Parallel self-assembly is an efficient approach to accelerate the assembly process for modular robots. However, these approaches cannot accommodate complicated environments with obstacles, which restricts their applications. We in previous work consider the surrounding stationary obstacles and propose a parallel self-assembly planning algorithm. With this algorithm, modular robots can avoid immovable obstacles when performing docking actions, which adapts the parallel self-assembly process to complex scenes. The algorithm was simulated in 25 distinct maps with different obstacle configurations and shows a significantly higher success rate, which is more than$80\%$, compared to the existing parallel self-assembly algorithms. For verification in real-world applications, we in this paper develop a multi-agent hardware testbed system. The algorithm is successfully deployed on four omnidirectional unmanned surface vehicles, CuBoats. The navigation strategy that translates the high-level discrete plan to the continuous controller on the CuBoats is presented. The algorithm’s feasibility and flexibility were demonstrated through successful self-assembly experiments on 5 maps with varying obstacle configurations.Note to Practitioners—This paper addresses deploying of self-assembly technologies for modular robots in practical environments with obstacles to facilitate overwater construction tasks or collective transportation systems. Stationary obstacles may severely influence the assembly planning and robot routing processes. Moreover, efficient task coordination, robot navigation, and structure formation are required for large-scale assembly tasks. The algorithm in this work allows all participating robots to navigate online and connect simultaneously to promote efficiency. The strategy presented here endows the robots’ assembly with obstacle-avoidance capability in dense environments. This work will interest those pursuing efficient assembly in scenes with surrounding obstacles. Our hardware experiments demonstrate a concept system and verify the real-time performance of the algorithm under limited computing power. The approach introduced here is not applicable to robots with heterogeneous shapes, three-dimensional target structures, or overcrowded environments with too many obstacles.

Sailboats are purely wind-driven and thus have great potential for long-term voyaging. For robotic sailboats, the constraints on the energy of the control boards, sensors, communication modules, and actuators are crucial to the sustainability of automation. Reducing the control frequency of actuators is crucial for energy conservation. This study proposes an energy-efficient long-short term (EeLsT) approach for sustainable sailing. In EeLsT, long-term and short-term observers are designed to adaptively take control decisions for time-varying environmental influences (e.g., waves and currents). Our approach can be generally applied as an energy management module in sailing robots. It explicitly leverages the sailing motion characteristics and the dynamic model of the robot considering marine disturbances. We have designed an experimental enhanced simulation platform to evaluate motion performance and energy consumption. Both baseline approach and the scheme incorporating EeLsT method (refer to as EeLsT approach in the subsequent sections) have been conducted. In simulation, EeLsT approach saves 31.8% energy. In the real marine environment, experiments are conducted with OceanVoy, a catamaran sailing robot. The results show that 27.4% of the energy is saved during stable sailing. In long-term sailing, compared to the standby mode when the motors are not working, the average power of the full automation mode has increased by no more than 1 W, i.e. 4% relatively.

For researchers or administrators of relevant institutions who need to collect hydrological data of a certain water area, using autonomous sailboats to tow floating detection equipment is an energy-saving and convenient scheme for deploying detectors. However, due to the limited pulling force provided by a single autonomous sailboat, this scheme is not suitable for floating equipment with large masses. This paper proposes a new approach for multiple autonomous sailboats to tow floating objects. A system of linear arrangement and connection of two autonomous sailboats is considered an appropriate solution for towing heavy floating objects because of its ability to provide greater pulling force. The main part of the article introduces a new design of multi sailboat towing system that can tow floating objects to sail with or against wind. Repetitive experiments have been conducted at the test site equipped with a motion capture system to find the best strategy to control the sails and rudder, in order to increase the towing system’s pulling force and tacking success rate. Three connection modes are proposed, compared, and tested. The best one is applied to the sailboat’s towing system and improves its performance.

This paper presents a strategy for predicting the turning radius of a sailing robot with consideration of aerodynamic and hydrodynamic interferences from the marine environment. The turning radius is initially obtained based on three consecutive designated points during the turning process, which is regarded as the baseline method. Subsequently, on the basis of our constructed turning datasets, a model is trained using Gaussian process regression (GPR) to achieve radius prediction. The feasibility and effectiveness of the proposed scheme have been validated in both simulation and experiments (conducted with OceanVoy as shown in Fig. 1). Under experimental circumstances, the Mean Absolute Error (MAE) of the turning radius produced by the trained prediction model is 0.58m. Furthermore, it has been observed that during longterm sailing covering a distance of 1200km, apart from wind speed and robot velocity, the tidal range also has a significant impact on the navigation of sailing robots.

