrieves the corresponding elliptic shape primitive. Our algorithm provides either faster or more accurate ellipse detection results than the current state-of-the-art methods, irrespective of challenging scenarios such as occluded or overlapping ellipses. This is verified by performance comparison with six state-of-the-art elliptic shape detection algorithms on four public image datasets. The algorithm has been integrated on robots to demonstrate the ability to carry out accurate robotic manipulations (tracking, grasping, and stacking) of cylindrical objects in real time. We show that the robotic manipulator, empowered by the elliptic shape primitive algorithm, performs well in complex manipulation experiments as well as dynamic scenarios.

Modern automated warehouses are equipped with one or many expensive and sophisticated equipment, such as palletizing robots, automated guided vehicles as well as an automated storage and retrieval system (AS/RS). These equipment are operated manually at many levels. These manual interruptions are accompanied by disadvantages of slow storage and retrieval speed, high operating costs and high frequency of errors in the operations. This paper presents an approach for efficient robotic stowing of items for inventory replenishment in a storage system. The objective is to enable a robotic arm system to stow items into a storage bin system and automatically generate a file to indicate which bin each object is stowed to. This would require a robust object recognition imbued with recognition history such that a previously recognized object is remembered as being stowed, even if it has been obscured by other objects subsequently during the task. A feature confidence aggregation strategy has been implemented to analyze a sequence of images containing a number of objects that are added to the storage system sequentially. The strategy is based on a weighted aggregation of ranked machine-learned classification scores and feature-matching recognition scores. This method is able to produce a high recognition rate and has been applied in the Amazon Robotic Challenge 2017 by Team Nanyang.

In order to build a stable and reliable system for the Amazon Robotics Challenge we went through a detailed study of the performance and system requirements based on the rules and our past experience of the challenge. The challenge was to build a robot that integrates grasping, vision, motion planning, among others, to be able to pick items from a shelf to specific order boxes. This paper presents the development process including component selection, module designs, and deployment. The resulting robot system has dual 6 degrees of freedom industrial arms mounted on fixed bases, which in turn are mounted on a calibrated table. The robot works with a custom-designed top-open extendable shelf. The vision system uses multiple stereo cameras mounted on a fixed calibrated frame. Feature-based comparison and machine-learning based matching are used to identify and determine item pose. The gripper system uses suction cup and the grasping strategy is pick from the top. Error recovery strategies were also implemented to ensure robust performance. During the competition, the robot was able to pick all target items with the shortest amount of time.

This work presents a method for automated generation of navigation plan for autonomous or robotic surface vessel. Historical Automatic Identification System (AIS) data is of significant value to this problem. The method joins AIS locations of a same vessel at different time and locations in a region into a route. Next, it automatically computes navigation plans using nearest neighbour based path retrieval relying on two representations, Ship Feature and Navigation Feature. Before starting service, existing AIS records in the form of ship properties and corresponding route are preprocessed and stored in the form of Ship and Navigation Feature. During online retrieval, given input constraints in vector form, nearest neighbour of this query vector in the same space is found and corresponding path of the neighbour is returned as recommended path. Analysis was done in four and two dimensional spaces for Ship and Navigation Feature respectively. Application of the method is demonstrated in two regions of Australian, covering Bass Strait and Great Australian Bight.

We propose a high efficient learning approach to estimating 6D (Degree of Freedom) pose of the textured or texture-less objects for grasping purposes in a cluttered environment where the objects might be partially occluded. The method comprises three main steps. Given a single RGB-D image, we first deploy appropriate features and the random forest to deduce the object class probability and cast votes for the 6D pose in Hough space by joint regression and classification framework, adopting reservoir sampling and summarizing the pose distribution by clustering. Next, we integrate the auto-context into cascaded Hough forests to improve the efficiency of learning. Extensive experiments on various public datasets and robotic grasps indicate that our method presents some improvements over the state-of-art and reveals the capability for estimating poses in practical applications efficiently.

An autonomous framework to evaluate layout of a piping design in the form of piping and instrumentation diagram (P&ID) according to a set of standards of marine and offshore industry is proposed. The method starts with transforming a P&ID into a vector x in Rd. Transformation is done based on a concept introduced for piping known as Histogram of Connectivity. The proposed descriptor captures two essential properties of P&ID: attributes of each component and connectivity among the components. Next, linear support vector machine (SVM) is used to learn a classifier from existing compliant and noncompliant designs. Subsequently, the linear classifier can be used to check if an unseen design complies with the standards. In addition, to enable follow up on noncompliant design including correction or modification, a method to analyze the reason of noncompliance prediction by the learned SVM model is introduced. The method has demonstrated encouraging performance in two challenging data sets of designs created with advice from experienced engineers in the industry, based on International Convention for the Prevention of Pollution from Ships (MARPOL) and Rules for Classification of Ships of Lloyd's Register.

In this paper, we propose a novel ellipse detection algorithm for synthetic and real images. Existing ellipse detection methods are too slow when used with limited hardware resources. The proposed method demonstrates the capability of detecting ellipses with an excellent accuracy at an acceptable speed level in three public datasets. The excellent performance is attributed to the novel combination of classification of arcs into different quadrants of a candidate ellipse, edge curvature and convexity-concavity analysis, and an elliptic geometry constraint.

This paper describes the outcome of a pilot study of deploying humanoid social robots as a teaching aid in preschool classroom. Sixteen K2 students each from two pre-schools were recruited to test commercially available robots, Pepper and Nao. The robots were assigned to different schools and were programmed to deliver 6 lessons over a span of 3 months. To assess the deployment and the performance of the children, we used Likert-scale based survey to record our observations. One of the survey forms used was TEPI, which measures the performance of the children's behavior based on three major criteria. The TEPI data gathered suggests that during the lessons children display desirable behavior such as critical thinking, imagination and creativity, and social interaction and independence. We also observed classroom atmosphere, classroom management, and class behavior during the lessons. Data from the survey highlights the potential benefits and the challenges of deploying robots as a teaching aid in a real classroom setting.

A robot performs a sequence of motions to animate a given input, e.g., dancing to music or telling a story. Each input is pre-processed to determine labels, e.g., emotions of the music or words in the story. Each label corresponds to multiple motions, and each motion has multiple labels. Therefore, the robot can choose one sequence from multiple motion sequences to animate the input. We aim to choose the best sequence to animate based on the audience's preferences. The audience prefers some motions over others, and each motion has an initially unknown preference value. At the end of the motion sequence, the audience provides feedback which is the sum of the motions' preference values. However, the observation of the feedback is noisy due to the device used to capture the audience's feedback. To select the most preferred sequence, the robot has to determine the sequence to query the audience with, so as to learn the preference values of individual motions from noisy observations of the audience's feedback. By learning the individual motion preference values, the most preferred sequence can be determined. Moreover, the audience may get bored of watching the same single motion in multiple sequences and the preference value will degrade based on the number of times the motion is viewed. We contribute MAK (Multi-Armed bandit and Kalman filter) and show that MAK outperforms least squares regression in selecting the best sequence with lower degradation in our simulation experiments.

In surface treatment processes like plasma spraying and spray painting of workpieces, protecting the uninvolved surface by applying masking tape is a common process. Due to the operation complexity for different geometries, such taping tasks depend on a lot of manual works, which is tedious and tiring. This paper introduces an automatic agile robotic system and the corresponding algorithm to do the surface taping. The automatic taping system consists of a 3D scanner for workpiece 3D model reconstruction, a taping end-effector which is mounted on a robot manipulator to handle the taping task, and a rotating platform that is used to hold the workpiece. The surface covering method and the taping path planning algorithms using the scanned model are introduced. With the implementation of the compliance mechanism, the force feedback and the tape cutting mechanism, the system is able to tape flat, cylindrical, freeform, and grooved surfaces. Experiments conducted on taping an engine inner liner shows that the surface can be covered with uniform taping overlap and very little wrinkle. The proposed system is a useful taping package for industrial applications such as workpiece repairing and surface protection, where surface treatments are involved.

