Kai Qin, Liangxin Liu, Yu Liang, Longzheng Wang, Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Houde Liu, Daiting Shi. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Visual SLAM algorithms have been enhanced through the exploration of Gaussian Splatting representations, particularly in generating high-fidelity dense maps. While existing methods perform reliably in static environments, they often encounter camera tracking drift and fuzzy mapping when dealing with the disturbances caused by moving objects. This paper presents DyPho-SLAM, a real-time, resource-efficient visual SLAM system designed to address the challenges of localization and photorealistic mapping in environments with dynamic objects. Specifically, the proposed system integrates prior image information to generate refined masks, effectively minimizing noise from mask misjudgment. Additionally, to enhance constraints for optimization after removing dynamic obstacles, we devise adaptive feature extraction strategies significantly improving the system’s resilience. Experiments conducted on publicly dynamic RGB-D datasets demonstrate that the proposed system achieves state-of-the-art performance in camera pose estimation and dense map reconstruction, while operating in real-time in dynamic scenes.

Fast and efficient collision detection is essential for motion generation in robotics. In this paper, we propose an efficient collision detection framework based on the Signed Distance Field (SDF) of robots, seamlessly integrated with a self-collision detection module. Firstly, we decompose the robot's SDF using forward kinematics and leverage multiple extremely lightweight networks in parallel to efficiently approximate the SDF. Moreover, we introduce support vector machines to integrate the self-collision detection module into the framework, which we refer to as the SDF-SC framework. Using statistical features, our approach unifies the representation of collision distance for both SDF and self-collision detection. During this process, we maintain and utilize the differentiable properties of the framework to optimize collision-free robot trajectories. Finally, we develop a reactive motion controller based on our framework, enabling real-time avoidance of multiple dynamic obstacles. While maintaining high accuracy, our framework achieves inference speeds up to five times faster than previous methods. Experimental results on the Franka robotic arm demonstrate the effectiveness of our approach. Project page: https://sites.google.com/view/icra2025-sdfsc.

Safety-critical traffic scenarios are of great practical relevance to evaluating the robustness of autonomous driving (AD) systems. Given that these long-tail events are extremely rare in real-world traffic data, there is a growing body of work dedicated to the automatic traffic scenario generation. However, nearly all existing algorithms for generating safety-critical scenarios rely on snippets of previously recorded traffic events, transforming normal traffic flow into accident-prone situations directly. In other words, safety-critical traffic scenario generation is hindsight and not applicable to newly encountered and open-ended traffic events. In this paper, we propose the Deep Motion Factorization (DeepMF) framework, which extends static safety-critical driving scenario generation to closed-loop and interactive adversarial traffic simulation. DeepMF casts safety-critical traffic simulation as a Bayesian factorization that includes the assignment of hazardous traffic participants, the motion prediction of selected opponents, the reaction estimation of autonomous vehicle (AV) and the probability estimation of the accident occur. All the aforementioned terms are calculated using decoupled deep neural networks, with inputs limited to the current observation and historical states. Consequently, DeepMF can effectively and efficiently simulate safety-critical traffic scenarios at any triggered time and for any duration by maximizing the compounded posterior probability of traffic risk. Extensive experiments demonstrate that DeepMF excels in terms of risk management, flexibility, and diversity, showcasing outstanding performance in simulating a wide range of realistic, high-risk traffic scenarios.

Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework that combines optimization and learning to achieve compliant catching on mobile manipulators (CCMM). First, we propose a high-level capture planner for mobile manipulators (MM) that calculates the optimal capture point and joint configuration. Next, the pre-catching (PRC) planner ensures the robot reaches the target joint configuration as quickly as possible. To learn compliant catching strategies, we propose a network that leverages the strengths of LSTM for capturing temporal dependencies and positional encoding for spatial context (P-LSTM). This network is designed to effectively learn compliant strategies from human demonstrations. Following this, the post-catching (POC) planner tracks the compliant sequence output by the P-LSTM while avoiding potential collisions due to structural differences between humans and robots. We validate the CCMM framework through both simulated and real-world ball-catching scenarios, achieving a success rate of 98.70% in simulation, 92.59% in real-world tests, and a 28.7% reduction in impact torques. The open source code will be released for the reference of the community1.

