Long-term autonomy requires robust navigation in environments subject to dynamic and static changes, as well as adverse weather conditions. Teach-and-Repeat (T&R) navigation offers a reliable and cost-effective solution by avoiding the need for consistent global mapping; however, existing T&R systems lack a systematic solution to tackle various environmental variations such as weather degradation, ephemeral dynamics, and structural changes. This work proposes LTR$^{2}$, the first cross-modal, cross-platform LiDAR-Teach-and-Radar-Repeat system that systematically addresses these challenges. LTR$^{2}$leverages LiDAR during the teaching phase to capture precise structural information under normal conditions and utilizes 4D millimeter-wave radar during the repeating phase for robust operation under environmental degradations. To align sparse and noisy forward-looking 4D radar with dense and accurate omnidirectional 3D LiDAR data, we introduce a Cross-Modal Registration (CMR) network that jointly exploits Doppler-based motion priors and the physical laws governing LiDAR intensity and radar power density. Furthermore, we propose an adaptive fine-tuning strategy that incrementally updates the CMR network based on localization errors, enabling long-term adaptability to static environmental changes without ground-truth labels. We demonstrate that the proposed CMR network achieves state-of-the-art cross-modal registration performance on the open-access dataset. Then we validate LTR$^{2}$across three robot platforms over a large-scale, long-term deployment (40+ km over 6 months), including challenging conditions such as nighttime smoke. Experimental results and ablation studies demonstrate centimeter-level accuracy and strong robustness against diverse environmental disturbances, significantly outperforming existing approaches.

Persistently exploring ever more sustainable and economically promising alternative protein sources is a grand challenge that humanity must tackle today. Edible insects, as one long-standing food source for mankind, are attracting considerable attention in both academia and capital markets due to the exacerbated global food crisis and new technological advancements. To massively and safely rear edible insects, all stages in their lifecycle must be carefully engineered, monitored, and controlled. This work aims to first investigate the natural mating and ovipositing behaviors of black soldier flies and then engineer an artificial environment that well suits their needs. In particular, a modularized and scalable facility equipped with dedicated light recipe and climate control has been designed and built. The results of our comparative experiments revealed that black soldier flies in the artificial facility tended to have better mating and oviposition quantitatively and qualitatively as compared to the control group in the nature environment. As a result, this work demonstrated that within a well-controlled and comfortable artificial environment, it is possible and feasible to stably breed black soldier flies in large scale. This paves the foundation for industrialized insect-based bioconversion.

This paper proposes a learning-based passive fault-tolerant control (PFTC) method for quadrotor capable of handling arbitrary single-rotor failures, including conditions ranging from fault-free to complete rotor failure, without requiring any rotor fault information or controller switching. Unlike existing methods that treat rotor faults as disturbances and rely on a single controller for multiple fault scenarios, our approach introduces a novel Selector-Controller network structure. This architecture integrates fault detection module and the controller into a unified policy network, effectively combining the adaptability to multiple fault scenarios of PFTC with the superior control performance of active fault-tolerant control (AFTC). To optimize performance, the policy network is trained using a hybrid framework that synergizes reinforcement learning (RL), behavior cloning (BC), and supervised learning with fault information. Extensive simulations and real-world experiments validate the proposed method, demonstrating significant improvements in fault response speed and position tracking performance compared to state-of-the-art PFTC and AFTC approaches. Video and code will be available at https://github.com/HITSZcjh/uav_ftc.

Quadruped manipulators require precise detection of external impact forces to ensure safe and compliant responses during environmental interactions. However, these systems often lack tactile sensors on their body surfaces or force/torque sensors at critical joints. This study introduces a whole-body admittance control framework for quadruped manipulators, utilizing a novel external impact force observer that estimates impact forces acting on the manipulator or the quadruped’s base without relying on dedicated force sensors. The observer leverages the robustness of a super-twisting algorithm (STA) based on the momentum model of quadruped manipulators. Model uncertainties are mitigated using a low-pass filter (LPF) and compensated by ground reaction forces, significantly reducing estimation oscillations during dynamic gaits. By integrating these estimated impact forces, the whole-body admittance control framework enables compliant interactions with the environment and mitigates unsafe behaviors caused by torque saturation through a set-valued feedback loop that constrains command torques within actuation limits, including joint torque boundaries and friction cone constraints of the ground reaction force. Experimental validation across diverse scenarios confirms the effectiveness of this approach in facilitating safe and adaptive interactions between quadruped manipulators and external forces.

Differential-drive robots are widely used in dynamic pedestrian environments, such as hospitals, for time-sensitive tasks like medication delivery, which require high navigation efficiency to ensure timely arrivals. However, existing methods tend to overemphasize safety, resulting in overly conservative behaviors and prolonged navigation times, which in turn lead to reduced efficiency. To address this issue, this paper proposes a novel navigation framework that integrates a pedestrian risk map, modeled using asymmetric Gaussian distributions, into B-spline trajectory optimization. Rather than strictly avoiding high-risk regions, the method balances collision risk and trajectory length minimization, leading to more effective navigation. Additionally, multiple planning modes enhance adaptability in complex environments, ensuring both safety and efficiency. Furthermore, kinematic constraints specific to differential-drive robots are incorporated to ensure the feasibility of the generated trajectories. Simulations and real-world experiments validate the proposed method’s effectiveness in achieving safe and efficient navigation in dynamic pedestrian environments. The video is available at https://youtu.be/S9qJmXyPEzw.

A comprehensive understanding of 3D scenes is essential for autonomous vehicles (AVs), and among various perception tasks, occupancy estimation plays a central role by providing a general representation of drivable and occupied space. However, most existing occupancy estimation methods rely on LiDAR or cameras, which perform poorly in degraded environments such as smoke, rain, snow, and fog. In this paper, we propose 4D-ROLLS, the first weakly supervised occupancy estimation method for 4D radar using the LiDAR point cloud as the supervisory signal. Specifically, we introduce a method for generating pseudo-LiDAR labels, including occupancy queries and LiDAR height maps, as multi-stage supervision to train the 4D radar occupancy estimation model. Then the model is aligned with the occupancy map produced by LiDAR, fine-tuning its accuracy in occupancy estimation. Extensive comparative experiments validate the exceptional performance of 4D-ROLLS. Its robustness in degraded environments and effectiveness in cross-dataset training are qualitatively demonstrated. The model is also seamlessly transferred to downstream tasks BEV segmentation and point cloud occupancy prediction, highlighting its potential for broader applications. The lightweight network enables 4D-ROLLS model to achieve fast inference speeds at about 30 Hz on a 4060 GPU. The code of 4D-ROLLS will be made available at https://github.com/CLASS-Lab/4D-ROLLS.