Capturing stationary aerial objects on unmanned surface vehicles (USVs) is challenging due to quasiperiodic and fast floating-base motions caused by wave-induced disturbances. It is hard to maintain high motion prediction accuracy due to the stochastic nature of these disturbances, and perform object capture through real-time tracking due to the limited active torque. We introduce confidence analysis in predictive capture. To address the inaccuracy predictions, we calculate a real-time confidence tube to evaluate the prediction quality. To overcome tracking difficulties, we plan a trajectory to capture the object at a future moment while maximizing the confidence of the capture position on the predicted trajectory. All calculations are completed within 0.2 s to ensure a timely response. We validate our approach through experiments, where we simulate disturbances by executing real USV motions using a servo platform. The results demonstrate that our method achieves an 80% success rate.

Ocean observation is an emerging field, and sailing robots have several promising features (e.g., long-range sailing, environmental friendliness, energy-saving and low-noise) to perform tasks. In this paper, we define an ocean observation mission in a restricted target area as a station keeping problem. Inspired by an orientation-restricted Dubins path method, the robot keeps sailing and collecting data in a smooth reciprocation, where the trajectories consist of sailing against wind segments and turning downwind parts divided by a goal area and an acceptable area. The upwind sailing segments are of interest for data acquisition. However, the system stability can not be guaranteed during the whole reciprocation especially for sailing outside the goal area. Hereby, we refer to a switched systems approach and propose a desired heading generation scheme to realize safe and stable control in both areas. The stability for subsystems is proved with Lyapunov-like functions. The stable station keeping scheme is verified in both simulation and real experiments. Finally, we completed continuous and effective observation within 50 minutes in the goal area with a radius of 50 meters by a catamaran robot named OceanVoy460.

Magnetically driven microrobots hold great potential to perform specific tasks more locally and less invasively in the human body. To reach the lesion area in vivo, microrobots should usually be navigated in flowing blood, which is much more complex than static liquid. Therefore, it is more challenging to design a corresponding precise control scheme. A considerable amount of work has been done regarding control of magnetic microrobots in a flow and the corresponding theories. In this paper, we review and summarize the state-of-the-art research progress concerning magnetic microrobots in blood flow, including the establishment of flow systems, dynamics modeling of motion, and control methods. In addition, current challenges and limitations are discussed. We hope this work can shed light on the efficient control of microrobots in complex flow environments and accelerate the study of microrobots for clinical use.

A solar tracker significantly increases the amount of energy harvested by a floating photovoltaic system by adjusting the pose of its photovoltaic (PV) panels to optimize their exposure to the solar rays. Conventional solar trackers perform such an adjustment using actuators, which is energy-consuming and involves complex structures. In this paper, a novel dual-axis wave-driven solar tracker is proposed where the photovoltaic (PV) panel is adjusted by the inertia force and gravity. Actuators are replaced by brakes to fix the pose of the PV panel. The kinematics and dynamics of the system are investigated, which are further used for the state feedback and the formulation of the control strategy. A sliding-mode observer is used to estimate the effect of winds on the system, which enables the robustness of the system to be enhanced. A motion planning strategy is also developed to track the solar position and minimize the movements of two joints. Indoor experiments are conducted to test the accuracy of the state feedback and dynamic model. The performance of the uncertainty observer is also tested by experiments. At last, field experiments on the real water surface are implemented to verify the feasibility of the proposed system. The results show that the solar tracker is able to adjust the PV panels at a velocity of 8.89 deg/s under the effect of limited base motions with amplitude less than 4°. Note to Practitioners—Traditional solar trackers can be driven passively by chemical energy or actively using actuators. The former cannot function stably in the field environment, and the latter is complex in structure and energy-consuming. For the FPV system under the effect of waves, the solar tracker can utilize the motion of the base to align its PV panel to the solar position. Thus, we present a wave-driven solar tracker without using actuators, and brakes are applied to lock the position of joints. The proposed solar tracker is energy-efficient, has a simpler structure and hardware than the active one, and is more robust than the passive one. We model the system and propose a control scheme to drive the PV panel using inertia and gravity. We also observe the effect of winds to enhance the system robustness. The motion planning strategy is investigated to minimize the movements of the joints. The impact of base motion and control period on the energy-harvesting efficiency is analyzed by simulations. Both indoor and field experiments are implemented to verify the feasibility of the system. The experimental results show that the system performs well even when the base shacking is less than 4°. Note that the control algorithm is a model-based algorithm, which means the model parameters (inertia, gravity, etc.) need to be reidentified for different PV panels and system properties. That is a challenge for large-scale deployment with different configurations. Future research will address the system identification problem and enable the model parameters to be updated automatically.