For individuals with limited mobility who are bedridden for extended periods, providing comfortable assisted feeding services is one of the most significant actions to enhance their quality of life. Despite the development of various feeding assistive robots, there remain limitations in terms of interaction convenience and safety, which restrict the overall feeding experience for users. To address these challenges, this study first establishes a feeding assistive robot system that integrates multimodal interaction methods. Furthermore, we propose an interactive feeding method that combines both safety and comfort. This method utilizes visual recognition to detect the user’s active meal intent, food selection preferences, and chewing status. Additionally, based on a Large Language Model (LLM), a monitoring thread is designed to conduct voice interactions regarding the user’s ambiguous intentions, temporary changes in intent, emergency situations, and risky behaviors throughout the feeding process. Comprehensive experimental results demonstrate that the proposed multimodal interaction method, which aligns with the natural eating patterns, incorporates both language and visual interactions, the two most convenient forms for users. It also matches force sensing and pose control techniques during the feeding stages, thereby enhancing the flexibility and safety of the assisted feeding system.

Recent advances in robotic manipulation leverage foundation models pre-trained on internet-scale data, where keypoint-based representations have shown promising results in spatial reasoning. However, existing approaches primarily focus on zero-shot generalization or human-collected demonstrations, with limited exploration of large-scale robotic datasets. In this work, we propose Keypoint-Aware Retrieval Augmented Generation (KARAG), a simple yet novel framework that synergistically integrates visual-language models (VLMs) with robotic datasets through retrieval-augmented generation (RAG). Our framework bridges the retrieval and generation phases via in-context learning with keypoint-aware constraints, enabling simultaneous utilization of internet-scale knowledge and structured robotic datasets. Extensive experiments in both simulated and real-world environments demonstrate that KARAG significantly enhances the stability and accuracy of VLM-generated outputs without requiring human demonstrations or additional training, achieving 10%–20% success rate improvements in real-world scenarios and 12%–46% improvements in simulation over the baseline. Furthermore, we present an algorithm for converting large robotic datasets into Keyframe-Keypoint-Trajectory representations to facilitate retrieval. Our dataset and implementation are publicly available at https://github.com/RobertAckleyLin/KARAG/.

Locomotion in robots remains an unsolved challenge, particularly for those with complex structures and dynamic environments. Consequently, the control systems for such robots must place greater emphasis on risk mitigation and safety considerations to ensure reliable and stable operation. Existing studies have explicitly incorporated risk factors into policy training, but lacked the ability to adaptively adjust the risk sensitivity for hazardous environments. This deficiency impacts the agent’s exploration during training and thus fails to select the optimal action. We innovatively introduce Adaptive Risk-aware Control (ARC) policies based on Distributional Reinforcement Learning (Dist.RL), a novel framework that dynamically adjusts risk sensitivity levels in response to changing environmental conditions. Our approach uniquely integrates two key components: (1) the Inter Quartile Range (IQR) for quantifying intrinsic environmental uncertainty, and (2) Random Network Distillation (RND) for evaluating parameter uncertainty. This dual-mechanism architecture represents a significant advancement in risk assessment methodologies. Simulations conducted on a variety of robots have demonstrated that our method achieves significantly more robust performance compared to other approaches. Furthermore, sim2real validation on a humanoid robot confirms the practical viability of our approach.