This paper describes the design and development of an automated construction quality assessment robot system (QuicaBot) for hollowness, crack, evenness, alignments and inclination problems. To the best of our knowledge, this work is the first attempt to pave the way towards a fully autonomous robotic system for post construction quality assessment of buildings. The main goal of the novel robot is twofold: to systematize the manual inspection work through automation resulting in more reliable and objective inspection reports, and to speed up the inspection process resulting in a cost reduction. Based-on our initial on-site tests, the developed robot increases the overall efficiency in all the aforementioned five problems.

This paper describes a novel methodology to reduce the effort in automating manual surface finishing processes by bridging the knowledge transfer gap of the manual operator's skills to a robot program. Key process variables (KPVs), i.e., contact force, tool path, and feed rate, of the manual operator performing the task are captured with a “sensorized” hand-held belt grinder, while the changes to the work-piece geometry is captured using a 3-D scanner. The entire manual tool-path strategy is segmented into its primitives or primary strategies before programming an equivalent robotic tool-path and strategy. The manual tool-path primitives are imported into computer-aided-manufacturing software where boundary splines are created to generate the robotic tool-paths. An analytical material removal rate (MRR) model is used to scale the extracted manual KPVs such that the parameters can be executed by the robotic platform, while still maintaining an equivalent material removal profile. In the first experimental trial with the designed robotic finishing strategy using this approach, the work-piece could be finished to within 0.7 mm of the desired shape.

Autonomous navigation in an unknown or uncertain environment is one of the challenging tasks for unmanned aerial vehicles (UAVs). In order to address this challenge, it is necessary to have sophisticated high level control methods that can learn and adapt themselves to changing conditions. One of the most promising frameworks for such a purpose is reinforcement learning. In this paper, a novel model-based reinforcement learning algorithm, TEXPLORE, is developed as a high level control method for autonomous navigation of UAVs. The developed approach has been extensively tested with a quadcopter UAV in ROS-Gazebo environment. The experimental results show that our method is able to learn an efficient trajectory in a few iterations and perform actions in real-time. Moreover, we show that our approach significantly outperforms Q-learning based method. To the best of our knowledge, this is the first time that TEXPLORE has been developed to achieve autonomous navigation of UAVs.

Utilizing construction quality standards for almost perfect building projects ensures future marketability of projects, customer satisfaction and maximization of asset value. This paper describes a novel robot system to autonomously assess the post-construction quality of buildings which is currently done manually by using human inspectors. However, manual inspection always has the disadvantages, such as labile inspection accuracy, being time consuming and indistinct recording. As a novel solution to the aforementioned drawbacks, a mobile robot is equipped with a laser scanner, a thermal camera, an inclinometer and a RGB camera to achieve an autonomous assessment system. This proposed system can assess five types of defects: evenness, alignment, cracks, hollowness, and inclination. A movable trolley with different mechanisms are designed to mount and integrate all these sensors. Its mechanical design with four motors and one linear actuator, which are installed to increase the measurement range of sensors, is also presented. The experimental tests show that the proposed system has a great potential in construction quality assessment area in building sector.

This paper describes a novel methodology for programming grinding tool-paths, tool-path orientations and grinding strategies based on the captured trajectories of a surface finishing tool operated by the Skilled-Operator. In order to extract the grinding parameters and strategy from the trajectory of the skilled operator, the manual tool-path is first segmented into tool-path primitives. The order of the manual tool-path primitives will form the grinding strategy. Next, the robotic tool-path primitives are then generated based on the boundaries of the manual tool-path primitive in a computer-aided-manufacturing software. The orientations of the manual tool-path are used to anchor the orientations of the robotic tool-path. Spherical linear interpolation and spherical spline quaternion interpolation are used to interpolate the orientations for the robotic tool-path points along the cross curves, followed by the flow curves respectively. The programmed robotic tool-paths generated were subsequently applied and proven to be able to grind the work-piece to the desired profile within the desired tolerance.

A labeled motion library, in which robot motions are associated with semantic meanings, e.g., words, is useful for human-robot interaction, as a robot can use it to autonomously select motions to support its non-verbal communication. Manually assigning labels to new motions to a motion library is time consuming. However, a new motion may be similar to motions in the labeled motion library, and can be mapped to existing labels. We formally define motions, labels, and mappings between motions and labels. We use a NAO humanoid robot as a motivating example, though our approach is general for use on a humanoid robot with rotational joints. We explain how we generate motions and labels, define eight distance metrics to determine the similarity between motions, and use the nearest neighbor algorithm to determine the labels of a new motion. The distance metrics are varied across three axes - Euclidean versus Hausdorff, joint angles versus points of interest (postures), and mirrored versus non-mirrored. We evaluate the efficacy of these eight distance metrics, using precision, recall, and computational complexity.

Automating item picking in the e-commerce warehouse is pressing but challenging, due to a massive variety of items, tight environmental constraints and item location uncertainty. In this paper, we present an effective and efficient strategy-based planning approach to implement the robotic picking from shelves for e-commerce. Making full advantage of a gripper with multiple securing methods, differentiated strategies are modeled as picking primitives with different securing methods. A strategy generator is proposed to produce feasible potential pickings as quickly and as successfully as possible. A strategy evaluator considering reachability, collision, object-bias preference and the securing performance is also presented for ranking the picking strategies. Experiments were conducted to validate that the robotic picker is able to plan a picking strategy within 2 ms and pick daily items from the shelves with an average success rate of 68%.

Robots have been shown to assist in education and development of social skills in children. However, there has been no study yet that benchmarks the effectiveness of robots with respect to traditional playtools found inside a classroom, such as pretend play items and blocks. This paper presents the design, development and testing of robotic puppets, which would be used to support teaching in kindergarten education. Different types of robotic puppet design were considered before settling on a glove-type puppet. To benchmark the robot performance, a total of 52 five year-old children were observed, from which quantitative and qualitative data were collected. The result of the study indicates that when playing with the robotic puppets, the performance of the children with respect to thinking and learning, creativity and imagination, and social interaction and independence, is comparable to other traditional playtools.

Recent research suggests children with autism show certain positive social behaviors while interacting with robots without the presence of peer pressure. This paper explores possible use of interactive robots for interaction with children with autism. Logical artificial intelligence, reasoning about beliefs, desires and intentions (BDI model) serve as a basis to construct a set of scenarios. The present invention also describes a novel real-world motivated learning method. It uses a supervised reinforcement learning approach combined with goal creating. Autonomous agent learns problems in the real world through interaction with the patient. Methods and systems for management of brain and body functions and sensory perception, observing/analyzing, interactive behavior are presented. In one instance, the virtual agent is integrated with a computer-aided system for diagnosis, monitoring, and therapy.

This paper describes a methodology for capturing the tacit knowledge of the manual grinding and polishing process. Key Process Variables (KPVs) i.e. contact force, tool path, feed rate, etc. of the manual operator performing the task are captured with a `sensorised' hand-held belt grinder, while the changes to the work-piece geometry is captured using a 3-D laser scanner. These KPVs are fed into an analytical material removal model to generate a material removal profile, which can then be calibrated using the actual material removed determined from the manual surface finishing process. The skill of the surface finishing skill is encapsulated in this material removal model and reduces the need for costly robotic Design of Experiment (DoE) trials with test coupons to develop empirical material removal models. Parts from the production process require different processing variables, but the common objective is to generate various material removal maps in order to manufacture a part of a desired form and dimension. The metal removal rate (MRR) model could then be utilized by the industrial robot to determine suitable polishing parameters to accomplish the polishing task. The characteristics of the skilled worker's captured motions can then be extracted and used for optimization of the industrial robot polishing tool path.