In this paper, a new capture system for UAV precision landing in a disturbed environment is proposed. Compared with the traditional visual guided landing methods, perching mechanism based methods, and tethered landing methods, the proposed system takes into account the stability during landing process and retains the high accessibility of the UAV. The proposed system consists of a winch subsystem and a magnetic catcher device. They establish an automatic tethered-UAV system for landing before the UAV touchdown. We analyzed the design principle as well as the feasibility of the magnetic catcher. An optimization problem is formulated to obtain a better layout of magnets on the catcher. The problem is relaxed based on interpolation simulation of attraction force. Experiments are conducted both in indoor and outdoor environments based on different UAV platforms respectively. The results validate that the catcher design and the capture system can achieve a successful landing in both cases.

This paper introduces an intelligent system which composes music following the users' instructions. Current auto-matic music generation models are lack of stability. Meanwhile, they cannot satisfy the preference of different people. To overcome these challenges, we train a Transformer-based neural network to generate short music segments using a dataset. A user can compose music pieces by interacting with a well-trained generator. Our system collects the user's feedback during the interactions, and fine-tunes the neural network to optimize the generator. After a large number of interactions, our system can learn the musical taste of the user and customize a personal automatic music composer for him or her. Our work enhances the application value of generative models significantly, which enables people to compose music with the assistance of artificial intelligence.

Superior dexterity and extreme flexibility are typical advantages for concentric cable-driven manipulators working in confined spaces. However, its inverse kinematics and configuration planning are very complicated. In this article, we propose a spatial biarc method for the above problem. The distinguishing feature of this method is that input parameters are two positions and two direction vectors in three-dimensional (3-D) space, and the output is a reasonable spatial biarc for controlling a concentric cable-driven manipulator in 3-D space. This method has the following three advantages. First, the positions and direction vectors of the base and inner distal tip are considered simultaneously. In addition, the length and ratio of the overlapped section and separated section can be adjusted by changing the length of the direction vectors. Furthermore, by judging the angular value of the direction vectors, one can predetermine whether the spatial configuration of the entire arm is C- or S-shaped. The proposed method realizes the parameterization of a concentric cable-driven manipulator, which makes it convenient to intuitively control the manipulator to achieve interference-free motion trajectory planning in confined spaces. Finally, trajectory tracking inspections are simulated and experimentally executed. It can be seen from results that the proposed spatial biarc method can provide reasonable solutions for concentric cable-driven manipulators. The method is especially favorable in terms of 3-D-pose-determination problem and trajectory-planning problem. It can also be applied to other manipulators with similar configurations. Without loss of generality, when the given points and direction vectors are coplanar, the proposed spatial biarc method can be transformed to a planar biarc method.

Obstacle avoidance is crucial for autonomous surface vehicles (ASVs) in the sea because rescue is extremely difficult there. OceanVoy, a sailboat toward long range energy-saving voyage, has to overcome the dual challenges, i.e. from the environmental interference and its low mobility preventing from precise obstacle avoidance. We propose a control scheme based on real-time collision risk assessment and a hybrid propulsion system to enhance safety of OceanVoy. A novel sailboat safety zone (SSZ) has been designed to warn the potential collision during its sailing. Both intrinsic characteristics of OceanVoy and environmental factors have been considered in SSZ. We use lateral and axial thrusters to provide emergency propulsion. A collision avoidance algorithm is executed to coordinate motors in rudder, sail and thrusters based on SSZ. Both simulation and experiments have been conducted and the results have validated our system and collision avoidance scheme.

Self-assembly has attracted growing interests in modular robotics during past decades. Recent work accelerates the assembly process by parallelizing the docking actions among robots. However, these methods can only apply to ideal environments without obstacles. Otherwise, robots will get trapped during the assembly process, due to the complex scenes with obstacles. This paper presents an efficient parallel assembly planning algorithm for modular robots by taking the surrounding obstacles into consideration. By this algorithm, the docking actions are able to avoid immovable obstacles, and therefore parallel self-assembly of robots can adapt to complex environments. To validate the efficacy and generality, the authors have implemented this algorithm in a grid-world simulation environment with 25 distinct maps. The simulation results show a much higher success rate (more than 80%) of our proposed algorithm compared with the existing parallel self-assembly planning algorithms. Finally, the feasibility of the algorithm is affirmed by a self-assembly experiment on the automated guided vehicles (AGVs).