The number of motors directly influences the dexterity, size, and cost of a robotic hand. In this paper, we present MuxHand, a robotic hand that utilizes a time-division multiplexing motor (TDMM) mechanism. This system enables independent control of 9 cables with just 4 motors, significantly reducing both cost and size while maintaining high dexterity. To enhance stability and smoothness during grasping and manipulation tasks, we integrate magnetic joints into the three 3D-printed fingers. These joints provide impact resistance, resetting capabilities. The three fingers together have a total of 30 degrees of freedom (DOF), 18 of which are passive DOF, allowing the hand to conform closely to the surface of an object during grasping. We conduct a series of experiments to assess the performance parameters of MuxHand, including its grasping and manipulation capabilities. The results show that the TDMM mechanism precisely controls each cable connected to the finger joints, enabling robust grasping and dexterous manipulation. Furthermore, compared to the traditional approach of assigning a motor to each active DOF, the cost is reduced by 42.06%. The maximum load of a single finger reaches 7.0 kg, the maximum load at the finger joint root is 12.0 kg, the maximum driving force at the joint root is 5.0 kg, and the maximum fingertip force is 10.0 N.

We present KD-RIEKF, a novel state estimation framework that incorporates kinodynamic constraints into the Right-Invariant Extended Kalman Filter (RIEKF). Our framework integrates generalized momentum-based contact estimation, centroidal dynamics, and a noise-adaptive module, improving state estimation accuracy by probabilistically adjusting propagation noise to account for contact uncertainty and sensor noise. A key innovation is the expansion of the ground reaction force (GRF) into a state variable. By using GRF-based acceleration as a measurement, our method significantly reduces estimation errors in position, velocity, and orientation. The integration of contact-force-driven adaptive noise effectively boosts the stability of estimation, especially when the system is undergoing turning, acceleration, or deceleration processes. We validated our algorithm in simulation on highly uneven terrain, showing significant enhancements in z-axis position estimation compared to RIEKF. Further experiments on the Unitree Go2 robot across different speeds demonstrated that even in high-speed scenarios over 200 meters, our method reduced position estimation relative error (RE) by 47% and orientation estimation by 42%, confirming its robustness and accuracy under dynamic locomotion.

We present SafeSim, a comprehensive benchmarking platform and a unified end-to-end framework for safety-critical driving scenario simulation and curriculum-based adversarial training. SafeSim integrates 8 classic adversarial environment generation algorithms, enabling the generation at any trigger moment and for any duration based on various naturalistic traffic datasets. During the automatic vehicle (AV) training process, SafeSim dynamically adjusts the risk-level of scenarios based on the current capability of AV, progressively enhancing its ability to handle accident-prone situations. The platform features rich interfaces and extensibility, providing a complete workflow and tool-chain for risk scenario generation, risk assessment, AV training, and algorithm evaluation. Additionally, SafeSim includes multiple baseline results, serving as a standardized benchmark for future research.

It is difficult to guarantee an empty living environment to prevent unexpected contact between the object being manipulated by the robot and unplanned obstacles. In this paper, we propose a planar compliant contact control method for planar manipulation to cope with unexpected contact. We first use sheet gel as a multi-dimensional passive elastic element and combine it with a two-finger gripper to design a multi-dimensional elastic gripper. Subsequently, we explore the lumped parameter model for the force-displacement relationship of gel deformation and combine the model with the high impedance motion of robots to design an elastic interaction controller. The controller not only actively adjusts the deformation of the gel to provide the desired contact force and torque depending on contact, but also performs avoidance by following the surface of obstacles. Finally, we design and deploy several planar compliant contact experiments to validate the proposed method and demonstrate the unexpected contact response in human-robot co-packing. The results show that our method enables the robot to remain compliant in the face of unexpected contact caused by unplanned obstacles, which provides a guarantee for safe manipulation. Physics experiments can be viewed in the attached video.

Simultaneous localization and mapping (SLAM) systems frequently employ LiDAR and cameras as essential sensing components. However, these sensors are proved to be unreliable in environments with poor visibility or reflective surfaces. And UWB (Ultra Wide Band) sensor with a longer wavelength shows better potential to achieve perception tasks. However, since UWB sensors can only obtain distance information from the anchors, it is difficult to densely construct the geometric structure of the environment. In this paper, We propose Range-SLAM, a method based on received signal strength indicator (RSSI) recognition and binary filtering to complete the mapping task and enhance positioning based on the map, and only require UWB as external perception sensor. Real-world experiments are conducted and prove the effectiveness, real-time performance and robustness of the Range-SLAM algorithm.