Cooperative cable-driven transportation with mobile robots connects mobile robots to the payload with cables, rather than attaching mobile robots to the payload. A cooperative cable-driven transportation system can move over low obstacles and reduce the mutual influence of mobile robots through flexible connections. In a large-scale environment, a cooperative cable-driven transportation system could be regarded as a point and sophisticated path planning approaches are available. However, in a small-scale environment with space constraints and obstacles, a cooperative cable-driven transportation system cannot be regarded as a point and the simultaneous path and motion planning of the system is a changeling problem. To this end, this paper proposes a novel Reinforcement Learning (RL)-based planning approach for cooperative cable-driven transportation. The paper also develops sampling-based and optimization-based planning approaches based on the existing planning approaches for Cable-Driven Parallel Robots (CDPRs). The paper applies these planning approaches to cooperative cable-driven transportation scenarios in simulation and in the real world. The time consumption of these planning approaches and the quality of planning results are compared and analyzed. Based on the comparison of these planning approaches, the paper provides a guideline for the selection and development of planning approaches for cooperative cable-driven transportation with mobile robots.

Accurate foot orientation and trajectory estimation are pivotal for advanced gait analysis, yet achieving this with inertial measurement units (IMUs) remains challenging due to their susceptibility to external acceleration, magnetic disturbances, and unbounded position errors. To address these limitations, we propose a multi-kernel correntropy smoother, which effectively mitigates unknown disturbances and enhances orientation accuracy. Furthermore, while the conventional zero-velocity update (ZUPT) method has been widely adopted in IMUs, the impact of incorporating position constraints has been largely overlooked. This paper demonstrates that by incorporating a single loop closure, the maximum positioning error can be reduced by up to 75%, with further reductions achievable through multiple position constraints. Comprehensive theoretical analysis and extensive experimental validation confirm the superior performance of the proposed methods.

To solve the time-optimal problem of velocity planning, various optimization-based methods were proposed in the literature, but these existing methods typically have limitations on completeness and real-time performance. For the scenario of single-axis multipoint (SAMP) motion, this article proposes a global dynamic programming algorithm with local greedy strategies to solve the time-optimal velocity planning problem, which is important for the multiaxis synchronous velocity planning problem. The proposed method, which is called SAMP algorithm, transfers the problem into the splicing problem of interval endpoints and acceleration. Then, based on the assumptions of continuity and monotonicity of piecewise polynomial functions, it derives the optimal motion mapping in these different intervals. Finally, the SAMP algorithm obtains the global time-optimal solution by employing the global dynamic programming with a backtracking algorithm. Simulation and experiments demonstrate that the SAMP algorithm not only has time optimization but also shows good numerical efficiency.

State-of-charge (SOC) is a critical parameter of battery management systems to ensure safe, efficient, reliable and durable battery operations. However, SOC cannot be directly measured and is highly sensitive to different temperatures. Thus, the SOC estimation accuracy and uncertainty management are significant for robust control and energy dispatch. Gaussian process regression (GPR) is thus becoming appealing due to its non-parametric and interpretable probabilistic advantages. But the scalability of GPR is still challenging, suffering from cubic complexity. To solve these challenges, a SOC estimation method is proposed by an end-to-end scalable deep recurrent structure with fast transfer learning at different temperatures. First, convolutional and recurrent neural networks are employed to catch nonlinear temporal dependency within measurements. Second, a GPR layer is concatenated after neural networks, so that the estimation can be quantified with uncertainty while retaining nonlinear expressive ability. Then, a non-parametric fast transfer learning is designed to realize fast transfer between different temperatures. Next, structured sparse approximations and a semi-stochastic gradient procedure are established for scalable training. Finally, the accuracy and fast transfer of the proposed structure are verified through comparison. The structure demonstrates the state-of-the-art performance on estimation accuracy and efficiency with transfer learning faster than fine-tuning strategy by two orders of magnitude.

Lithium-ion batteries are extensively utilized in applications ranging from portable electronics to electric vehicles and renewable energy systems. Accurate prediction of the state of power capacity (SOP) in lithium-ion batteries is fundamental for guaranteeing the safe, reliable, and efficient operation of these systems. However, most existing SOP prediction algorithms only account for the external measurable state constraints of the battery, ignoring the influence of the internal electrochemical states. Using electrochemical models to model batteries can introduce the electrochemical perspective, but many related methods ignore the impact of temperature variations on model parameters. Therefore, this paper proposes an SOP estimation framework based on a physics-informed data-driven approach, which fully integrates the electrochemical model and battery operation data to provide accurate power capacity estimation against temperature effects. First, the battery is modeled using an electrochemical model, and the battery operation data is used to identify the electrochemical temperature-sensitive parameters to enhance the accuracy of the model. Secondly, safety constraints for battery operations are introduced from the perspective of the battery mechanisms and the bisection method is employed to search for the maximum current. Compared with the SOP calibration results and the state-of-the-art method, the results highlight the accuracy of the proposed method. Finally, by referring to the characteristic maps-based method and employing Gaussian process regression, the search interval of SOP is significantly reduced based on historical data, reducing the search time by 80%.

In this article, we propose a distributional policy-gradient method based on distributional reinforcement learning (RL) and policy gradient. Conventional RL algorithms typically estimate the expectation of return, given a state-action pair. Furthermore, distributional RL algorithms consider the return as a random variable and estimate the return distribution that can characterize the probability of different returns resulted by environmental uncertainties. Thus, the return distribution provides more valuable information than its expectation, leading to superior policies in general. Although distributional RL has been investigated widely in value-based RL methods, very few policy-gradient methods take advantage of distributional RL. To bridge this research gap, we propose a distributional policy-gradient method by introducing a distributional value function to the policy gradient (DVDPG). We estimate the distribution of policy gradient instead of the expectation estimated in conventional policy-gradient RL methods. Furthermore, we propose two policy-gradient value sampling mechanisms to do policy improvement. First, we propose a distribution-probability-sampling method that samples the policy-gradient value according to the quantile probability of return distribution. Second, a uniform sample mechanism is proposed. With our sample mechanisms, the proposed distributional policy-gradient method enhances the stochasticity of the policy gradient, improving the exploration efficiency and benefiting to avoid falling into local optimal solutions. In sparse-reward tasks, the distribution-probability-sampling method outperforms the uniform sample mechanism. In dense-reward tasks, the two sample mechanisms perform similarly. Moreover, we show that the conventional policy-gradient method is a special case of the proposed method. Experimental results on various sparse-reward and dense-reward OpenAI-gym tasks illustrate the efficiency of the proposed method, outperforming baselines in almost environments.