In the process of landing unmanned aerial vehicles (UAVs) on an unmanned surface vehicle (USV), a manipulator can be applied to help the UAV land safely and accurately. However, it is a challenge to control the manipulator on a disturbed USV due to joint velocity constraints and bandwidth limitations. To solve this problem, a predictive control framework is proposed in this paper. We leverage a first-order delay system to describe the kinematics of each joint, and control joint velocities by the model predictive controller (MPC). To generate references for MPC, the motion of the floating base needs to be predicted. We apply the recent approach for motion prediction based on the wavelet network (WN) and modify the network to get smooth trajectories. The accuracy of the modified wavelet network (MWN) for motion prediction is tested on four-hour motion data from the real ocean environment and the smoothness of the generated trajectories is also evaluated. Simulations and experiments are implemented to verify the proposed method, the results show that the average control accuracies are improved by more than 30% and 50% in position and rotation compared with the traditional inverse kinematics (IK) controller for 1 Hz base fluctuation.

This study focuses on the multiphase flow properties of crowd motions. Stability is a crucial forewarning factor for the crowd. To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd, a novel motion structure analysis model is established based on purposiveness, and is used to describe the continuity of pedestrians’ pursuing their own goals. We represent the crowd with self-driven particles using a destination-driven analysis method. These self-driven particles are trackable feature points detected from human bodies. Then we use trajectories to calculate these self-driven particles’ purposiveness and select trajectories with high purposiveness to estimate the common destinations and the inherent structure of the crowd. Finally, we use these common destinations and the crowd structure to evaluate the behavior of newly arriving pedestrians and crowd stability. Our studies show that the purposiveness parameter is a suitable descriptor for middle-density human crowds, and that the proposed destination-driven analysis method is capable of representing complex crowd motion behaviors. Experiments using synthetic and real data and videos of both human and animal crowds have been conducted to validate the proposed method.

This paper presents a novel Cable Climbing Robot CCRobot-III, which is the third version designed for bridge cable inspection tasks, aiming at surpassing previous versions in terms of climbing speed and payload capacity. Benefiting from Split-type Wire-driven design, CCRobot-III can climb along a 90-110mm diameter bridge cable in inchworm-like gait at a speed of up to 12m/min, and carrying more than 40kg payload at the same time. CCRobot-III consists of a climbing precursor and a main-body frame. The two parts are connected and driven by steel wires. The climbing precursor, acting as a mobile anchor, moves quickly on a bridge cable. The mainbody frame, acting as a mobile winch, carries payload and pulls itself to a certain position with steel wires. Both parts have one or two pairs of palm-based gripper, which is the key component for providing strong adhesion to support the robot climbing. Experimental results have shown that CCRobotIII possesses outstanding climbing performance, high payload capacity, and good adaptability to complex conditions of cable surface. Moreover, it has potential engineering applications on the cable-stayed bridge for fieldwork.

Traditional solar trackers often adopt motors to automatically adjust the attitude of the solar panels towards the sun for maximum power efficiency. In this paper, a novel design of solar tracker for the ocean environment is introduced. Utilizing the fluctuations due to the waves, electromagnetic brakes are utilized instead of motors to adjust the attitude of the solar panels. Compared with the traditional solar trackers, the proposed one is simpler in hardware while the harvesting efficiency is similar. The desired attitude is calculated out of the local location and time. Then based on the dynamic model of the system, the angular acceleration of the solar panels is estimated and a control algorithm is proposed to decide the release and lock states of the brakes. In such a manner, the adjustment of the attitude of the solar panels can be achieved by using two brakes only. Experiments are conducted to validate the acceleration estimator and the dynamic model. At last, the feasibility of the proposed solar tracker is tested on the real water surface. The results show that the system is able to adjust 40° in two dimensions within 28 seconds.

In this paper, the 2D robot-to-robot relative pose (position and orientation) estimation problem based on ego-motion and noisy distance measurements is considered. We address this problem using an optimization-based method, which does not require complicated numerical analysis while yields no inferior relative localization (RL) results compared to existing approaches. In particular, we start from a state-of-the-art method named square distances weighted least square (SD-WLS), and reformulate it as a non-convex quadratically constrained quadratic programming (QCQP) problem. To handle its non-convex nature, a semidefinite programming (SDP) relaxation optimization-based method is proposed, and we prove that the relaxation is tight when measurements are free from noise or just corrupted by small noise. Further, to obtain the optimal solution of the relative pose estimation problem in the sense of maximum likelihood estimation (MLE), a theoretically optimal WLS method is developed to refine the estimate from the SDP optimization. Comprehensive simulations and well-designed experiments are presented for validating the tightness of the SDP relaxation, and the effectiveness of the proposed algorithm is highlighted by comparing it to the existing approaches.