Designing a bipedal robot is a complex and challenging task, especially when dealing with a multitude of structural parameters. Traditional design methods often rely on human intuition and experience. However, such approaches are time-consuming, labor-intensive, lack theoretical guidance and hard to obtain optimal design results within vast design spaces, thus failing to full exploit the inherent performance potential of robots. In this context, this paper introduces the SERL (Structure Evolution Reinforcement Learning) algorithm, which combines reinforcement learning for locomotion tasks with evolution algorithms. The aim is to identify the optimal parameter combinations within a given multidimensional design space. Through the SERL algorithm, we successfully designed a bipedal robot named Wow Orin, where the optimal leg length are obtained through optimization based on body structure and motor torque. We have experimentally validated the effectiveness of the SERL algorithm, which is capable of optimizing the best structure within specified design space and task conditions. Additionally, to assess the performance gap between our designed robot and the current state-of-the-art robots, we compared Wow Orin with mainstream bipedal robots Cassie and Unitree H1. A series of experimental results demonstrate the Outstanding energy efficiency and performance of Wow Orin, further validating the feasibility of applying the SERL algorithm to practical design.

Traversing 3-D complex environments has always been a significant challenge for legged locomotion. Existing methods typically rely on external sensors such as vision and lidar to preemptively react to obstacles by acquiring environmental information. However, in scenarios like nighttime or dense forests, external sensors often fail to function properly, necessitating robots to rely on proprioceptive sensors to perceive diverse obstacles in the environment and respond promptly. This task is undeniably challenging. Our research finds that methods based on collision detection can enhance a robot’s perception of environmental obstacles. In this work, we propose an end-to-end learning-based quadruped robot motion controller that relies solely on proprioceptive sensing. This controller can accurately detect, localize, and agilely respond to collisions in unknown and complex 3D environments, thereby improving the robot’s traversability in complex environments. We demonstrate in both simulation and real-world experiments that our method enables quadruped robots to successfully traverse challenging obstacles in various complex environments. The videos and appendix can be found at Quad-Traverse-Go2.github.io

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

The grasping of transparent objects is challenging but of significance to robots. In this article, a visual–tactile fusion framework for transparent object grasping in complex backgrounds is proposed, which synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. First, we propose a multiscene synthetic grasping dataset named SimTrans12 K together with a Gaussian-mask annotation method. Next, based on the TaTa gripper, we propose a grasping network named transparent object-grasping convolutional neural network for grasping position detection, which shows good performance in both synthetic and real scenes. Inspired by human grasping, a tactile calibration method and a visual–tactile fusion classification method are designed, which improve the grasping success rate by 36.7% compared with direct grasping and the classification accuracy by 39.1%. Furthermore, the tactile height sensing module and the tactile position exploration module are added to solve the problem of grasping transparent objects in irregular and visually undetectable scenes. The experimental results demonstrate the validity of the framework.

Continuum robots are typically slender and flexible with infinite freedoms in theory, which poses a challenge for their control and application. The shape reconstruction of continuum robots is vital to realize closed-loop control. This paper proposes a novel general real-time shape reconstruction framework of continuum robots based on the piecewise polynomial curvature (PPC) kinematics model. We illustrate the coupling between orientation and position at any given location of the continuum robots. Further, the coupling relation could be bridged by the PPC kinematics. Therefore, we propose to estimate the shape through multi-attitude solving, using the off-the-shelf orientation sensors, e.g., IMUs, mounted on certain locations. The approach gives a valuable framework to real-time shape reconstruction of continuum robots, which is general, accurate and convenient. The accuracy of our approach is verified in the experiments of distinct physical prototypes.