In post-disaster scenarios, the lack of medical staff at Casualty Collection Points (CCPs) slows down injury triage. Using robots to apply wristbands to casualties may be a promising solution, but it could potentially pose a risk of further injury due to limb adjustments. To address this, this paper proposes deploying a search and rescue (SAR) robot equipped with a manipulator at CCPs. These robots can autonomously apply wristbands, sparing the need for limb adjustments. Firstly, a SAR robot system and an easily wearable medical sensor wristband were proposed, allowing the system to conveniently place the wristband on human wrists and ankles. Next, to address the challenge of wearing the wristband/anklet without adjusting the injured person's limbs, a 6D pose estimation method for wrists/ankles was introduced. This method enables accurate pose estimation for effective wearing. Subsequently, a control strategy for autonomously wearing the wristband by the manipulator was proposed, along with the development of a method to assess the applicability of the wristband and ensure proper wearing. Finally, a large number of experiments were conducted to wear the wristband, both indoors and outdoors with SAR robots. The performance of the robot was excellent in outdoor large-scale disaster drills.

Understanding the environment is crucial for the autonomous navigation of vehicles. Accurately identifying and removing dynamic objects that cause occlusions and noisy pose issues is crucial to the task. The casualty collection point (CCP) is a designated location for treating casualties during disasters. These sites are commonly located in open fields to ensure that the injured receive timely and appropriate care. Therefore, the construction of a 3D map in CCP may encounter additional challenges. In this paper, we introduces a novel algorithm that integrates a learning-based multi-object detection network with a Kalman Filter tracking framework to remove dynamic object traces from the 3D map building, particularly in CCP scenarios, and it redirects attention on the velocity attribute of dynamic objects at the object-level scale. Additionally, we contribute a new dataset specifically designed for the CCP scenario. Comparative experiments conducted on the SemanticKITTI dataset and CCP dataset show that our proposed method achieves the state-of-the-art performance in removing traces of dynamic objects on 3D Map. Dataset is available at https://github.com/haidongwang96/ccp_dataset.

Quadruped robots are being widely deployed in various scenarios with uneven terrains, such as rescue and supervision, due to their ability to climb obstacles and carry heavy loads. However, these robots struggle when faced with complex tasks that require reaching time-varying multiple target locations or landmarks. The visiting order of the landmarks and the total travel cost can significantly impact their overall work efficiency. The situation is worsened by limited on-board computing resources, restricted battery storage, and real-time computing demand for navigation systems. To address this issue, we propose a novel approach of multi-goal path planning specifically designed for quadruped robots. Our system extends the conventional Rapidly-Exploring Random Tree (RRT) algorithm to optimize the visiting order of multi-goal while taking into account the kinematics and safety-distance of quadruped robots. Simulation and experimental results demonstrate that our proposed multi-goal path planning system is more efficient than traditional methods in navigating complex tasks while reaching each sub-target position.

Traditional trajectory planning methods are challenged by high-dimensional robot navigation, particularly in handling high-velocity obstacles and computation efficiency. This paper introduces a novel approach leveraging Dynamic Control Barrier Functions (DCBF) to address these issues. The proposed method ensures safety and precise obstacle avoidance in dynamic environments, demonstrated through superior performance in mobile manipulator experiments. Key contributions include the design of efficient DCBF functions, real-time trajectory planning under dynamic conditions, and validation of the algorithm's effectiveness, offering a significant advancement for mobile manipulators in complex work settings.

An increasing number of robotic manipulation tasks now use optical tactile sensors to provide tactile feedback, making tactile servo control a crucial aspect of robotic operations. This paper presents a rapid tactile transfer framework (RTTF) that achieves optical-tactile image sim2real transfer and robust tactile servo control using limited paired data. The sim2real aspect of RTTF employs a semi-supervised approach, beginning with pretraining the latent space representations of tactile images and subsequently mapping different tactile image domains to a shared latent space within a simulated tactile image domain. This latent space, combined with the proprioceptive information of the robotic arm, is then integrated into a privileged learning framework for policy training, which results in a deployable tactile control policy. Our results demonstrate the robustness of the proposed framework in achieving task objectives across different tactile sensors with varying physical parameters. Furthermore, manipulators equipped with tactile sensors, allow for rapid training and deployment for diverse contact-rich tasks, including object pushing and surface following.

In normal operations, when quadruped manipulators with impedance control experience external disturbances, they may become unstable and lose balance due to actuation saturation, affecting their stability, safety, and compliance with the environment. To address this issue, we propose a whole-body compliance controller to prevent unstable behaviors like slip, oscillation, and overshoot, which arise from actuation saturation. The controller includes an admittance scheme with a set-valued operator as the internal feedback, to constrain joint torques within actuators’ limits and ground reaction forces within friction cones to ensure stability against disturbances. Then, it formulates a hierarchical optimization problem using the Hierarchical Quadratic Programming (HQP) to impose the output of the admittance scheme while ensuring physical consistency to maintain compliance behaviors. Unlike traditional compliance control with one-dimensional torque limitations, our approach considers both joints torque limits of manipulator joints and friction cones of quadruped ground reaction as actuation saturation. This ensures overall compliance and stability for the quadruped manipulators, even under significant external forces, regardless of where they are exerted on the robot. We demonstrate through experiments involving variable stiffness environments and external forces during normal operations how effective our approach is in enhancing the safety of quadruped manipulators.

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution while its tail shape is heavy-tailed and the extent of heavy-tail is controlled by the kernel bandwidth. Interestingly, when the bandwidth is infinite, the MKC-induced distribution becomes a Gaussian distribution, enabling the MKC to address both Gaussian and non-Gaussian problems by appropriately selecting correntropy parameters. To automatically tune these parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the correntropy parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method.Note to Practitioners—The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian situations. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both theoretical analysis and experiments on magnetometer calibration demonstrate the superiority of the proposed method over the conventional methods. In the future, we will extend the proposed method to more general cases (such as nonlinear regression and classification) and derive new algorithms to accommodate more complex applications (such as with equality or inequality constraints or with prior knowledge of parameter vectors).