A manipulator with variable stiffness allows the manipulator to adjust its stiffness to fulfill different task requirements. In this paper, a cable-driven manipulator with the ability to significantly regulate its stiffness through tension manipulation is introduced. Variable stiffness is achieved by attaching a novel variable stiffness device along each driving cable, in which the stiffness of the device is a function the cable tension. As cable-driven manipulator has actuation redundancy, the tension distribution can be manipulated even at a stationary pose. Such property allows the cable-driven manipulator to adjust the stiffness of each variable stiffness device, thereby changing the stiffness of the manipulator. The design and analysis of the variable stiffness device is presented. The variable stiffness device uses commercial torsion springs, and has a compact and light-weight design. Experimental and simulation results verified that cable-driven manipulator with such variable stiffness devices is able to achieve significant stiffness regulation.

This paper introduces a skeleton calibration method for orientation measurement sensors based motion capture systems. In this method, the orientation sensors are used to measure the posture of the limbs. A template is used to register the end-effectors' postures (feet, hands for example). Through capturing the limb postures while the end-effectors match with the pre-defined postures on the template, a linear equation system of the skeleton dimensions can be generated based on the human kinematics. The limb dimension parameters can be optimized based on that. The identifiablility of this skeleton dimension is discussed. The symmetric property of the skeleton is also taken into consideration. To demonstrate the method, IMU sensors and a footprint template are used to calibrate the lower limb dimension. Results show the absolute dimension errors can be controlled within centimeter level for this human lower limb calibration. Compared with existing methods, this template based method is a quick and self-contained method which does not need extra measurement devices, and the skeleton model does not have asymmetric problems. Since the inertial MoCap systems are widely used nowadays, this method is useful to generate an accurate skeleton model for precise behavior presentation.

This paper introduces a method to track the spatial location and movement of a human using wearable inertia sensors without additional external global positioning devices. Starting from the lower limb kinematics of a human, the method uses multiple wearable inertia sensors to determine the orientation of the body segments and lower limb joint motions. At the same time, based on human kinematics and locomotion phase detection, the spatial position and the trajectory of a reference point on the body can be determined. An experimental study has shown that the position error can be controlled within 1-2% of the total distance in both indoor and outdoor environments. The system is capable of localization on irregular terrains (like uphill/downhill). From the localization results, the ground shape and the height information that can be recovered after localization experiments are conducted. A benchmark study on the accuracy of this method was carried out using the camera-based motion analysis system to study the validity of the system. The localization data that are obtained from the proposed method match well with those from the commercial system. Since the sensors can be worn on the human at any time and any place, this method has no restriction to indoor and outdoor applications.

To fulfill the needs for accurate trajectory tracking with large displacement in a handheld instrument, a 3-DOF serial compliant mechanism is developed. The mechanism is compact with a total length less than 150 mm and a maximum diameter of 22 mm. Two flexures are developed using different rapid prototyping techniques: one 3-DOF flexural lever made of Vero-Gray by Polyjet and a 1-DOF translational flexure made of stainless steel by Direct Metal Laser Sintering (DMLS). Analytical and Finite Element (FE) models are developed for the proposed flexural mechanisms. Experiments are conducted on a prototype. To improve the tracking accuracy, the hysteretic nonlinearities of the system are modeled using Prandtl-Ishlinskii model. Inverse feedforward controller is implemented to linearize the relationship between input and output. The tracking errors are reduced while maintaining a fast response of the system. The total tracking errors are identified individually for each axis and then compensated. Tracking performances of the tool tip are evaluated experimentally with different inputs. The RMS tracking error of the proposed mechanism is lower than 1 µm in all axes, which is improved more than four times compared to the previous systems.

A novel two degree-of-freedom (DOF) ultrasonic planar motor based on traveling wave has been proposed in this paper. The stator is the key component of the planar motor. The proposed stator is in a bar type, consisting of three piezoelectric ceramic cylinders and a metal driving head. The effective elliptical motion of the driving head tip is formed by the second bending mode and the first longitude mode of the stator. The two working vibration modes are analyzed by FEM software, and the dimensions of the stator are optimized and determined via FEA. As a result of the optimization, frequencies of the two working modes are very close to 22 kHz. The major feature of the design is the multi-DOF motion achieved by a single stator.

A novel dual Halbach array is proposed in this paper to increase the output performance of tubular linear machines. The magnetic field distribution in three-dimensional (3D) space is formulated with Bessel functions analytically. Following that, the modeling of force output for linear machines with three-phase windings is carried out based on Lorentz force law. The formula component of force ripple is separated from the analytical expression of total force. It is found that for multi-phase tubular linear machines, the force output and force ripple are not only closely related to the instantaneous position of the mover, but also the starting position. Therefore, the force output of the linear machine is formulated for three typical starting positions as a function of mover positions. The derived models are simulated with respect to the mover's motion. They are also validated with numerical results from finite element calculation. The study shows that an appropriate starting position of the mover helps to improve the output performance of the tubular linear machines. The proposed analytical force model can also be employed to analyze the influence of the structure parameters on the force output of linear machines with similar structures.

A flexure-based parallel manipulator (FPM) is a closed-loop compliant mechanism in which the moving platform is connected to the base through a number of flexural legs. Utilizing parallel-kinematics configurations and flexure joints, the FPMs can achieve extremely high motion resolution and accuracy. In this work, we focus on the analysis and design of a 3-DOF (θx- θy- Z) zero-torsion FPM for nano-alignment applications. Among various possible zero-torsion parallel kinematics configurations, it is identified that the 3-legged Prismatic-Prismatic-Spherical (3PPS) is a suitable candidate. Based on the concept of instantaneous rotation, the critical kinematic design issues, such as displacement and workspace analyses, are addressed. With these analysis algorithms, the major kinematic parameters are readily determined to meet the task requirements. To achieve a large workspace, beam-based flexure joints are employed in the FPM design. As the beam based Universal (U) flexure joints are able to accommodate the required passive prismatic and spherical motions, each flexure PPS leg can be replaced by a simple flexure PU leg. A research prototype of the 3-DOF 3PU FPM has been developed, which achieves position and orientation resolutions of 20 nm and 0.05 arcsecond throughout a workspace of 5° × 5° × 5 mm, respectively.

This article introduces a method called SLAC (Simultaneous Localization And Capture) to track the spatial location of a human using wearable inertia sensors without additional external assistive global sensing device (e.g., camera, ultrasound, IR, etc.) The method uses multiple wearable inertia sensors to determine the orientation of the body segments and lower limb joint motions. At the same time, based on human kinematics and locomotion phase detection, the spatial position and trajectory of a reference point on the body can be determined. Preliminary experimental study has shown that the position error of SLAC can be controlled stairs within less than 2% error of the total distance travelled for a person to walk around a rectangle on the floor and climb up and down stairs. A benchmark study on the accuracy of SLAC was carried out using the camera-based Motion Analysis® system. The localization data obtained from SLAC tally well with that from the commercial system. The positioning accuracy obtained from SLAC is at least an order of magnitude better than that of GPS. Since the sensors can be worn on the human at any time and any place, this method has no restriction to indoor and outdoor applications and is complimentary to GPS applications.

This paper introduces a method to monitor and capture the velocity, location, and dynamic behavior of a human with a self motion capture suit. To track the location of the subject in motion, a method for monitoring the velocity of a reference root point on the subject is proposed. Velocity from lower limb kinematics and the integration of accelerations are fused through Kalman Filters to achieve smooth, accurate and drift free estimation results. Using this velocity estimation and lower limb kinematics, the spatial location of the subject can be calculated. Based on some preliminary experimental results of walking motion, the RMS error within 0.48% of the velocity is achieved with respect to the commercial optical motion capture system, Motion Analysis. The localization results from the proposed method also tally well with the references. Motions like walking, jumping, running and one leg jumping are properly captured with this self-contained system. Compared with the lab-based systems, since this system does not depend on external devices and has little constraint to the human movements, it can show advantage in everyday practice and sport training in large area or home environments.