In this paper, we propose a novel real-time 6D object pose estimation framework based on Point-wise Attention Keypoints Voting Network (PAV-Net). Compared with previous methods that use all features indiscriminately, we evaluate and integrate the visible points features before estimation to deal with the unstructured and uneven properties of point-wise features. Specifically, we first locate the object roughly by object detection and transfer the captured point cloud coordinates to the local center. Then we extract point-wise features from RGB images and point clouds respectively and perform semantic segmentation. Finally, the point-wise features are screened and integrated with the help of the attention keypoints voting to predict the accurate keypoint coordinates, and the 6D object pose can be obtained within keypoints fitting. The proposed method can effectively avoid external interference and improve the efficiency of influential point features utilization by point-wise attention voting so that the framework only needs a simple feature extraction network support to have better real-time performance. Extensive experiments confirm this conclusion and show that the performance of proposed framework on LineMOD and YCB-Video datasets is superior to other real-time pose estimation methods at the same speed.

Finger dexterity and tactile perception are key capabilities for humans to manipulate objects within hand, as well as robots. Inspired by the thumb-forefinger dexterous manipulative movement, we devised a novel robotic finger with an active rotational tactile sensor (i.e. TacRot), and mounted the finger on a parallel-jaw gripper. By processing the high-resolution images of the vision-based tactile sensor, we achieved depth reconstruction of the surface and localization of the contact area. To improve gripping flexibility and stability, we applied a self-adaptive grasping strategy with real-time contact detection feedback, which performed 94% success rate in experiment. Based on the rotational actuator at the fingertip, we proposed two in-hand manipulation primitives: (1) pivot: fingertips co-rotating for object reorientation; (2) twist: fingertips contra-rotating for object spin. The primitives are theoretically analyzed and experimentally verified in two practical tasks: pivoting a paper cup under vertical constraints and twisting a screw with spin angle estimation. Our design and experiments demonstrate a feasible way to enhance the active tactile manipulation ability for common parallel-jaw grippers.

The attitude control of an underactuated reusable launch vehicle (RLV) in the reentry phase involving nonminimum phase problem and control input constraints is investigated in this article. To address the nonminimum phase problem, an approach combining output redefinition and robust backstepping is proposed, where a synthetic output is constructed using the combination of the original output and the internal states to obtain stable zero dynamics, and then robust backstepping is performed on the new output. Besides, the ideal internal dynamics are obtained by using optimal bounded inversion, which are incorporated into the controller as the reference trajectories for the internal states to improve the output tracking accuracy. To cope with the control input constraints, a simple and useful anti-windup strategy is proposed by using feedback error clipping, which is shown to be very effective in mitigating control input saturation. Numerical simulations are given to validate the effectiveness of the proposed method.

Symmetry in running was observed by Marc Raibert and was applied to simplify the control of dynamic legged systems. In this paper, we show that symmetry also exists in biped walking and investigate it using two simplified 2D models, that are, the inverted pendulum (IP) model and the linear inverted pendulum (LIP) model, both leading to similar conclusions. To characterize the symmetry in biped walking, the concept of acceleration factor is proposed. Symmetry occurs when the acceleration factor is zero, which results in an unchanged mid-stance velocity. And an important property of symmetry is that the n-step reachable region and the n-step controllable region are exactly the same. This means that if we can achieve speed B from A in n steps, then we can also achieve speed A from B in n steps. Symmetry in walking helps us to better understand human walking and also provides an intuitive way to control robotic walking. As an example, we propose a feedforward controller and a feedback controller, respectively, which can regulate the walking speed very effectively. This work provides us some new insights to view biped walking.