Cable-Driven Parallel Mechanisms (CDPMs) are subject to collision-free constraints from birth to the present. The collision-free constraints confine the workspace of CDPMs. To expand the workspace, scholars suggested allowing cables to wrap on rigid bodies (e.g., the end-effector) in recent years, opening new perspectives for CDPMs. However, the modeling and control of a CDPM with cables wrapped on general rigid bodies remain challenging. To this end, this study investigates the necessary conditions for a path of a cable of a CDPM wrapped on a smooth and frictionless rigid body. The solvability of the necessary conditions and the kinematics of the CDPM is explored then. It is shown that the kinematics of CDPMs with cables wrapped on rigid bodies, except for certain simple rigid bodies such as cylinders and spheres, is usually analytically unsolvable. To address the analytically unsolvable kinematics, the study develops a data-driven kinematic modeling and control strategy. The study applies the strategy to control the orientation of spatial rotational CDPM prototypes with wrapped cables and compares the strategy to a classical Jacobian-based kinematic control strategy. Experimental results indicate that for a CDPM allowing cables to wrap on a cylinder, the data-driven kinematic modeling and control strategy outperforms the Jacobian-based kinematic control strategy. For a CDPM allowing cables to wrap on a deformed cylinder, the data-driven kinematic modeling and control strategy can effectively control the CDPM.Note to Practitioners—This paper was motivated by the collision-free constraints of Cable-Driven Parallel Mechanisms (CDPMs) used in wearable devices. The collision-free constraints confine the workspace of the devices and make the devices bloated. This study systematizes a data-driven kinematic modeling and control strategy to allow the cables of a CDPM to wrap on complex rigid bodies and control the pose of the CDPM, for the first time. The necessity and implementation of the data-driven kinematic modeling and control strategy are presented. With the data-driven kinematic modeling and control strategy, the design of a CDPM for a wearable device or a device used in an environment with obstacles is more flexible. The data-driven kinematic modeling and control strategy is tested to demonstrate performance using CDPMs with cables wrapped on different rigid bodies.

Ensuring product quality while reducing costs is critical in manufacturing scenarios. However, real-world operational factories, particularly in the hydrogen storage industries, pose several challenges, including strict quality control standards and limited but extremely biased data available for model development. To address these challenges, we propose an augmented hybrid learning method for visual defect inspection that leverages the strengths of deep learning and unsupervised learning. The proposed method is developed using only one-class OK samples and then validated on a real-world operational manufacturing line. The experiment results demonstrate that our method achieves a recall rate of nearly 90% with an overkill rate of only 0.6%. This method outperforms several benchmark methods that often struggle to balance high recall and low overkill rates. Experiments with industrial setups show that our method provides a promising solution for visual defect inspection in real-world manufacturing scenarios.

This study presents the development and implementation of an advanced hybrid neural network (HNN) model for predicting nitrogen oxide (NO$_{x}$) emissions and controlling ammonia (NH$_{3}$) injection in a 1-GW generator within a 2-GW operational coal-fired power plant. The HNN model, which integrates both endogenous and exogenous input features to effectively analyze complex relationships, shows significant improvement in accuracy with a forecast skill of 22% compared to multiple benchmark models. The real-world application of the HNN-based control strategy resulted in a slight increase in average outlet NO$_{x}$concentration but remained well within the regulated limit of 50 ppm, while reducing the standard deviation from 9.7 to 4.9 ppm, indicating a more stable and controlled outlet NO$_{x}$concentration. The successful deployment of the HNN model in an operational power plant demonstrates its practical applicability and effectiveness in large-scale industrial settings, ultimately supporting the transition toward a sustainable energy future.

Developing a fast and safe charging strategy has been one of the key breakthrough points in lithium battery development owing to its range anxiety and long charging time. The majority of current model-based charging strategies are developed for deterministic systems. Real battery dynamics are, however, affected by model mismatches and process uncertainties, which may lead to constraint violations and even premature aging. This article proposes a fast charging scheme based on distributionally robust model predictive control (DRMPC) against uncertainty. Specifically, a coupled electrothermal-aging model is first introduced to describe the battery behavior, and electrothermal parameters of the adopted model are identified online based on the recursive least-squares algorithm. Subsequently, an online DRMPC-based charging framework is proposed, utilizing the Wasserstein ball centered on the empirical distribution to characterize uncertainty. Finally, the proposed algorithm is compared to model predictive control and constant current-constant voltage algorithms, and its effectiveness is validated on a real battery simulator. Results show that the proposed algorithm can handle the uncertainty effectively while satisfying the constraints, and significantly improve the charging speed.

It is challenging to realize the safe navigation of mobile robots in crowds. Most of the previous studies may lead to unsafe robot navigation in crowds, as safety guarantee is lacked. To solve this problem, we devise a safety filter (SF) that enables realization of safe robot navigation in crowds, and provides safety guarantees by verifying whether the optimal action recommended by an unsafe method is safe and, if not, corrects the action. The three main processes performed by the SF applied to given robot are (1) construction of the safe state constraints of the robot using a safe set; (2) construction of the safe action constraints of the robot based on discrete-time generalized velocity obstacles (DGVOs); and (3) determination of a feasible solution of the SF design problem, or, if none can be found, replacement of the above hard constraints with heuristic soft constraints. We used the SF with a reaction-based method and three learning-based methods in simulation experiments of random and non-random crowds, and the results showed that the SF decreases the collision rates and danger rates and thereby increases the success rates of these methods. We also deployed the SF with three learning-based methods on an mr1000 robot in real-world experiments, and the results showed that the SF enabled the robot using learning-based methods to navigate to its goal without colliding with humans.

Exactly estimating and tracking the motion of surrounding dynamic objects is one of important tasks for the autonomy of a quadruped manipulator. However, with only an onboard RGB camera, it is still a challenging work for a quadruped manipulator to track the motion of a dynamic object moving with unknown and changing velocities. To address this problem, this manuscript proposes a novel image-based visual servoing (IBVS) approach consisting of three elements: a spherical projection model, a robust super-twisting observer, and a model predictive controller (MPC). The spherical projection model decouples the visual error of the dynamic target into linear and angular ones. Then, with the presence of the visual error, the robustness of the observer is exploited to estimate the unknown and changing velocities of the dynamic target without depth estimation. Finally, the estimated velocity is fed into the model predictive controller (MPC) to generate joint torques for the quadruped manipulator to track the motion of the dynamical target. The proposed approach is validated through hardware experiments and the experimental results illustrate the approach's effectiveness in improving the autonomy of the quadruped manipulator.

Snap-fit peg-in-hole assembly widely exists in both industry and daily life, especially for consumer electronics. The buckle mechanism leads to a damping zone inside the port where insertion force needs to be increased. It is much difficult to automate this process by robots, for size and clearance of the components are always small, and the damping buckle should be perceived and distinguished from solid inner walls of the port. End-effector position control might be invalid, since grasping error will make it difficult to locate the plug accurately. In this article, we undertake this assembly challenge by taking advantage of fingertip tactile perception combined with visual images and force feedback. Raw sensor data is collected, processed, and fused together to be state input of a reinforcement learning network, generating continuous action vectors. We also propose a novel damping zone predictor through feature extraction and multimodal fusion, which is able to identify whether the plug has touched the buckle mechanism, so as to adjust the insertion force. The whole framework is implemented through a common USB Type-C insertion experiment on Franka Panda robot platform, reaching a success rate of 88%. Furthermore, system robustness is verified, and comparisons of different modalities are also conducted.