The paper describes a Yoga training system that is built based on motion replication technique (MoRep), including hardware, virtual scenario and feedback design. The motion replication technique proposed here can determine the similarity between Yoga master and student's postures and then provide feedback on the incorrect body posture of the student through multimodal channels. The key innovations of this project are also discussed.

The advance of robotics, actuators and manufacturing technology motivates the research on measurement of multiple degree-of-freedom (DOF) rotational motions. A novel laser-based noncontact high-precision spherical displacement measurement methodology has been proposed for spherical actuator applications in this paper. The laser detector is utilized to measure the distance from the target to the detector on several light spots, and thus to calculate the rotation angle of the rigid body in three directions. As there is no physical contact between the laser detector and the moving body, additional mass/moment of inertia and friction on the rotor are avoided, and thus the working efficiency of moving body can be improved. The algorithm of orientation angles has been derived. Experimental apparatuses have been developed to evaluate the working performance of the measurement method. Comparison between experimental and analytical results shows that the proposed method can achieve high-precision measurement for multi-DOF rotational motions. Precision of the laser-based angular displacement measurement method can be improved further by increasing the slot density on the rigid body or using new models of laser detectors.

Replication of human body motion is a very important means to maintain a subject's emotion, knowledge and experience. The replication process requires accurate motion capturing system with sensor technologies to measure postures of human bodies and posture transmission, as well as feedback systems to adjust postures to fit into targeted ones. The sensing and feedback technologies are fundamental building blocks of motion capturing systems, work training system and rehabilitation systems. In particular, the construction of sensing and feedback systems for dynamic postures is much more complicated than that for static postures in terms of the time evolution and non-ridge body. We believe that dynamic postures can be represented by a set of blueprint or code like trajectories of particle of human body movement. Furthermore, we derive that the sensing and feedback systems should be able to establish to directly measure those critical particles without relying on external infrastructures. In the paper, some of those kinds of sensing and feedback devices are presented and some evidences such as feature contours are obtained through analysis of captured data by those devices in order to prove our estimation. We confess that our work is preliminary for this new field, but we hope that the work presented here can lead more efforts to bring out systematic approaches of feature detection and extraction of human postures whose characteristics are different from those of video and audio.

Automatic motion generation for digital character under the real-time user control is a challenging problem for computer graphic research and virtual environment applications such as on-line games. The present study introduces a methodology to generate a glove puppet animation which is controlled by a new input device, called the Smart Glove, capturing the hand motion. An animation system is proposed to generate the puppet animation based on the procedural animation and motion capture data from Smart Glove. As the control of the puppet character in the animation takes into account the design of Smart Glove and the operation of the puppet in reality, the physical hand motion can either activate the designed procedural animation through motion recognition or tune the parameters of the procedural animation to build the new puppet motion, which allows the direct user control on the animation. The potential application and improvement of the current animation system are also discussed.

This paper presents the design and validation of a wearable glove-based multi-finger motion capture device (SmartGlove) with a specific focus on the development of a new optical linear encoder (OLE). The OLE specially designed for this project has a compact size, light weight and low power consumption. The characterization tests also show that the OLE's digital output has good linearity and accuracy. The first prototype of SmartGlove which uses ten OLEs to capture the flexion/extension motion of the 14 finger joints is constructed based on the multi-point sensing method. A user study for the evaluation of SmartGlove using a standard protocol shows high repeatability and reliability in both the gripped and flat hand positions compared with four other evaluated data gloves using the same protocol.

This paper presents the modeling and control of a Flexure-Based Electromagnetic Linear Actuator (FELA) that is employed as a high-precision imprinting actuator of a desktop hot-embossing imprinter. In this work, a systematic approach of obtaining the unknown physical parameters of FELA is presented. Subsequently, these parameters are used to design two model-based PID controllers that allow the FELA to perform high-precision positioning tasks and direct-force imprinting tasks respectively. As the imprinting tasks require the FELA to operate in both position and direct-force control modes, two types of control strategies are explored and their competency of enabling the FELA to perform such tasks are investigated. Lastly, the selected control strategy is implemented on the FELA to assist the hot-embossing imprinter in fabricating micro-channels on polymer substrates via a hot-embossing process.

The human body motion is a very important mean of expressing a person's emotion, knowledge and experience, as well as an effective communication tool in inter-personal interaction. We aim to provide a methodology for seamless integration of movements between the real human and the virtual one in Co-space, enabling motion replication in both directions. We developed the prototype systems consisting of a wearable InterfaceSuit that enables human motion replication and learning in Co-space, and a human-to-human motion replication methodology with multi-modal feedback mechanisms. We employed the wearable inertial sensors and optical linear encoder sensors to capture human body movement, and designed the haptic guidance device - 3 Dimensional Orientation Guide prototype with polyester tactor holder and elastic arm bands made of light-weight material for portability. We integrated sensing and feedback devices to build up a multi-resolution upper arm interfaceSuit to capture finger movements and arm movements (wrist, elbow and shoulder) and as well as wearable vibrotactile devices, sensor systems, and the control system for multimodal feedback.

Evidence from prior research has demonstrated that exocentric views of the environment can facilitate the acquisition of survey knowledge in a virtual environment. The present study examined the effect of different exocentric views on judging the relative direction of objects. During the participants' vertical movement in a virtual room, participants learned the spatial layout in one of three conditions: two-perspective, attentive-elevation, and normal-elevation conditions, where the number of the exocentric perspectives from which the spatial layout was observed was different. After spatial learning, they made the judgment of the relative direction of objects. The analysis of spatial judgment showed that as the number of exocentric perspectives increased, the accuracy improved in the mental representation of spatial vertical information and spatial information in novel directions. Results indicated that the increased number of exocentric perspectives during the vertical movement facilitated the flexible acquisition of survey knowledge. Applications of this study included the design of effective navigation aids in virtual multilevel buildings.

Cable-driven parallel manipulators (CDPMs) are a special class of parallel manipulators that are driven by cables instead of rigid links. Due to the unilateral property of the cables, all the driving cables in a fully-constrained CDPM must always maintain positive tension. As a result, tension analysis is the most essential issue for these CDPMs. By drawing upon the mathematical theory from convex analysis, a sufficient and necessary tension-closure condition is proposed in this paper. The key point of this tension-closure condition is to construct a critical vector that must be positively expressed by the tension vectors associated with the driving cables. It has been verified that such a tension-closure condition is general enough to cater for CDPMs with different numbers of cables and DOFs. Using the tension-closure condition, a computationally efficient algorithm is developed for the tension-closure pose analysis of CDPMs, in which only a limited set of deterministic linear equation systems need to be resolved. This algorithm has been employed for the tension-closure workspace analysis of CDPMs and verified by a number of computational examples. The computational time required by the proposed algorithm is always shorter as compared to other existing algorithms.

This paper presents a ball-joint-like three-degree-of-freedom (3-DOF) permanent magnet (PM) spherical actuator which features a ball-shaped rotor with multiple PM poles and a spherical iron stator with air-core coils. Torque output of this PM spherical actuator is formulated analytically. Based on the torque model, simulation result of the actuator torque variation is presented. In addition, the effect of the stator iron on the torque output is evaluated.