This paper proposes a novel modular selfreconfigurable robot (MSRR) "FreeBOT", which can be connected freely at any point on other robots. FreeBOT is mainly composed of two parts: a spherical ferromagnetic shell and an internal magnet. The connection between the modules is genderless and instant, since the internal magnet can freely attract other FreeBOT spherical ferromagnetic shells, and not need to be precisely aligned with the specified connector. This connection method has fewer physical constraints, so the FreeBOT system can be extended to more configurations to meet more functional requirements. FreeBOT can accomplish multiple tasks although it only has two motors: module independent movement, connector management and system reconfiguration. FreeBOT can move independently on the plane, and even climb on ferromagnetic walls; a group of FreeBOTs can traverse complex terrain. Numerous experiments have been conducted to test its function, which shows that the FreeBOT system has great potential to realize a freeform robotic system.

This paper introduces an obstacle-crossing strategy, and the self-reconfiguration algorithm for a new class of modular robots called the rolling sphere, which can fit obstacles represented by cubes of different sizes due to the chain connection of multiple spheres. For the self-reconfiguration of the rolling spheres, a large gradient is obtained by classifying its action types and hierarchically minimizing the distance between the initial configuration and the final configuration. The most direct use of this large gradient is the fast crossing of various obstacles, by jointing multiple self-reconfigurations according to the OctoMap of the obstacles. It is verified in simulation that the self-reconfiguration takes full advantage of the parallel movement of multiple modules to reduce the total time steps, and the obstacle-crossing strategy can adapt to a variety of obstacles.

Towards long range and high endurance sailing, energy is of utmost importance. Moreover, benefiting from the dominance of the sailboat itself, it is energy-saving and environment-friendly. Thus, the sailboat with energy planning problem is meaningful. However, until now, the sailboat energy optimization problem has rarely been considered. In this paper, we focus on the energy consumption optimization of an autonomous sailboat. It has been formulated as a Nonlinear Programming problem (NLP). We deal with it with a hybrid control scheme, in which pseudo-spectral (PS) optimal control method is used in heading control, and a model-free framework guided by Extreme Seeking Control (ESC) is used in sail control. The optimal path is generated with the optimal input motor torques in time series. As a result, both simulation and experiments have validated motion planning and energy planning performance. Notably, about 7% of energy is saved on average. Our proposed method can make sailboats sailing longer and sustainable.

Automatic latching for charging in a disturbed environment for Unmanned Surface Vehicle (USVs) is always a challenging problem. In this paper, we propose a two-stage automatic latching system for USVs charging in berth. In Stage I, a vision-guided algorithm is developed to calculate an optimal latching position for charging. In Stage II, a novel latching mechanism is designed to compensate the movement misalignments from the water disturbance. A set of experiments have been conducted in real-world environments. The results show the latching success rate has been improved from 40% to 73.3% in the best cases with our proposed system. Furthermore, the vision-guided algorithm provides a methodology to optimize the design radius of the latching mechanism with respect to different disturbance levels accordingly. Outdoor experiments have validated the efficiency of our proposed automatic latching system. The proposed system improves the autonomy intelligence of the USVs and provides great benefits for practical applications.

This paper investigates the cooperative moving-target enclosing control problem of networked unicycle-type nonholonomic vehicles with constant linear velocities. The information of the target is only known to some of the vehicles, and the topology of the vehicle network is described by a directed graph. A dynamic control law is proposed to steer the vehicles, such that they can get close to orbiting around the target while the target is moving with a time-vary velocity. Besides, the constraint of bounded angular velocity for the vehicles can always be satisfied. The proposed control law is distributed in the sense that each vehicle only uses its own information and the information of its neighbors in the network. Finally, simulation results of an example validate the effectiveness of the proposed control law.