In complex urban environments, the Inertial Navigation System (INS) is important for navigating unmanned ground vehicles (UAVs) for its environment-independency and reliability of real-time localization. It is usually employed as the baseline in the case of other sensors failures, such as the GPS, Lidar, or Cameras. However, one problem for the INS is that its estimation error of localization accumulates over time, and thus the estimated trajectories of the UAVs continue to drift away from their ground truths. To solve this problem, this paper proposes an improved algorithm based on the Invariant Extended Kalman Filter (IEKF) for dead-reckoning of autonomous vehicles, which dynamically adjusts the process noise and the observation noise covariance matrixes through Attention mechanism and Recurrent Neural Network (RNN). The algorithm achieves more robust and accurate dead-reckoning localization in the experiments conducted on the KITTI dataset, reducing the translational error by about 45%compared to the baseline.

Mobile robots are expected to move safely and efficiently in socially-compliant ways in dense crowds. In this paper, a unified multiple-motion-mode framework is proposed to achieve socially-compliant navigation in dense crowds. The proposed framework consists of three successive phases, the identification of pedestrian groups and prediction of their trajectories, the generation of candidate local trajectories of the robot, and the determination of an optimal local trajectory. The pedestrians are grouped according to distances among pedestrians and their velocities and a predictor predicts the groups’ trajectories. Three robot motion modes, moving-solo, pedestrian-following, and courteous-stopping, are proposed based on a systematic analysis and classification of dense crowds and they are utilized to generate candidate local trajectories. A composite metric is proposed to determine the best local trajectory by simultaneously considering the robot motion safety, efficiency, and stability of the trajectory. The proposed robot navigation framework is evaluated both in simulations and real-world experiments with various scenarios. The results show that the proposed framework surpasses the state-of-the-art methods in terms of efficiency and social compliance. Note to Practitioners—This paper is motivated by the socially-compliant navigation problem of mobile robots in densely crowded environments such as hospitals and airports. Robots navigation in these scenes need to follow collective social conventions to ensure ordered and efficient public pedestrian traffics, and individual social conventions for acceptance by human society. Existing approaches generally consider only one type of social conventions cannot accomplish a truly socially-compliant navigation and cannot be naturally accepted in the human pedestrian environment. This paper proposes to imitate the pedestrian’s walking behavior for robot navigation by naturally choosing one of the three walking modes, moving-solo, following, and courteous-stopping. We construct the candidate trajectory sampling strategies for different motion modes based on the global path and the two types of social conventions. Experimental results show that the proposed framework can be applied in large-scale, densely crowded, unstructured, and human-robot coexisting environments for socially-compliant navigation. However, the proposed navigation framework has limited improvement in navigation efficiency in a highly crowded environment due to the limited feasible space. Even pedestrians are difficult to walk in these highly crowded environments. In future work, we will study how to auto-adjust the weights to steer the robot to efficient navigation in highly crowded environments.

The Preisach model and the neural networks are two of the most popular strategies to model hysteresis. In this article, we first mathematically prove that the rate-independent Preisach model is actually a diagonal recurrent neural network (dRNN) with the binary step activation function. For the first time, the hysteresis nature and conditions of the classical dRNN with the tanh activation function are mathematically discovered and investigated, instead of using the common black-box approach and its variants. It is shown that the dRNN neuron is a versatile rate-dependent hysteresis system under specific conditions. The dRNN composed of those neurons can be used for modeling the rate-dependent hysteresis and it can approximate the Preisach model with arbitrary precision with specific parameters for rate-independent hysteresis modeling. Experiments show that the classical dRNN models both kinds of hysteresis more accurately and efficiently than the Preisach model.

A robot navigating in dense crowds should react to the motion of nearby pedestrians. However, it could lead to unsafe, inefficient, and illegible robot motions. This article presents an anticipative framework that predicts pedestrians intentions and their interactions in crowds, and the robot accordingly seeks an optimal trajectory based on the prediction. We propose: 1) a pedestrian motion model considering both pedestrian intention and interaction and 2) a multiobjective cost function considering real-time calculation, collision avoidance, quality of motion, and progress toward the goal along the trajectory. An interactive model predictive control framework is formulated to optimize the robot trajectory. The effectiveness of the proposed approach is evaluated in multiple simulation scenarios and a real experiment. It is demonstrated that the proposed approach generates safe, efficient, and legible robot behaviors in real time in dense crowds.

In this paper, a compliant five-bar leg mechanism is proposed, designed and manufactured for heavy-load legged robots, by using two magneto-rheological actuators (MRAs) that are capable of offering a maximal torque of 78Nm. To address the rate-dependent hysteresis of the MRA, a hybrid rate-dependent hysteresis model is derived based on the idea of mappings between different hysteresis loops. With integrating the classical Preisach model and the NARX neural network, the hybrid model is able to model hysteresis nonlinearity of the magneto-rheological clutch (MRC). It is then used to estimate and control the output torque of the MRA at the absent of external force/torque sensors. High fidelity force control and variable compliance of the leg mechanism are realized and validated in various experiments with using the MRAs.

The grasping control of an aerial manipulator in practical environments is challenging due to its complex kinematics/dynamics and motion constraints. This paper introduces a lightweight aerial manipulator, which is combined with an X8 coaxial octocopter and a 4-DoF manipulator. To address the grasping control problem, we develop an efficient scheme containing trajectory generation, visual trajectory tracking, and kinematic compensation. The NSGA-II method is utilized to implement the multiobjective optimization for trajectory planning. Motion constraints and collision avoidance are also considered in the optimization. A kinematic compensation-based visual trajectory tracking is introduced to address the coupled nature between manipulator and VAV body. No dynamic parameter calibration is needed. Finally, several experiments are performed to verify the stability and feasibility of the proposed approach.