This paper proposes a multi-task control strategy for a quadruped robot named THU-QUAD II. The mechanical design of the robot ensures a wide range of motion for all joints, which allows it to stand and walk like a mammal as well as sprawl to the ground and crawl like a reptile. Five basic leg configurations are defined for the robot, including four mammal-type configurations with bidirectional knees and one sprawling-type configuration. A multi-task control framework is developed by combining configuration selection and gait planning. According to the locomotion environments, the robot can nimbly switch between different configurations, which gives it more flexibility when facing different tasks. For the mammal-type configuration, a parametric climbing gait is designed to traverse structural terrain. For the sprawling-type configuration, a crawling gait is designed to achieve robust locomotion on uneven terrain. Simulations and experiments show that the robot is capable to move on multiple challenging terrains, including doorsills, stairs, slopes, sand and stones. This paper demonstrates that even some challenging locomotion tasks can be achieved in a rather simple way without using complicated control algorithms, which suggests us to rethink about the leg configurations in designing quadruped robots.

The continuum robot has attracted more attention for its flexibility. Continuum robot kinematics models are the basis for further perception, planning, and control. The design and research of continuum robots are usually based on the assumption of piecewise constant curvature (PCC). However, due to the influence of friction, etc., the actual motion of the continuum robot is approximate piecewise constant curvature (APCC). To address this, we present a kinematic equivalent model for continuum robots, i.e. APCC 2L-5R. Using classical rigid linkages to replace the original model in kinematic, the APCC 2L-5R model effectively reduces complexity and improves numerical stability. Furthermore, based on the model, the configuration self-estimation of the continuum robot is realized by monocular cameras installed at the end of each approximate constant curvature segment. The potential of APCC 2L-5R in perception, planning, and control of continuum robots remains to be explored.

Traversing at a high speed while maintaining stability is important for the application of quadruped robots. Prior works mainly concentrated on optimizing the stability margin of quadruped robots when walking through a variety of terrains. However, the problem of improving quadruped robots' walking velocity with static gait is less concerned in their works. In this paper, the static gait planning problem is considered under the assumption that a set of irregular footholds on the rough terrain is given, and two approaches are proposed to improve the walking speed. The first one is a distance optimization algorithm, which can minimize the moving distance of the center of gravity (COG) in the stance phases based on the stability and the kinematic constraint. The other is a velocity optimization algorithm, which enables the body and the feet to move at the highest velocity with the joint angular velocity limit. The joint application of these two optimization algorithms significantly improves the walking speed of the quadruped robot. Simulation results in V-REP are presented to demonstrate the effectiveness of the proposed approaches in improving the walking speed. Compared with the traditional gait planning techniques, one that moves the robot with the optimal stability margin, and the other that moves the robot without optimizing the velocity, our algorithms increase the average walking velocity by 81.6% and 32.8%, respectively.

Accurate object 6D pose estimation is a core task for robot bin-picking applications, especially when objects are randomly stacked with heavy occlusion. To address this problem, this paper proposes a simple but novel Point-wise Pose Regression Network (PPR-Net). For each point in the point cloud, the network regresses a 6D pose of the object instance that the point belongs to. We argue that the regressed poses of points from the same object instance should be located closely in pose space. Thus, these points can be clustered into different instances and their corresponding objects' 6D poses can be estimated simultaneously. In our experiments, PPR-Net outperforms the state-of-the-art approach by 15% - 41% in average precision when evaluated on the benchmark Siléane dataset. In addition, it also works well in real world robot bin-picking tasks.

In a multiarm space robotic system, one or more manipulators can be used to stabilize the base through counteracting the disturbance caused by other manipulators performing on-orbital tasks. However, singularities are inevitably present in the traditional methods based on differential kinematics solutions. In this paper, we propose a singularity-free trajectory planning method to simultaneously keep the attitude and centroid position of the base stabilized in inertial space; the balance arms are also designed. First, we derive the coupling motion equations of a free-floating multiarm space robotic system. Then, the singularity problems are theoretically analyzed, and the theoretical basis for singularity-free trajectory planning is established. Second, we decompose the six degrees of freedom pose (attitude and position) stabilization problem into two 3DOF subproblems related to attitude and position balancing. We then design two robotic arms: 1) a position balance arm and 2) an attitude balance arm, to maintain the base centroid position and attitude, respectively. Third, we plan the coordinated trajectories of the two balance arms according to holonomic and nonholonomic constraints. As long as the desired motion is not beyond its balance ability, the reasonable joint variables can always be determined without encountering a singularity problem. Finally, the proposed methods are verified using simulations of typical on-orbital missions, including joint trajectory tracking and target capturing.