In this paper, we present a novel sensing technique, optical linear encoder (OLE), in which the motion of an optical encoder on a reflective strip is converted to limb joints' goniometric data. A body sensing module is designed to integrate the OLE and an accelerometer. A sensor network of three sensing modules is established via Controller Areas Network (CAN) bus to capture full motion of human arm with a 7-DOF kinematic model. In addition, a statistical study was conducted to confirm the repeatability and reliability of our sensor network. Results demonstrate that the sensor system has strong potential to be used as a low-cost tool for motion capture, and objective arm function evaluation for both short-term and long-term monitoring.

This paper describe an implemented robot lion dance system, developed by a multi-disciplinary team of researchers over the past three years. We aim to use advance robotic technology to develop a mechatronic system that can perform life-size lion dancing with the traditional lion dance outfit. We intend to experiment the fusion of traditional art form and robotic technology so as to stimulate people's interest in the disappearing traditional art form and folk lords, and give new meanings to the new art form. The robot employs an event design of the robotic lion head, upper and lower body and methodology of conveying robotic lion dance into robotic lion motion. In this paper we will first describe the design concept and the architecture of the robot lion. Secondly, we will describe in detail the workings of the robotic lion dance choreography and motion control to mimic a life size lion dancing. Lastly, this paper will highlight three key challenges that were faced during the design of the robot to mimic the actual human lion dance.

This paper develops a physical model of an inertial/magnetic measurement unit by effectively integrating an accelerometer, a magnetometer, and two gyroscopes for low-g motion tracking applications. The proposed model breaks down the errors contributed by individual components, then determines error elimination methods based on sensor behavior and characteristics, and finally constructs a feedback loop for continuous self-calibration. Measurement errors are reduced by adopting a systematic design methodology: 1) tilt errors are minimized through a careful selection of A/D convertor resolution and by making compensation on sensor bias and scale factor; 2) heading errors are reduced by cancelling out nearby ferrous distortions and making tilt-compensation on the magnetometer; 3) errors from gyroscope measurements are eliminated via the least squares algorithm and continuous corrections using orientation data at the steady-state position. Preliminary tests for low-g motion sensing show that the motion tracker can achieve less than ±0.5° accuracy in tilt and less than ±1° accuracy in yaw angle measurement with above-mentioned methods.

A novel self-calibrating sensing technology using miniature linear encoders and Inertial/magnetic Measurement Unit (IMU) provides the accuracy, fast response and robustness required by many body motion processing applications. Our sensor unit consists of an accelerometer, a 3-axis magnetic sensor, 2 gyroscopes and a miniature linear encoder. The fusion of data from the sensors is accomplished by extracting the gravity related term from the accelerometer and consistently calibrating the gyroscopes and linear encoder when the sensor unit is under static conditions. Using the fused sensors, we developed a complete motion processing system that consists of a gateway where the human kinematics modeling is embedded. A time divided multiple access wireless architecture is adopted to synchronize the sensor network at 100Hz. Experimental results show that the combination of the IMU and linear encoder produces a low RMS error of 3.5° and correlation coefficient of 99.01%. A video showing the capture a performer’s upper body motion is also realized.

This paper presents a novel flexural-based nano-positioning actuator that has a positioning accuracy of 10 nm (limited by encoder resolution) throughout a range of 3 mm. A new dual-magnet configuration is introduced to enhance this electromagnetically driven actuator in achieving 60 N/Amp in a compact configuration. In addition, a constant and evenly distributed magnetic flux density is obtained throughout 11 mm of air gap. A new mathematical model is presented to accurately predict the behavior of magnetic field within the effective air gap of this configuration. Complete analytical models for this actuator's electromagnetic drive and flexural-bearing support element are presented. Lastly, a prototype of this actuator is developed for validating the established analytical models and verifying the claimed capabilities.

A robotic puppet is developed for studying motion generation and control of highly articulated biomimic mechatronic systems with anatomical motion data of human in real time. The system is controlled by a pair of data gloves tracking human fingers' actions. With the primitives designed in a multilayered motion synthesis structure, the puppet can realize some complex human-like actions. Continuous full body movements are produced on the robotic puppet by combining and sequencing the actions on different body parts using temporal and spatial information provided by the data gloves. Human is involved in the interactive design of the coordination and timing of the body movements of the robotic puppet in a natural and intuitive manner. The methods of motion generation exhibited on the robotic puppet may be applied to the interactive media, entertainment and biomedical engineering.

This paper presents a large-displacement and decoupled AYZ-flexure parallel mechanism (FPM) using typical large-displacement prismatic joints, and a nonlinear modeling method for these prismatic joints is proposed. The monolithic prismatic joints using notch hinges have large motion range of more than 1 mm, hence, the assembled XYZ-stage is large-displacement. Since the prismatic joints have small parasitic motion error and are orthogonally combined in parallel, the XYZ-stage can achieve the three decoupled translational motions. Exact stiffness and dynamics models are given using the proposed modeling method. The comparison between the proposed method and the classical pseudo-rigid-body (PRB) method is done to the example of the XYZ-stage. Finally, the experiments are conducted to verify the proposed XYZ-stage and the comparison between two methods

In this paper, the dynamics of planar parallel manipulators with joint-coupling is presented. In the manipulator with joint-coupling, the actuator torques are distributed among the coupled joints. Hence, the dynamic behavior of the manipulator and the trajectory planning will be different from those manipulators without joint-coupling. Furthermore, the coupling coefficients of the joint-coupling provide an additional way to adjust the dynamic performance of the manipulator. Because of joint-coupling, the total power consumption along the desired trajectory becomes a function of the coupling coefficients. With a maximum limit on the motor torque output, it is possible to find an appropriate set of coupling coefficients that minimize the total power consumption

Workspace analysis and optimization are important in a manipulator design. As the complete workspace of a 6-DOF manipulator is embedded into a 6-dimensional space, it is difficult to quantify and qualify it. Most of the literatures only considered the 3-D sub workspaces of the complete 6-D workspace. In this paper, a finite-partition approach of the special Euclidean group SE(3) is proposed based on the topology properties of SE(3), which is the product of special orthogonal group SO(3) and Ropf3. It is known that the SO(3) is homeomorphic to a solid ball D3with antipodal points identified while the geometry of Ropf3can be regarded as a cuboid. Furthermore, the solid ball and the cuboid can be parametrically and proportionally partitioned into a number of elements. Therefore, a basis volume element of SE(3) is the product of a basis volume element of Ropf3and a basis volume element of SO(3), which is the product of a basis volume element of D3and its associated integration measure. By this way, the integration of the complete 6-D workspace volume become the simple summation of the basis volume elements of SE(3). Two global performance indices, i.e., workspace volume ratio (Wr) and global condition index (GCI)., are defined over the complete 6-D workspace. An optimization algorithm is developed for a 3RPlowbarPS parallel manipulator to illustrate the effectiveness of the finite-partition approach. As a result, the workspace optimization method is valid although it is computationally intensive

This paper proposes an XYZ-flexure parallel mechanism (FPM) with large displacement and decoupled kinematics structure. The large-displacement FPM has large motion range more than 1 mm. Moreover, the decoupled XYZ-stage has small cross-axis error and small parasitic rotation. In this study, the typical prismatic joints are investigated, and a new large-displacement prismatic joint using notch hinges is designed. The conceptual design of the FPM is proposed by assembling these modular prismatic joints, and then the optimal design of the FPM is conducted. The analytical models of linear stiffness and dynamics are derived using pseudo-rigid-body (PRB) method. Finally, the numerical simulation using ANSYS is conducted for modal analysis to verify the analytical dynamics equation. Experiments are conducted to verify the proposed design for linear stiffness, cross-axis error and parasitic rotation

This paper presents a design concept of spherical actuators including a ball-shaped rotor with two layers of permanent-magnet (PM) poles and a spherical-shell-like stator with two layers of circumferential air-core coils. Corresponding to the poles configuration, the torque model of the spherical actuator has been derived. The magnetic field as well as torque output have been compared with that of spherical actuator with single-layer PM-pole configuration. This generic torque modeling method can be extended for spherical actuators with multi-layer PM&coil poles which can achieve high motion resolution as well as large working range

A new three-limb, six-degree-of-freedom (DOF) parallel manipulator (PM), termed a selectively actuated PM (SA-PM), is proposed. The end-effector of the manipulator can produce 3-DOF spherical motion, 3-DOF translation, 3-DOF hybrid motion, or complete 6-DOF spatial motion, depending on the types of the actuation (rotary or linear) chosen for the actuators. The manipulator architecture completely decouples translation and rotation of the end-effector for individual control. The structure synthesis of SA-PM is achieved using the line geometry. Singularity analysis shows that the SA-PM is an isotropic translation PM when all the actuators are in linear mode. Because of the decoupled motion structure, a decomposition method is applied for both the displacement analysis and dimension optimization. With the index of maximal workspace satisfying given global conditioning requirements, the geometrical parameters are optimized. As a result, the translational workspace is a cube, and the orientation workspace is nearly unlimited.