In typical contour-following applications such as computer numerical control (CNC) machining, contouring error characterizes the surface quality of final workpieces. The traditional task coordinate frame (TCF)-based approach transforms the contouring control problem into a 3-D regulation problem along the axes of the local Frenet frame. The contouring error is then controlled indirectly by two decoupled regulation systems. In order to achieve a satisfactory contouring error performance, two sets of control parameters must be tuned for the two systems, respectively. In this paper, a novel TCF (nTCF) is proposed. Given the nearest position from the actual position to the desired (or approximated) contour, one axis is set along the line passing the actual position and the nearest position and another axis is along the advancing direction. The system dynamics in the world Cartesian coordinate frame is transformed into nTCF. The contouring control problem for 3-D contours can be reduced and it locally becomes a 2-D regulation problem, one in contouring direction and the other in advancing direction. By implementing the computed-torque control, two proportional-derivative controllers are integrated to regulate the advancing error and the contouring error, respectively. The contouring error is directly regulated by tuning a single set of control parameter, which becomes much simpler than that in the TCF-based approach, where two sets of control parameters are needed to tune. Experiments on an industrial three-axis glass engraving computer numerical control (CNC) machine show the validity of the proposed nTCF-based approach with two typical 3-D contours. Note to Practitioners-In machining applications, the contouring error is a crucial index with respect to the surface quality of machined parts. In this paper, a novel task coordinate frame (nTCF) is proposed to deal with the 3-D contouring control issue. By transforming the system dynamics from the world Cartesian coordinate frame to nTCF in real-time control, a 3-D contouring control problem is locally reduced and transformed into a 2-D error dynamics regulation problem. In the nTCF, the contouring performance and advancing performance can be decoupled and separately regulated by only two parameters in 3-D contouring control applications. The contouring error can be directly controlled. Moreover, existing contouring error calculation (estimation) techniques, such as linear approximation, circular approximation, or analytical methods, can be readily integrated into the nTCF-based approach. Both analysis and experiments show that the nTCF-based approach is effective and much simpler in parameter tuning for 3-D contouring control.

The contouring error and cross-coupled gains calculation have always been the critical issues in the application of cross-coupled control. Traditionally, the linear approximation and circular approximation are widely used to determine the contouring error and cross-coupled gains. However, for linear approximation and circular approximation, the contouring error and cross-coupled gains are calculated sophisticatedly, especially in three-dimensional applications. In this paper, a contouring error vector is established under task coordinate frame, then the contouring error and cross-coupled gains can be easily obtained based on the magnitude and orientation of the contouring error vector. The experimental results on a three-axis CNC machine indicate the proposed approach simplifies the calculation of contouring error and cross-coupled gains.

How to achieve the required contouring tracking accuracy especially during high-speed and large-curvature contouring tasks, has always been an important problem in manufacturing applications. In this paper, a contouring error estimation method based on natural local approximation is used, and then the position-loop cross-coupled controller is proposed to reduce the estimated contouring error. The effectiveness and superiority of the natural local approximation method using on the position-loop cross-coupled control scheme are demonstrated through experiments on a biaxial linear motor drive servo system.

According to the automobile industry's flexible manufacturing requirements, a novel flexible fixturing system for sheet metal assembly is proposed with parallel robots. A methodology of the structure synthesis is presented by taking account simultaneously several performance indices. Taking the 3UPU/UPS parallel robot in the system as an example, models of inverse kinematics, Jacobian matrix and design space are developed. Thus, the structure synthesis of the parallel robot is simplified to a two-dimensional problem. Once the performance indices (workspace, singularity and stiffness) are established, the corresponding indices atlases are expressed in the design space. The structure parameters of the parallel robot are obtained by analyzing these atlases. The prototype of the fixturing system is developed, and the relevant clamping and stiffness experiments are conducted. The experimental results generally agree well with the simulation results and satisfy the flexible fixturing systems' requirements.

Optimal design is an inevitable step for parallel manipulators. The formulated optimal design problems are generally constrained, nonlinear, multimodal, and even without closed-form analytical expressions. Numerical optimization algorithms are thus applied to solve the problems. However, the optimization algorithms are usually chosen ad arbitrium. This paper aims to provide a guideline to choose algorithms for optimal design problems. Typical algorithms, the sequential quadratic programming (SQP) with multiple initial points, the controlled random search (CRS), the genetic algorithm (GA), the differential evolution (DE), and the particle swarm optimization (PSO), are investigated in detail for their convergence performances by using two canonical design examples, the Delta robot and the Gough-Stewart platform. It is shown that SQP with multiple initial points can be efficient for simple design problems, while DE and PSO perform effectively and steadily for all design problems. CRS can be used to generate good initial points since it exhibits excellent convergence evolution in the starting period.

In this paper, a novel contouring control method based on natural local approximation of desired contour in task frame is proposed for multi-axis control systems. Based on local geometry properties, natural local approximation can achieve more accurate contouring error estimation compared with other local approximation methods for both planar and spatial contouring tasks. The contouring controller, integrated with a PD controller cooperated with the feedback linearization technique and a feedforward compensation, is designed to realize the decoupling control of estimated errors in the task frame. Contouring performance can then be improved directly by increasing corresponding controller parameters. Simulations of 3-axis system and experiments on biaxial XY-stage verified that our proposed method can reduce the contouring errors dramatically in high speed and large curvature cases compared with first-order based method.

A novel class of spatial four degree-of-freedom Schönflies-motion parallel manipulators with four identical subchains is presented. Their features are that each serial subchain undergoes the pure Schönflies motion without redundant joints. The parallel mechanisms possess the simplest topology and are suitable for pick-and-place operations. Kinematic analysis of the 4-PRPaR parallel manipulator, including its inverse and forward kinematics, singularity, and workspace, is discussed in detail. The analysis shows that the moving platform and the base must be in dissimilar dimension for good manipulability performance. The optimal design of the parallel manipulator is formulated as a multiobjective optimization problem. A novel performance index characterizing the approximation of the generated workspace to the prescribed regular workspace, the regular workspace share, is proposed to serve as one of the design objectives. The other objective is the global condition index, which measures the manipulability. The multiobjective optimization problem provides multiple optimal solutions for choice. Simulation verifies that the designed parallel manipulator can approximate the prescribed regular workspace with good condition index.

In this paper, we identify a class of structurally distinguished machines, called quotient kinematics machines (QKM). A QKM realizes a motion task, typically characterized by a subgroup G of rigid transformation group SE(3), through coordinated motion of two mechanisms called modules. One is referred to as a subgroup module H and the other a complementary or quotient module G/H of H in G. Since QKM can retain both large workspace/rotation range of SKMs and speed/accuracy of PKMs by appropriate choice of modules, it is often implemented in high end machine design for semiconductor die/wire-bonding and 5-axis machining, etc. To promote QKM technology beyond occasional studies and applications, we use differential geometric techniques to develop a rigorous and precise treatment of QKMs, including: (i) modeling and analysis of QKMs; (ii) classification and synthesis of QKMs; (iii) PKM realization of quotient modules.