In this paper, a longitudinal wheel-slip controller for four wheel independent steering and drive (4WISD) vehicles is proposed to suppress longitudinal wheel slip in varying road conditions. Different from conventional methods that consider single driving source and zero steering angle, the proposed controller considers all independent traction sources from each driving wheel and omnidirectional steering command so as to eliminate slip detection errors in 4WISD vehicles. The proposed controller requires low cost sensing equipment, including merely wheel speed sensor and accelerometer, which makes the system practical to be utilized. The proposed wheel-slip controller can be applied to vehicles with arbitrary quantity of driving wheels and different steering configurations such as traditional two-front-wheel steering and two-rear-wheel steering. Numerical simulation results are presented to demonstrate the efficiency of the proposed longitudinal wheel-slip controller.

Known as “the art of strokes”, Chinese calligraphy expresses its aesthetic through the strokes. A calligraphy learner practise the strokes and compose a calligraphic character by the strokes thereafter. Following the same process, the calligraphy robot, Callibot [2] needs to extract the strokes from a character. Therefore, we propose an approach to extract strokes using the geometric properties on the contour(s) of a character. A key discovery is that if two strokes intersect, the contour is concave; otherwise it is convex. The curvature vector defined in [1] is used to locate the vertexes whose interior angles are greater than 180° (these vertexes are named as C-points). C-points separate the contours into sub-contours. The corresponding sub-contours then form the basic strokes (i.e. dot stroke, horizontal stroke, vertical stroke, left-falling stroke and right-falling stroke). The experimental results show that this approach is feasible of extracting strokes from characters. This research is also useful for Chinese character recognition and calligraphic styles classification.

Chinese calligraphy is a unique form of art in the world, whose aesthetic is mainly created by the proper manipulation of the brush. However, it is impossible for a person to figure out the 6-D motion of the brush from calligraphy images, if he has no experience of writing calligraphy. In this paper, we propose a Learning from Demonstration approach for our calligraphy robot, Callibot, to acquire calligraphy skills. We first propose a new stroke parametrization approach. Then we apply Locally Weighted Linear Regression to map from the stroke parameters to the trajectory of the brush. The training data are obtained from several demonstrations. Thereafter, Callibot is capable of writing a new stroke, if the stroke's parameters are given. The resulting motion is as natural as human writing. Experimental results prove the feasibility of our proposed approach. This approach is independent of the robot and is compatible with any robot with six or more degrees of freedom. This approach can be further integrated with our previous research, i.e. stroke extraction, so that Callibot will be able to replicate calligraphy from images.

This paper1presents a novel shape representation approach, Finite Element Contour (FEC), based on studies of shape analysis from the perspective of Finite Element Method (FEM). We assume that an edge of a contour can be modeled as a bendable beam element. Linking finite number of beam elements end to end along the contour, we obtain a closed-loop Finite Element Contour model as an approximate physical model of the original shape. By this model, we can calculate its natural frequency, which is one of mechanical properties that directly related to the geometric shape, and employed it as the shape representation. FEC shape feature possesses translation and rotation invariant properties naturally. We also realized scale and unique affine normalization in few simple steps based on intrinsic physical properties of shape from FEM viewpoint. Experimental results validated that the proposed FEC feature is capable of identifying shape in object recognition task. It also can describe shape deformation. In the well-known MPEG 7 shape retrieval task, the enhanced FEC approach obtains Bullseye score 87.11%.

In this paper, a traction/braking force distribution algorithm for omni-directional all-wheel-independent-drive vehicles is proposed as a tool to enhance driving stability. In the proposed algorithm, the amount of the traction or braking force on each driving wheel can be determined so as to generate a desired tangential force, yaw moment and centripetal force independently. The algorithm considers omni-directional steering command and is capable of handling both traction and braking force commands. The algorithm is applicable on vehicles with at least three independent driving wheels. Simulations have been conducted to illustrate the use of the proposed force distribution method in enhancing vehicles stability.

As one of the most unique types of art in Chinese culture, nowadays Chinese calligraphy is attracting increasing interests from researchers. It will be a big step if we have a robot to write Chinese calligraphy, especially in various styles, and it will form a bridge to combine science with art directly. However, there are so many different types and styles in Chinese calligraphy, and to distinguish them is the most basic quality to a green hand, but it is a big challenge for a robot to do so. For the lack of exploration about this, we conduct a lot of experiments to help the robot to accomplish it automatically. We first propose a parametric representation of calligraphic characters, and then adopt the Mahalanobis distance for similarity measurement and classification. The average accuracies of classifying types and styles of the Chinese calligraphy are 96.36% and 95.61% respectively. During the experiments, some interesting phenomena are discovered through similarity measure. Meanwhile, the parametric representation also has some potential applications, such as defining aesthetic grading standards of calligraphy and synthesizing calligraphy. Based on our research, the calligraphy robot can tell which style of calligraphy it sees for mimicking.