Orientation workspace analysis is a critical issue in the design of robot manipulators, especially the spherical manipulators. However, there is a lack of effective methods for such analysis, because the orientation workspace of a robot manipulator is normally a subset of SO(3) (the special orthogonal group) with a complex boundary. Numerical approaches appear more practical in actual implementations. For numerical analysis, a finite partition of the orientation workspace in its parametric domain is necessary. It has been realized that the exponential coordinates parameterization is more appropriate for finite partition. With such a parameterization, the rigid body rotation group, i.e., SO(3), can be mapped to a solid sphere D/sup 3/ of radius /spl pi/ with antipodal points identified. A novel partition scheme is proposed to geometrically divide the parametric domain, i.e., the solid sphere D/sup 3/ of radius /spl pi/, into finite elements with equal volume. Subsequently, the volume of SO(3) can be numerically computed as a weighted volume sum of the equivolumetric elements, in which the weightages are the element-associated integration measures. In this way, we can simplify the partition scheme and also reduce the computation efforts, as the elements in the same partition layer (along the radial direction) have the same integration measure. The effectiveness of the partition scheme is demonstrated through analysis of the orientation workspace of a three-degree-of-freedom spherical parallel manipulator. Numerical convergence on various orientation workspace measures, such as the workspace volume and the global condition index, are obtained based on this partition scheme.

An actuator with 3-DOF spherical motion is developed based on layered arrangement of stator coils and rotor poles. Due to the use of air-core coils and permanent magnet poles, the torque model of the actuator cannot be obtained by traditional coenergy approach. This paper describes a generic torque modeling method based on Lorentz force law. The closed-form solution of the torque model is derived from the scalar potentials of the magnetic field. Experimental study on the torque model is carried out. A comparison between the closed-form solution and the experimental result shows that the proposed torque model is valid and can be used for real-time control.

This paper has proposed a design concept of a spherical actuator including a ball-shaped rotor with a full circle of permanent-magnet (PM) poles and a spherical-shell like stator with two layers of circumferential air-core coils. One key feature of this design is parameterization of the PM pole, which benefits the design optimization of the spherical actuator greatly. According to the magnetic field model, the variation of flux density with respect to PM-pole parameters can be revealed. Therefore, these parameters can be appropriately chosen to achieve a high magnetic flux density. Another advantage of this design is the singularity-free, which is verified within the workspace with torque model and condition numbers.

A parallel manipulator (PM) based on 3-limb design termed a selectively actuated parallel manipulator (SA-PM) is proposed. The end-effector of the manipulator can produce 3-DOF spherical motion, 3-DOF translation, 3-DOF hybrid motion, or complete 6-DOF spatial motion depending on the types of the actuation (rotary or linear) chosen for the actuators. The manipulator architecture decouples translation and rotation of the end-effector for individual control. The structure synthesis of SA-PM is achieved using the line geometry. A Lego model of the SA-PM is built for the constructibility and preliminary study. Singularity analysis of the SA-PM based on geometry is presented for all actuation schemes to facilitate the kinematic design of the manipulator. Because of the decoupled motion, the SA-PM has a great potential for high precision motion alignment and assembly, especially in micro- or nano-motion stage design.

The design of a new six-degree-of-freedom (6-DOF) parallel-kinematics machine (PKM) has been proposed. Different from the conventional Stewart-Gough platform which has six extensible legs, the new PKM employs three identical RPRS legs to support the moving platform. Since all joint axes, excluding the three spherical joints at the leg ends, are parallel to each other and perpendicular to the base plane, this 6-DOF PKM presents a promising platform structure with decoupled-motion architecture (DMA) such that translation in a horizontal plane and rotation about a vertical axis are driven by the three active revolute joints, while translation in the vertical direction and rotation about horizontal axes are driven by the three active prismatic joints. As a result, this 6-DOF 3RPRS PKM with DMA has simple kinematics, large cylindrical reachable workspace, and high stiffness in the vertical direction. These features make it appropriate for light machining and heavy parts assembly tasks. Because of the DMA, a projection technique is employed for its kinematics analysis. By projecting the manipulator onto horizontal directions and vertical planes, the kinematics issues such as the displacement, singularity, and workspace analysis are significantly simplified.

This paper considers the capability of collaboration and interaction between agents in a new architecture of multi-agent systems (MAS) basing on the thought of network behavior and co-evolution. Actually, current development of MAS nearly synchronously accompanies with the development of communication networks ever increasingly because the approaches of information exchange are mainly depended on the modes of data transmission and the spectacular of advanced communication technologies. The real detailed complexity inside the media transmission platform is masked in the proposed architecture of MAS so that the complex processes of data dissemination can be coped with reasonably and simply by way of decentralized intelligent agents. According to the simulation result and the comparison with other typical models of MAS, the presented architecture of MAS and its running mechanism exhibit better advantages such as flexibility, robustness and expansibility suited to the collaboration and interaction among decentralized agents in MAS. Toward the future research, some related optimal algorithms and running mechanisms concerning the integrated performance and the complexity of the MAS's architecture will be investigated and developed.

Joint-coupling is introduced in the design and control of parallel manipulators for purposes of singularity management. The idea behind joint-coupling is to drive several joints with one single actuator by mechanical or electronic means, while preserving the mobility of the manipulator. Such joint-coupling can affect the condition of direct singularities of parallel manipulators through its coupling coefficients. As drivability of the end-effector is affected by the direct singularities, a nonlinear optimization method is proposed to determine the appropriate coupling coefficients according to a given set of task postures. The coupling coefficients obtained in this manner can shape the direct singularity loci of the manipulator to ensure a given set of manipulator postures (tasks) free from direct singularities. Examples are included to illustrate the concept of joint-coupling and singularity management.

This paper presents a numerical technique for path planning inside the workspace of parallel manipulators avoiding singularity. A generic numerical algorithm for generating the reachable workspace of parallel manipulators is described. The singularity points are determined inside the workspace. These points are grouped into several clusters and modeled as obstacles. Subsequently, a path planning algorithm is used to find an optimal path avoiding these obstacle. If any singularity point lies on or very close to the path, the path is restructured to avoid the singularity point by a local routing method based on Grassmann's line geometry. The path planning algorithm is uniformly applicable to parallel manipulators with any combinations of revolute and prismatic joints. An example is demonstrated for the effectiveness of the algorithm.

A joystick-based interactive motion control approach is proposed for modular reconfigurable manipulators. Based on the product-of-exponentials (POE) formula, the velocity models as well as the incremental displacement models have been formulated for both serial manipulators (with arbitrary configurations and DOFs) and a class of three-legged parallel manipulators. As a result, two different control modes, i.e., the velocity control mode and the incremental displacement control mode, have been developed. A user-friendly GUI has also been developed, which can display the joystick input, the actual joint angles, and the end-effector pose simultaneously. A 6-DOF serial modular robot and a 6-DOF 3RPRS parallel robot have demonstrated the effectiveness of this approach.