Modern industries, e.g., semiconductor packaging, imposes increasing stringent requirement on equipment with very high acceleration and high precision. Traditionally, arm linkage and drive mechanism are first designed followed by control design. The integrated design method is proposed as a preferable technique of the traditional one. In this paper, a general framework of the integrated design method for a point-to-point control is presented. The dynamic model for a flexible planar two-link manipulator is derived by the finite element method. The PD control strategy is applied in the closed-loop system. The structural parameters and control parameters are optimized simultaneously by solving the integrated design problem. The differential evolution (DE) technique, a global optimization technique, is used to solve the optimal design problem. A simulation shows the integrated design method gives improved system performance.

The authors proposed in this paper an improved geometric formulation of POE (product of exponential) based kinematic calibration of serial robots. We use both joint offset-free formulation and adjoint transformation errors of joint screws, and apply it to the calibration of an elbow manipulator. Our formulation explains why the original POE calibration always fails with the existence of joint offset errors; the adjoint formulation of joint screw errors eliminates joint screw constraints that was imposed in the original iterated least square calibration algorithm. The second contribution of this paper is the proposal of a modified POE formulation which adopts point measurement data instead of frame measurement data of the end-effector, which can be more realistic and convenient for practical implementation. Simulation results show that the proposed method is plausible and effective. An experiment is under preparation to verify the effectiveness of the proposed calibration method on an elbow manipulator built by Googol Technology.

In mechanism and machine design, the notion of serial kinematics machine (SKM), parallel kinematics machine (PKM) and hybrid kinematics machine (HKM) is well understood. In this paper, we introduce a fourth type of kinematics machine, known as quotient kinematics machine(QKM). A QKM generating a subgroup motion G consists of two mechanisms (or motion modules) acting in unison, one synthesizing a subgroup H of G, and another that of a complement of G/H. Apparently, the two motion modules of a QKM have simpler kinematic structures than that of a SKM, PKM or HKM with the same motion type G, and thus is expected to have performance advantages in terms of stiffness (speed and accuracy), modularity and etc, over its SKM/PKM/HKM counterparts. The formulation of the QKM concept and its analysis and synthesis are considered in this paper.

This work intends to deal with the optimal kinematic synthesis problem of parallel manipulators under a unified framework. Observing that regular (e.g., hyper-rectangular) workspaces are desirable for most machines, we propose the concept of effective regular workspace, which reflects simultaneously requirements on the workspace shape and quality. The effectiveness of a workspace is characterized by the dexterity of the mechanism over every point in the workspace. Other performance indices, such as manipulability and stiffness, provide alternatives of dexterity characterization of workspace effectiveness. An optimal design problem, including constraints on actuated/passive joint limits and link interference, is then formulated to find the manipulator geometry that maximizes the effective regular workspace. This problem is a constrained nonlinear optimization problem without explicitly analytical expression. Traditional gradient based approaches may have difficulty in searching the global optimum. The controlled random search technique, as reported robust and reliable, is used to obtain an numerical solution. The design procedure is demonstrated through examples of a Delta robot and a Gough-Stewart platform.

In view of the successful application of planar parallelogram in the Delta robot and its variants, we are interested to investigate mechanisms consisting of spatial parallelograms. The spatial parallelogram, denoted by Pscra*, is a 2-SS (S stands for a spherical joint) parallel mechanism having identical length for opposite links. We show that a 3-PPscra* mechanism is generically undergoes 3-dimensional purely translational motion. Based on the 3-PPscra* topology, an integrated optimal design on both architecture and geometry design is carried out. Using the formulation of maximizing effective cubic workspace, the Orthopod, which has three orthogonally arranged linear joint axes, is found to be the best in our settings. A prototype machine of the Orthopod is thus designed and manufactured.

Since the tracking error does not truly reflect product quality, the contouring error is introduced in the dynamic control of parallel machining systems. For real-time computation reason, the contouring error is approximated by the distance from the actual position to the tangent plane of the desired contour at the corresponding desired position, i.e., the error in normal direction. By attaching a moving task frame to each point on a desired trajectory, the tracking error is decomposed into tangential error and normal error. By the transformation introduced by the task frame, we obtain error dynamics in the task frame. The error dynamics is decoupled into error dynamics in tangential and normal directions by applying the computed torque control and choosing appropriate system matrices. Simulation shows that a larger bandwidth of the normal dynamics leads to smaller contouring error given fixed natural frequency for the tangential dynamics. By a comparison with the PD control in the world frame, the task space based contouring control exhibits much better performance in contouring accuracy

A novel 3-DoF purely translational parallel mechanism, the Orthotripod, is proposed. It is a variant of the tripod based parallel machine and has a similar architecture to the Orthoglide. In order to reduce the number of passive joints and remove the effect of ease of abrasion of revolute joints, spherical joints are applied in the parallelogram. A mathematic mobility analysis shows the mechanism is indeed 3-DoF purely translational. We optimally design the Orthotripod and the tripod based parallel machine by maximizing the well-conditioned workspace. The optimized Orthotripod possesses a nearly ball-shaped workspace and has much better kinematic performance than the optimized tripod based parallel mechanism. The proposed mechanism is adaptable for machine tool applications

In tasks of grasping and manipulation, the hand sometimes uses not only fingertips but also fingers' inner links and the palm to achieve more robust grasp. This kind of grasp is called whole hand grasp, or power grasp. One property of whole hand grasp is that the hand may not be able to generate grasping forces in any directions, so previous fingertip grasping analysis is no longer suitable for whole hand grasp. In this paper, concepts of active force and passive force are introduced. With these concepts, the contact force space is decomposed into four orthogonal subspaces. Considering the roles of both active force and passive force, a new cost index is proposed for the whole hand grasping force optimization, which is then reformulated into a convex optimization problem involving LMIs. Finally, numerical example and simulation results verify the validity and performance of our formulation of the problem with that new proposed cost index

Large-scale motion of the grasped object is one of the tasks, which is involved in practical dextrous manipulation of multifingered robotic hand. When the large-scale motion can not be accomplished only by rolling and sliding of the finger, finger gaiting, or regrasping, is used. In this paper, two primitives of finger gaits are introduced. Based on the characteristic of finger gaits, we model finger gaits as a hybrid automaton. Finally, we do simulations on a three fingered hand to verify the validity of our model

In this paper we concentrate on contouring control for surface machining. The object of the motion control system is tracking the spatial curve lying on the surface. Observing that the contour error can be approximated by the tracking error (projected to the normal subspace of the surface), we propose a new design procedure based on the geometrical properties of the curves and surfaces. Essentially the controllers look ahead using the information provided by the curvature of the curves and surfaces. The simulation results show the efficiency of the design method

In this paper, the desired performance of the mechanical system is specified in terms of contouring error instead of traditional method which specifies a task as a desired timed trajectory tracking problem. By defining the task frame, a simplified contouring error model is obtained through projecting tracking error to this new frame. Then a novel adaptive contouring controller is developed directly in the task frame to handle bounded external disturbances and system model uncertainties while maintaining superior contouring tracking performance. The algorithm effectively exploit the the structure of manipulator dynamics to reduce the computation complexity. Experimental results on an AC motor driven X-Y table demonstrate the merit of significant improvement of the proposed controller for increasing contouring accuracy compared with other conventional control algorithms.