Hand posture is a natural and effective human robot interaction way. In this paper, an user-independent hand posture recognition system using depth and color images captured from an RGB-D camera is presented. To recognize hand posture against complicated background conditions, we propose a novel method for automatic and accurate hand posture segmentation which detects the hand with Chamfer matching, tracks the hand with Kalman filter and segments the hand with region growing algorithm only in the depth space. A new hand posture descriptor invariant to scale, shift and in-plane rotation is constructed with the combination of local contour Fourier descriptor and global Bag-of-Features (BoF) descriptor based on Scale Invariance Feature Transform (SIFT). The sparse representation-based classification (SRC) is applied to perform the hand posture recognition task in the system. Experiments with a self-built large scale hand posture database collected online show the robustness and effectiveness of the proposed system.

The Canadarm2, also named Space Station Remote Manipulator System (SSRMS), is a 7-joint redundant manipulator. Without spherical wrists, the singularity analysis and avoidance of these manipulators are very difficult. In this paper, a method is presented to analytically identify its singular configurations based on the elementary transformation of Jacobian matrix. Firstly, we constructed a general kinematics model to describe them in a united manner. Correspondingly, the differential kinematics equation and the modified form are derived. Secondly, the singularity conditions are isolated and collected in a 3×4 sub-matrix by several times row transformation of the modified Jacobian matrix, which is partitioned into a block-triangle matrix. Finally, all the singularity configurations are determined by analyzing the rank degeneracy conditions of the 3×4 sub-matrix. The proposed method isolates the singularity conditions, and collects them in a 3×4 sub-matrix, largely reducing the computation workload.

In this paper, a deterministic yaw moment controller for four wheel independent steering and drive vehicles is proposed to enhance driving stability and controllability. Different to conventional methods that track a desired yaw rate, the proposed controller stabilizes a vehicle by additionally tracking the heading angle of a vehicle which is more efficient and robust. The heading angle of a vehicle is obtained by a novel method which is based on centripetal force detection. It eliminates the prerequisite knowledge of the characteristics between wheels and road surface which are time varying and difficult to be measured in real time. The proposed system only requires low cost sensing equipment such as wheel speed sensor and accelerometer that makes the system practical to be utilized. The proposed heading angle detection method can be generally applied to any kind of vehicle. The deterministic yaw moment controller is also applicable to any type of four wheel independent drive vehicles.

This paper presents a novel climbing robot called Clothbot which has high maneuverability on flexible clothes. It has a novel gripper consisting of two parallel wheels that can grip continuously and stably on various kinds of clothes. Clothbot also has an omni-directional tail of two DOFs so that it can change its center of gravity to control the moving direction on complex and undeterminate clothes. Consequently, Clothbot is able to access most positions of the clothes by moving straight and turning around with only four motors. It is compact, small and light-weighted but has a load capacity six times its own weight. A series of experiments validate its high performance on flexible clothes.

Collision avoidance of industrial robot arms in varying environment is a challenging task which has been a tough problem for decades. It often requires a large number of sensors and high computational power. Moreover, since the sensors are often mounted on the surface of robot arms, they may affect the appearance of the robot arms and may be vulnerable to damage. This video presents a cost-effective invisible sensitive skin that can cover a large area without utilizing a large number of sensors and it is built inside the robot arm. By using only 5 contactless capacitive sensors and specially designed antennas, collision avoidance of a 6-DOF industrial robot arm is attained.

This paper proposed a novel Tri-star wheeled robot called “Tribot”, which targets on high obstacle performance in unstructured environments, especially at the performance for climbing vertical obstacles. Tribot equips with six Tri-star wheels and each wheel can be driven independently. The chassis of the Tribot is divided into two parts which are connected by an articulated mechanism, making the Tribot has a remarkable obstacle performance to adapt changing environments mechanically, without any interpolate complex control. Numerous experiments have been conducted for vertical obstacle performance tests. Although the diameter of the wheel of the Tribot is only 220 mm, the robot can climb over vertical obstacle of 450 mm high, twice more of the wheel diameter. All results show that Tribot has excellent vertical climbing performance in unstructured environments.