This paper presents a numerical algorithm for the analysis of reachable workspace and singularity representation of three-legged parallel manipulators. After finding out the approximate maximum workspace, a radial and equal area discretization is done. Then, a multitasking search is performed to determine the exact workspace boundary. The volume of the workspace is determined easily by a numerical integration method. Any void inside the workspace is found out. information about its position and stretch is determined and singularity curves inside the workspace is also drawn. A three-legged modular parallel manipulator is considered to demonstrate the effectiveness of the algorithm and found to work quite satisfactorily. The model is based on Product-of-Exponential scheme of formulation and, hence, the algorithm is uniformly applicable to any combinations of revolute and prismatic joints in the configuration of the leg.

In this paper, we propose a 2-DOF parallel mechanism with flexure hinges. The kinematics and static analyses of this mechanism are studied in order to determine the kinematics properties, workspace, holding forces of the actuators and the reaction forces at the flexure hinges. These factors are useful for design process of this mechanism. The pseudo-rigid-body model (PRB model) of this mechanism is formulated. The theoretical results are compared with that obtained from ANSYS.

This paper describes a new 2DOF planar manipulator (PM) using coupled kinematics to manage the singularities. It is designed in order to overcome the singularity configuration of planar manipulators. Manipulators are composed of five rigid links, five revolute joints and two actuators each actuating two of input links coordinated fashion, what we term coupled kinematics. Coupled kinematics is an actuating arrangement. It may be defined as a pair of axial rotations, which are distributed by an actuator at varieties of parameters, which depend transmission ratios of mechanism. Using the geometrical approach, the position of end-effector is obtained. Matching the velocity of end-effector from two branches, the input and output velocities relation can be derived. Considering the coupled kinematics effects, Jacobian matrix is determined. We show that, by mean of proper tuning of the parameters of coupled kinematics, the singularity manifold can be substantially simplified. Finally, note that kinematic coupling can be implemented using mechanical or electronic hardware.

This paper describes the generation of expressive gestures on a robotic puppet controlled using behavior-based method. Recent findings on biological motion mechanisms provide an inspiring model for controlling high dimensional robot with motor primitives. The modular features of primitives make them the building blocks for constructing complex behaviors. The inborn similarity between gesture and speech enable us to study behavior with those methods for language. We designed a motor controller using a small set of primitives. The effectiveness of this method is validated on the robotic puppet with different expressive gestures generated by superimposing and sequencing the primitives.

Locomotion and navigation of a surface walking/climbing robot - Planar Walker, based on a novel planar 8-bar mechanism are studied. The robot moves on a surface through decoupled transverse gaits and turning gaits with finite lengths and finite rotation angles. Motions of the gaits are modeled using planar rigid motion group. Three point-to-point navigation methods are developed for various situations: simple line of sight (SLS), simulated annealing accurate planning (SAAP), and localized hybrid accurate planning (LHAP) algorithms. Computer simulation shows that SAAP produces accurate gait sequences and LHAP saves computation time and resources for long-range targets. However, experiment shows that SLS outperforms SAAP and LHAP as the number of gaits becomes the major criteria in evaluating the gait performance due to imprecision of individual gait movements.

A parallel robot, due to its closed-loop structure, normally has two Jacobian matrices: the inverse and forward Jacobian matrices. Conventional methods for singularity analysis of planar parallel robots are based on analysis of the ranks of the two Jacobian matrices. The inverse singularity has been well studied as the inverse Jacobian matrix always has a simple diagonal form. However, the forward singularity analysis is somehow complicated, partially because some essential geometric relations may be occulted in the formulation of the forward Jacobian matrix. This paper focuses on the forward singularity analysis of a class of 3-RRR planar parallel robots with various actuation schemes. A simple geometric approach based on the concept of instantaneous center is proposed. By analyzing the instantaneous mobility of the moving platform when all the active joints are locked, the necessary and sufficient geometrical conditions for the forward singularity configurations are readily identified. It has been shown that this simple geometrical approach can be employed for singularity analysis of various planar parallel robots and mechanisms.

A closed-form inverse kinematics solver for non-redundant reconfigurable robots is developed based on the product-of-exponentials (POE) formula. Its novelty lies in the use of POE reduction techniques and subproblems to obtain inverse kinematics solutions of a large number of possible configurations in a systematic and convenient way. Three reduction techniques are introduced to simplify the POE equations. Eleven types of subproblems containing geometric solutions of those simplified equations are identified and solved. Based on the sequence and types of robot joints, the solved sub-problems can be re-used for inverse kinematics of different robot configurations. This solver can cope with closed-form inverse kinematics of all robots with DOFs of 4 or less, 90 percent of the 5-DOF robots and 50 percent of the 6-DOF robots, as well as frequently used industrial robots with both prismatic and revolute joints. The solver is implemented as a C++ software package and is demonstrated through an example.

Focusing on the instantaneous velocities of passive joints, a formulation approach is proposed for the instantaneous kinematics and singularity analysis of a class of three-legged parallel robots. Since only four 3/spl times/3 matrices need to be analyzed, the complexity of singularity analysis is significantly, reduced. Using the product-of-exponentials (POE) formula, the kinematic equations possess well-defined algebraic structures so that the instantaneous kinematics and singularity analysis algorithms can be readily and systematically formulated. Three types of singularities, i.e. the forward, inverse, and combined singularities, have been identified. A unified condition for various singularities is proposed. Significant geometric conditions are also presented for identifying singularity configurations that require simple computations.

An instantaneous kinematics and singularity analysis of a class of three-legged, 6-DOF parallel manipulators are addressed. The kinematic relation between the actuator joint rates and end-effector velocity is established using twist annihilators. Then the singularity analysis is performed using the concepts of reciprocal screw and Grassmann line geometry. The instantaneous kinematics model derived using the product-of-exponential formulation, is uniform to any combination of revolute and prismatic joints in the leg. It is shown that reciprocal screws can be easily constructed by using twist annihilators. Based on the line geometry, the geometric conditions are proposed to identify each of the case of singularity configurations for the considered class of parallel manipulators. These geometric conditions are simple and thus, the singularity configurations are readily conceived.

This paper presents a feasible formulation of reachability test for sensor-based navigation. It directly incorporates the distance sensor information and tolerates the sensing and control errors. The concept of obstacle range is proposed, and the intersection between obstacle ranges is investigated based on geometrical reasoning. The necessary and sufficient condition for reachability is derived, along with a reachability test algorithm. Based on obstacle ranges, the algorithm requires no analytical expression of obstacle boundaries. It is computationally efficient and can be applied to sensor-based autonomous robotic systems.

The closed-loop structure of a parallel robot results in complex kinematic singularities in the workspace of the mobile platform. Singularity analysis become important in design, motion planning, and control of parallel robots. Focusing on the instantaneous velocities of passive joints, a new formulation approach is proposed for the instantaneous kinematics and singularity analysis of a class of three-legged parallel robots. Excluding the passive spherical joints at the leg ends, the number of 1-dof passive joints in a three-legged, 6-DOF, parallel robot is three, which is only half of the number of active joints (six). Consequently, the complexity of the singularity analysis is significantly reduced because only four 3/spl times/3 matrices need to be analyzed. Using the product-of-exponential formula, the kinematic equations possess well-defined algebraic structures so that the instantaneous kinematics and singularity analysis algorithms can be readily and systematically formulated. Three types singularities, i.e., the forward, inverse, and combined singularities, have been identified. A unified condition for various singularities is proposed. Significant geometric conditions are also presented for identifying singularity configurations that requires simple computations.