This paper proposes to optimally design a parallel machine based on multiple criteria. Many criteria, workspace, condition number, accuracy, stiffness, maximum velocity, and maximum force, are considered. The optimal design problem is proposed as to find a set of design parameters such that (a) the Cartesian workspace generated by the resulting manipulator contains a prescribed workspace; (b) the resulting manipulator possesses a good condition number at each points in the prescribed workspace; (c) the resulting manipulator possesses good performance on accuracy, stiffness, velocity/force transmission factor. By some manipulations, the requirements on the latter four criteria are reduced to constraints on singular values of the kinematic Jacobian. A trade-off must be made since there're opposite requirements among those four criteria. The singular values of kinematic Jacobian are limited in a given interval to guarantee good properties. All the requirement are finally reduced to polynomial inequalities with respect to design parameters. The optimal design problem is transformed into a Max-Det optimization problem that can be ef ficiently solved. The Orthoglide is used as an example to demonstrate the procedure.

Kinematic design of parallel manipulators is addressed in this paper. By observation that regular (e.g., hyper-rectangular) workspaces are desirable for most machines, we propose the concept of effective regular workspace, which reflects both requirements on the workspace shape and quality. Dexterity index is utilized to characterize the effectiveness of the workspace. The optimal design problem is then formulated to find a manipulator geometry that maximizes the effective regular workspace. Since the optimal design problem is a constrained nonlinear optimization problem without explicit analytical expressions, the controlled random search (CRS) technique, which was reported robust and reliable, is applied to numerically solve the problem. The commonly-used Stewart-Gough platform is employed as an example to demonstrate the design procedure.

This paper deals with the problem of optimal geometry design of parallel manipulators. In order to reduce the main drawbacks of parallel manipulators, relatively small workspace and more singularities, two requirements, workspace and condition number, are considered. The design problem is thus formulated to find a parallel mechanism such that its Cartesian workspace contains a prescribed workspaces with good condition numbers in it. By observing that those requirements can be locally cast into Linear Matrix Inequalities (LMIs), we formulate the design problem locally as a convex optimization problem subject to LMIs with a max-det function as its objective function. Hence, at each node of discretized space of design parameters, there is an LMI-based convex optimization problem. A two-level algorithm can be applied to solve for a set of optimal design parameters: (1) Discretize the space of design parameters into a set of discrete nodes; (2) At each node the Newton algorithm is applied to solve the max-det optimization problem. By comparing all the locally optimal costs, we can obtain a corresponding set of globally optimal design parameters correspondingly. Simulation results verify the effectiveness of the proposed approach.

This paper deals with the problem of optimal design of parallel manipulators which are singularityless, of high stiffness and manipulability and the most economic. By observing that those requirements can be cast into Linear Matrix Inequalities (LMIs), we formulate the design problem as a convex optimization problem subject to LMIs with either a linear function or a max-det function as its objective function. The variables x associated with LMIs are nonlinear functions of some key kinematic parameters /spl alpha/. If the dimension of x, t, is equal to the number of kinematic parameters, l/sub 0/, a two-level algorithm can be applied to solve for a set of optimal kinematic parameters: (1) Applying the interior point algorithm for solving of x; (2) Applying Newton method to a set of nonlinear algebraic equations for solving of /spl alpha/. If the dimension of x is greater than the number of kinematic parameters (i.e., x are not linearly independent), we consider the constrained semi-definite programming problems and the constrained max-det problems by taking account of an additional set of nonlinear constraints. We propose a simplified constrained gradient algorithm for solving of x in such cases, /spl alpha/ derives from x using Newton method. Simulation results verify the effectiveness of the proposed algorithms.

This paper deals with the problem of optimal design of parallel manipulators which have no singularity, have high stiffness and manipulability and are the most economic. By observing that those requirements can be cast into linear matrix inequalities (LMIs), we formulate the design problem as a convex optimization problem subject to LMIs with either a linear function or a max-det function as its objective function. The variables x associated with LMIs are nonlinear functions of some key kinematic parameters /spl alpha/. If the dimension of x is equal to the number of independent kinematic parameters, a two-level algorithm can be applied to solve for a set of optimal kinematic parameters: (1) applying the interior-point algorithm for solving of x; (2) applying the Newton method to a set of nonlinear algebraic equations for solving of /spl alpha/. If the dimension of x is greater than the number of independent kinematic parameters (i.e., x are not linearly independent), we consider the constrained semi-definite programming problems and the constrained max-det problems by taking account of an additional set of nonlinear constraints. We propose a simplified constrained gradient algorithm for solving of x in such cases, /spl alpha/ derives from x using Newton method. Simulation results verify the effectiveness of the proposed algorithms.

A parallel manipulator is naturally associated with a set of constraint functions defined by its closure constraints. The differential forms arising from these constraint functions completely characterize the geometric properties of the manipulator. In this paper, using the language of differential forms, we provide a thorough geometric study on the various types of singularities of a parallel manipulator, their relations with the kinematic parameters and the configuration spaces of the manipulator, and the role redundant actuation plays in reshaping the singularities and improving the performance of the manipulator. First, we analyze configuration space singularities by constructing a Morse function on some appropriately defined spaces. By varying key parameters of the manipulator, we obtain homotopic classes of the configuration spaces. This allows us to gain insight on configuration space singularities and understand how to choose design parameters for the manipulator. Second, we define parametrization singularities which include actuator and end-effector singularities (or other equivalent definitions) as their special cases. This definition naturally contains the closure constraints in addition to the coordinates of the actuators and the end-effector and can be used to search a complete set of actuator or end-effector singularities including some singularities that may be missed by the usual kinematics methods. We give an intrinsic classification of parametrization singularities and define their topological orders. While a nondegenerate singularity poses no problems in general, a degenerate singularity can sometimes be a source of danger and should be avoided if possible.