This paper proposes a path and motion planning algorithm for a tree climbing problem. This problem is challenging as the shape of tree is complex and irregular. To our best knowledge, this is the first paper dealing with the path planning problem on natural tree environment. Different from conventional motion planning approach that requires constructing a complex configuration space, this paper divides the planning problem into two parts, i.e., path and motion planning problem so as to reduce the dimension of the problem. An intuitive method to represent a climbing space is proposed that highly simplifies the path planning problem. With the use of a dynamic programming algorithm, an optimal path to reach a target position can be acquired in linear time. In addition, an efficient motion planning algorithm for a tree climbing robot named TreeBot is developed to make TreeBot follow the planned path.

In this paper, we study the stability region for a set of intelligent controllers developed by learning human expert control skills using support vector machines (SVMs). Based on the discrete-time system Lyapunov theory, a Chebychev points based estimation approach is proposed to evaluate the stability region, a key property of this set of SVM-based human learning controllers. One of such learning controllers has been implemented in vertical balance control of a dynamically stable, statically unstable single wheel mobile robot - Gyrover. The experimental results validate the proposed scheme for estimation of the stability region.

The promising electric vehicle (EV) technology is a direction to tackle the global non-renewable energy problem. However, the efficiency to use the electric energy still needs deliberate research. Traditional EV has no choice to manage its energy flow, because it has only one traction motor. With the robotic research in 4 wheel independent drive (4WID), the driving task of the single traction motor can be shared by 4 independent in-wheel motors. By exploring the motor efficiency map, we propose the energy management strategy based on optimal driving torque distribution (ODTD). The total input power of the 4 motors can be minimized while the driving performance is still maintained, and electric energy consumption can be reduced compared with traditional single motor driving EV. Simulation results validate the proposed strategy. The energy management strategy can also be applied to multi-driving-wheel mobile robots.

Battery equalization, aiming at keeping the state of charge of inside cells in the same level, is of great importance to maximize the capacity of whole battery pack and keep cells away from overcharge and overdischarge damage. In this paper, based on the analysis of bi-directional Cuk converter, we have proposed a fuzzy controller to adaptively tune the equalizing current. The inputs of fuzzy controller are selected as the difference in state of charge, the average of state of charge and the total internal resistance. The overall performance of the proposed equalizer is evaluated by multi-indexes such as equalizing speed, efficiency and cell protection. Simulations are conducted based on a well established 6Ah Li-ion battery provided in Advisor. The results under various initial conditions show that the proposed equalizer has the ability to balance the equalizing speed and efficiency. Any pair of cells with difference in state of charge less than 0.3 can be equalized within one hour and with the energy efficiency around 0.95.

Battery fast charging is a crucial issue in both research and application to realize and promote the mass commercialization of electric vehicles, especially pure electric vehicles. However, due to the strong nonlinear properties of batteries, the charging process should take into consideration various factors such as state of charge (SoC), temperature, and charging current, so as to assure the safety, reduce charging time, and enhance charging efficiency. In this paper, we propose a fast charging strategy under the model predictive control framework. Two models are employed to predict SoC and temperature under a sequence of future charging currents. SoC predictor is based on RC equivalent circuit and temperature predictor is based on thermal conduction and convection. The prediction of battery future states allows optimization of the control sequence, with the objectives to follow a predetermined SoC trajectory and to minimize battery temperature rising. Genetic algorithm are introduced to solve the constrained multi-objective optimization problem. The results using Advisor platform demonstrate the availability and efficacy of the proposed framework and prove that it has the ability to reduce charging time and heat generation simultaneously.

In this paper, an omni-directional steer-by-wire interface for four wheel independent steering vehicle is presented. The proposed steering interface is an extension of a traditional steering interface that provides three steering inputs. By combination of which, driver can control the vehicle in traditional way or omni-directionally without any mode switching operation. The reservation of the conventional steering behavior makes driver easy to adapt the novel steering interface. The force feedback controller is designed to synchronize the extended steering interface and the orientations of wheels so as to improve vehicle handling. Hardware-in-the-loop simulations are conducted to verify the hardware prototype and examine the proposed algorithms.

This paper introduces a real-time video surveillance system which detects human abnormal behaviors. We present two approaches to such a problem. The first one employs principal component analysis for feature selection and support vector machine for classification of human behaviors. The proposed feature selection method is based on the border information of four consecutive blobs. The second approach computes optical flow to obtain the velocity of each pixel for determining whether a human behavior is normal or not. Both algorithms are successfully implemented in crowded environments for detecting the human abnormal behaviors, such as (1) running people in a crowded environment, (2) bending down movement while most are walking or standing, (3) a person carrying a long bar and (4) a person waving hand in the crowd. Experimental results demonstrate the two methods proposed are robust and efficient in detecting human abnormal behaviors.