The paper focuses on the kinematic control of redundant modular robots for trajectory tracing. Based on the geometric numerical inverse kinematic algorithm developed for modular robots, a new online control method is presented. In this method, the inverse kinematic solution can be optimized through constructing a weighted matrix. Following this approach, some fundamental interpolation algorithms are proposed for Cartesian space (task space) control of redundant modular robots. The effectiveness of the proposed algorithms has been experimentally demonstrated by a 7-DOF serial modular robot that performs a pick-and-place task with the avoidance of joint angle limits.

The metamorphic underwater vehicle (MUV) is a vehicle that propels in the water by continuously changing the shape of its body similar to the motion of microorganism Amoebae. We describe the basic design of Amoebot, a plastic MUV that achieves shape-changing capability through the inflation and deflation of water-filled balloons. A sequence of inflation and deflation procedure can be taken to produce cyclic swimming shapes that propel the MUV. Swimming shapes similar to microorganism Amoebae have been successfully reproduced by Amoebot. The mechanical system designed proves to be very reliable and flexible in producing desired body shapes. The physical shape of the Amoebot is analyzed numerically and its variation in time during the swim can be expressed explicitly. By formulating sets of idealized swimming rules based on the changing shape, simulations are carried out to predict the trajectory and study the pseudo dynamics of the swimming of Amoebot. Possible applications of this type of underwater vehicle are discussed.

The gait of a multisegment inchworm robot is a series of actuator actions that will change the shape of the robot to generate a net motion. In this article, we model the multisegment inchworm robot as a finite state automaton. Gait generation is posed as a search problem on the graph described by the automaton with prescribed state transitions. The state transitions are defined based on the kinematics of robot locomotion. The auxiliary actuator concept is introduced. Single-stride and multistride gait generations are discussed. Single-stride gaits exhibit fault-tolerant and real-time computation features that are neccessary in actual applications. Both computer simulation and experimental hardware platform are developed for various aspects of the gait generation and planning.

A modular parallel robotic system consists of a collection of individual standard units that can be assembled into various robot configurations for a diversity of task requirements. This paper is focused on the design and kinematic analysis of modular reconfigurable parallel robots. A set of fundamental modules is considered. A local frame representation of the product-of-exponentials (POE) formula, i.e., the local POE formula, is employed for the kinematic analysis of modular parallel robots. Two forward displacement analysis algorithms and a workspace visualization scheme are presented for a class of 3-leg modular parallel robots. Computation examples are also given to demonstrate the effectiveness of the proposed algorithms. The kinematic formulation shows that the local POE formula is a systematic and well-structured method for the kinematic analysis of parallel robots.

Inverse kinematics solutions of a reconfigurable robot system built upon a collection of standardized components are difficult to obtain because of its varying configuration. This paper addresses the formulation of a generic numerical inverse kinematics model and automatic generation of the model for arbitrary robot geometry including serial type and branching type geometry. Both revolute and prismatic types of joints are considered. The inverse kinematics is obtained through the differential kinematics equations based on the product-of-exponential formulas. The Newton-Raphson iteration method is employed for the solution. The automated model generation is accomplished through the introduction of assembly incidence matrix representation of a modular robot assembly configuration and the related accessibility matrix and path matrix. Examples of the inverse kinematics solutions for different types of modular robots are given to demonstrate the applicability and effectiveness of the proposed algorithm.

Manual derivation of the dynamic model of a modular robot is almost impossible because it may have very different geometries and DOFs through module reconfiguration. This paper presents an algorithm to automatically generate the closed-form equation of motion of a modular robot from a kinematic graph based representation of the assembly configuration. We consider modular robots with the more general branching geometry. The formulation of the dynamic model is started with recursive Newton-Euler algorithm. The generalized velocity, acceleration, and forces are expressed in terms of linear operations on se(3), the Lie algebra of the Euclidean group SE(3). Based on the equivalence relationship between the recursive formulation and the closed-form Lagrangian formulation, we use the accessibility matrix of the kinematic graph to assist the construction of the closed-form equation of motion of a modular robot. Applications of the closed-form dynamic model of a branching robot are in robot design, calibration, and motion optimization.

A modular reconfigurable robot system is a collection of individual link and joint components that can be assembled into different robot geometries for specific tasks requirements. However, the machining tolerance and assembly errors at the module interconnections may affect the positioning accuracy of the end-effector. Based on the product-of-exponentials formula and recursive forward dyad kinematics, this paper describes a novel kinematic calibration algorithm for modular robots. The error correction parameters are assumed to be in the relative initial positions of the dyads. A six-parameter calibration method is derived on the ground of a linear superposition principle and differential transformation theory. An iterative least square algorithm is employed for the calibration solution. A simulation example of calibrating a three-module manipulator is demonstrated. The result has shown that the average positioning accuracy of the end-effector increases two orders of magnitudes after the calibration.

A modular robotic system consists of standardized joint and link units that can be assembled into a number of different kinematic configurations. This paper describes the design and kinematic issues of a newly developed modular robot aimed for assembly tasks. All modules are designed as cubic units. There are connecting interfaces, termed connecting ports, on all faces of the cubes so that different kinematic configurations can be achieved by just re-connecting the modules into different ports. A graph based representation scheme, termed assembly incidence matrices (AIM), is employed to indicate the ever changing configurations. The dyad kinematics based on product-of-exponentials formula is introduced. Using dyad kinematics along with a graph traversing algorithm, the authors are able to derive forward kinematics for a modular robot with specific configuration automatically. This formulation can be applied to modular robots with hybrid geometries and is demonstrated by a 3-DOF serial modular robot example.

A "modular" robotic system consists of standardized joint and link units that can be assembled into a number of different kinematic configurations. Given a predetermined set of modules, this paper considers the problem of finding an "optimal" module assembly configuration for a specific task. The authors formulate the solution as a discrete optimization procedure. The formulation is based on an assembly incidence matrix representation of a modular robot and a general task-oriented objective function that can incorporate many realistic task criteria. Genetic algorithms (GA) are employed to solve this optimization problem, and a canonical method to represent a modular assembly in terms of genetic strings is introduced. An example involving a 3-DOF manipulator configuration is presented to demonstrate the feasibility of this approach.

The authors consider how to enumerate the nonisomorphic assembly configurations of a modular robotic system. They introduce an assembly incidence matrix (AIM) to represent a modular robot assembly configuration. Then they use symmetries of the module geometry and graph isomorphisms to define an equivalence relation on the AIMs. Equivalent AIMs represent isomorphic robot assembly configurations. Based on this equivalence relation, the authors propose an algorithm for generating nonisomorphic assembly configurations of an n-link tree-like robot with different joint and link module types. Examples demonstrate that this method offers significant improvement over a brute force enumeration process.

Two-finger antipodal point grasping of arbitrarily shaped smooth 2-D and 3-D objects is considered. An object function is introduced that maps a finger contact space to the object surface. Conditions are developed to identify the feasible grasping region, F, in the finger contact space. A "grasping energy function", E, is introduced which is proportional to the distance between two grasping points. The antipodal points correspond to critical points of E in F. Optimization and/or continuation techniques are used to find these critical points. In particular, global optimization techniques are applied to find the "maximal" or "minimal" grasp. Further, modeling techniques are introduced for representing 2-D and 3-D objects using B-spline curves and spherical product surfaces.>

The authors consider two-finger antipodal point grasping of arbitrarily shaped 2D and 3D objects. An object function which maps a finger contact space to the object surface is introduced. Conditions are developed to identify the feasible grasping region F in the finger contact space. A grasping energy function E is introduced which is proportional to the distance between two grasping points. The antipodal points correspond to critical points of E at F. Optimization and/or continuation techniques are used to find these critical points. In particular, global optimization techniques are applied to find the maximal grasp. Modeling techniques for representing 2D and 3D objects using B-spline curves and spherical product surfaces are described.>