Robotic systems are expected to play an increasingly important role in future space activities with the development of space technology. The robotic on-orbital service, whose key is the capturing technology, becomes research hot in recent years. This paper focuses on the guidance of a robot manipulator to capture a spinning satellite with unknown dynamics parameters. In capturing a spinning satellite, a reference trajectory for control of the manipulator is generated with time delay due to the processing time of the target motion estimator and the manipulator controller. Consequently, the control system shows a poor performance and the end-effector sometimes fails to capture the target satellite. To solve this problem, the motion characteristics and motion prediction of the spinning satellite is analyzed Firstly, and using Unscented Kaiman Filter (UKF) to predict its movement. Then, a method of autonomous path planning of a free-floating space robot for target capturing is proposed, which is based on motion prediction and speed compensation. Finally, a ground experiment system is set up based on the concept of dynamic emulation and kinematic equivalence. With the experiment system, the autonomous target capturing experiments are conducted. The experiment results validate the proposed algorithm.

Autonomous target capturing is the key for space robot to perform on-orbital servicing tasks. To meet the requirement of complex and long-term task, large flexible appendages, such as solar paddles and antenna reflectors are usually mounted on the base of a space robot. Due to the structure vibration, it is very challenging to capture a free-floating target satellite. In this paper, we derived the kinematics equations and proposed the autonomous target capturing method for free-floating flexible-base space robots. The kinematics equation established the mapping from the base velocities, joint rates and elastic motion to the end-effector velocities. Based on this equation, we designed resolved motion rate control with vibration compensation for the space manipulator. Another contribution of this paper is that we modeled the dynamic coupling between the rigid movement of the end-effector and the flexible vibration of the solar paddles. Based on this model, we analyzed the coupling effect which was very important for the design of the manipulator and determining the trajectory planning and control strategy. At last, a simulation system was created and simulation studies of the proposed methods were carried out. The simulation results verify the proposed methods.

Robotic systems are expected to play an increasingly important role in future space activities with the development of space technology. One broad area of application is in the servicing, construction, and maintenance of satellites and large space structures in orbit. Fine manipulation technology is very important for space robot to perform there tasks, since it must ensure safe and reliable interaction with objects or environment. In order to assure the task is accomplished successfully, ground experimentations are required for verifying key planning and control algorithms before the space robot is launched. In this paper, based on the concept of a hybrid approach combining the mathematical model with the physical model, a ground experiment system is set up, which is composed of two industrial robots, global and hand-eye visual equipments, six-axis force/momentum sensors, guide rail and four computers. Many control approaches of fine manipulation, such as compliance control, impedance control, hybrid force/position control, intelligent control, and so on, can be verified using this system. As an example, contour curves tracking experiment based on compliance control strategy is performed. Experiment results show that the ground system is very useful for verifying dexterous manipulation technology of space robot.

Space robotic system is expected to play an increasingly important role in repairing GEO (geostationary orbit) satellites in the future. To perform the servicing mission, the robotic system is firstly required to approach and dock with the target autonomously, for which the measurement of relative pose is the key. It is a challenging task since the existing GEO satellites are generally non-cooperative, i.e. no artificial mark is mounted to aid the measurement. In this paper, a method based on binocular stereo vision is proposed to estimate the pose of a GEO satellite in the final approach phase. It directly takes the natural circular feature on the GEO satellite as the recognized object. Correspondingly, an image processing and pose measurement algorithm is presented to determine the relative position and orientation of the target. This algorithm provides a closed-form solution using simple mathematics, therefore, it is suitable to space applications where the computation capability of the on-board processor is very limited. In addition, it effectively solves the orientation-duality problem for circular feature, requiring neither specific motions of the camera nor a priori knowledge about the radius of the circle. Computer simulations verify the proposed method.