A cable-driven hyper-redundant manipulator (CDHM) has superior dexterity for confined space applications. However, the modeling and control considering the cables are very complex. In this paper, we established the kinematics and dynamics models and proposed a dynamics control strategy. The multi-level mapping between the motors, cables, joints and end-effector was first analyzed. The corresponding kinematics equations were derived and solved by combining analytical and numerical methods. Especially, the cable coupling relationship was established and a decoupling method was addressed to compensate the coupled motion between cables. Furthermore, we derived the dynamics equations including the cable forces and the joint variables. Considering practical control requirements, the cables' forces were distributed by simplifying the dynamics equations and obtaining the minimal solutions. Then, we presented a dynamics control strategy, which uses the forward and inverse kinematics of multi-level mapping for motion resolution and compensation, and computes the feedforward torques for the motors using recursive dynamics and "cable force - motor torque" relationship. Finally, a prototype and a truss inspection experiment system were developed to verify the corresponding models and methods. Experiment results show that the derived kinematic and the dynamic equations, and the proposed dynamic control strategy are effective.

Grasping unstructured objects and sensing the contact force are two vital issues for grippers. However, it is still difficult for most existing grippers to realize these two functions simultaneously. In this article, we revise the traditional fin-ray finger by inserting a series of rigid nodes into the compliant structure and develop an adaptive two-finger gripper. This design linearizes the gripper's deformation-force relationship and enables an intrinsic force sensing ability without any tactile sensor. Experimental results show that the finger has high accuracy in sensing the external force applied at its middle part (average error less than 3%) but much larger errors appear near its two ends. Further experiments indicate that the gripper functions well in sensing the total grasping force (average error less than 8%). Although larger errors are observed in estimating the force distribution at each node, the variation tendency of the sensed force coincides well with the ground truth. Experiments are also carried out on grasping free-form objects and performing pick-and-place operations to further prove the gripper's adaptive grasping and intrinsic force sensing abilities.

With a light-slender and superior dexterous body, the cable-driven segmented redundant manipulators (CSRMs) are excellent candidates for operations in narrow confined space. However, their application is still largely limited due to their weak stiffness, low load capacity, and complex modeling and planning. In this article, we design a new type of CSRM with high stiffness and load capacity using hybrid active and passive cables, and propose a simplified kinematics modeling and configuration planning method. The manipulator is composed of three active-linkage segments and one active tool end-effector. Each active segment, driven by three active driving cables, has two degrees of freedom (DOFs). By utilizing the passive linkage cables, the segment can be fully constrained with high stiffness, and move with equal joint angles in nearly continuous shape. Based on such structural features, the kinematics equations about the active segment-joint variables (i.e., the equalpitch and yaw angles of the whole segment) are derived. The direct, inverse kinematics and differential kinematics are greatly simplified. To find suitable solutions under environment constraints, extended virtual joint (EVJ) is introduced for quick regional solutions searching and equivalent kinematic constraint analysis. Furthermore, a configuration planning method based on the simplified kinematics and equivalent EVJs is proposed for single obstacle avoidance and continuous narrow space crossing tasks. Finally, a prototype system is developed and typical experiments are performed. The results show the high linkage accuracy, improved stiffness, and load capacity of the developed CSRM and verify the adaptability of the proposed configuration planning method.

SUMMARY: The technologies of autonomous rendezvous and robotic capturing of non-cooperative targets are very crucial for the future on-orbital service. In this paper, we proposed a method to achieve this aim. Three problems were addressed: the target recognition and pose (position and attitude) measurement based on the stereo vision, the guidance, navigation and control (GNC) of the chaser, and the coordinated plan and control of space robot (CP&C). The pose measurement algorithm includes image filtering, edge detection, line extraction, stereo match and pose computing, et al. Based on the measured values, a certain GNC algorithm was designed for the chaser to approach and rendezvous with the target. Then the CP&C algorithm, which is proved to be advantageous over the traditional separated method, was used to plan and track the trajectories of the base pose and the joint angle. At last, a 3D simulation system was developed to evaluate the proposed method. Simulation results verified the corresponding algorithms.

The pose (position and attitude) measurement of space non-cooperative targets is one of the key technologies in on-orbit service. Traditional stereo-vision-based methods are easily affected by illumination conditions, while the 3D laser radar has good stability, but the point cloud is sparse. In this paper, a method based on 3D laser radar and stereo-vision information is proposed to measure the pose and estimate the motion for non-cooperative targets. The proposed method can not only adapt to the harsh lighting conditions, but also has better robustness and higher precision. Firstly, find the matching point corresponding to the binocular camera by using the stereo matching algorithm, and then calculate the three-dimensional coordinates of the matching points according to the least squares method. Combined with the fusion calibration result, the point cloud data acquired by the 3D laser radar is converted to the left camera frame. Secondly, integrate the point cloud data obtained by the above two sensors to obtain a complete point cloud model of the non-cooperative target. Thirdly, the obtained point cloud model is registered, and the rigid transformation matrix of two adjacent point clouds is solved. Then, the relative pose and velocity (linear velocity and angular velocity) of the non-cooperative target are estimated by the EKF(Extended Kalman Filter) algorithm. Finally, an experimental system was built. It consists of a UR5 robot, a satellite mockup, two cameras, a 3D laser radar and an API laser tracker (for evaluating the accuracy of measurement) was built. The experiment results verified the proposed method.

Dual-arm robots have widely used for fine manipulation such as assembly, maintenance, payload carrying and handling, et al. In order to ensure the safety during human-robot interaction (HRI), it is necessary for robot to detect unexpected collisions in real time and make fast and accurate responses. In this paper, we propose a practical collision detection and coordinated compliance control method based on momentum observer for a dual-arm robot without any force/torque sensors. The interactions between the dual-arm robot, human, environment and object to be manipulated are fully considered. The momentum observer is designed by combining the dynamics of the dual-arm robot. It is used to detect the collision and identify the mass of the object to be manipulated. The coordinated compliance control of the dual-arm robot is then realized by the impedance control of each arm and object, under the coordinated behavior during performing common task and manipulating common objects. The desired force and trajectory of each arm's end-effector are optimized based on the minimum energy criterion. The simulation and experimental results of typical tasks verify the proposed method.