A kind of biomimetic control method to anthropomorphize a redundant manipulator for complex tasks

It is an urgent problem for robots to operate complex tasks with some unknown motion mechanisms caused by the strong coupling of force and motion. However, humans can perform complex tasks well due to their natural evolution and postnatal training. A novel biomimetic control method based on a human motion mechanism with high movement adaptability is proposed in this paper. The core is to present a novel variable-parameter compliance controller based on human operation mechanisms with an action-planning method derived from optimization by human motion, and the main contribution is to change the parameters of compliance controller according to human operating intention synchronized with humanoid motion;this change could establish a humanoid map between the force and motion for a seven degree-of-freedom redundant manipulator to deal with the unknown motion mechanism in complex tasks, so the redundant manipulator can operate complex tasks with high performance. Sufficient experiments were performed, and the results validated the effectiveness of the proposed algorithm.

Autonomous Assembly Method of 3-Arm Robot to Fix the Multipin and Hole Load Plate on a Space Station

Using space stations for a large number of observation, exploration, and research is a necessary way to fully develop spacetechnology. It is a necessary means of space experiment to install the extravehicular experimental load by using the loadplate. However, the extravehicular environment is full of danger, which poses a threat to the health and even safety ofastronauts. Using robots to replace astronauts to complete this task can effectively reduce the threat to astronauts. Aimingat the problem that the configurations of existing space robots have difficulty in balancing the contradiction betweencomplexity and dexterity, our previous work proposes a 12-DOF 3-arm robot and preliminarily explores the feasibility ofits large-scale ability. This paper focus on the 8-DOF redundant dexterous manipulator composed of 2 of the robot arms.In view of the difficulties in solving the inverse kinematics of the redundant manipulator, the challenges of complexenvironmental lighting, and difficulties of matching multiple groups of holes and pins in the load plate assembly task, theresearch on the autonomous assembly of the load plate is carried out. The main work is as follows: (a) A variable D-Hparameter inverse kinematics solution method is proposed, which lays a foundation for humanoid dexterous operationplanning of the robot. (b) An autonomous operation method based on visual guidance and variable parameter admittancecontrol is proposed. Finally, the safety and robustness of the robot in the autonomous assembly of the load plate withmultipins and holes are successfully verified by experiments.

A novel space target-tracking method based on generalized Gaussian distribution for on-orbit maintenance robot in Tiangong-2 space laboratory

Accurate target tracking based on visual images is the key for intelligent robots to assist or replace astronauts to work in space station. However, the special space environment such as non-uniform illumination and high-energy particle radiation is a huge challenge, which may lead to complex noise coupling in vision image. This paper proposes a novel method for accurate target tracking, the essence of which is the Retinex image enhancement algorithm in CIELAB color space(LAB-GRetinex) and the generalized maximum correntropy Kalman filter(GMCKF) which are all based on generalized Gaussian distribution. The LABGRetinex algorithm chooses the CIELAB color space, which is closer to the human vision, as the processing color space, and the generalized Gaussian distribution can estimate the light image accurately, so the influence of non-uniform illumination can be reduced effectively. Meanwhile, the GMCKF algorithm adopts the generalized correntropy criterion based on the generalized Gaussian distribution to replace the minimum mean square error(MMSE) criterion to realize the optimal filtering effect under the complex non-Gaussian noise, which can improve the target tracking accuracy. Sufficient ground simulation experiments and application experiments in the Tiangong-2 space laboratory verify the effectiveness of the proposed algorithm which can track the target accurately in the special space environment and provide the precise pose information for on-orbit robot maintenance verification. This research lays a technical foundation for the application of intelligent robot in the construction and operation on space station in the future.

Generalized Maximum Correntropy Kalman Filter for Target Tracking in TianGong-2 Space Laboratory

Target tracking plays an important role in the construction,operation,and maintenance of the space station by the robot,which puts forward high requirements on the accuracy of target tracking.However,the special space environment may cause complex non-Gaussian noise in target tracking data.And the performance of traditional Kalman Filter will deteriorate seriously when the error signals are non-Gaussian,which may lead to mission failure.In the paper,a novel Kalman Filter algorithm with Generalized Maximum Correntropy Criterion(GMCKF)is proposed to improve the tracking accuracy with non-Gaussian noise.The GMCKF algorithm,which replaces the default Gaussian kernel with the generalized Gaussian density function as kernel,can adapt to multi-type non-Gaussian noises and evaluate the noise accurately.A parameter automatic selection algorithm is proposed to determine the shape parameter of GMCKF algorithm,which helps the GMCKF algorithm achieve better performance for complex non-Gaussian noise.The performance of the proposed algorithm has been evaluated by simulations and the ground experiments.Then,the algorithm has been applied in the maintenance experiments in TianGong-2 space laboratory of China.The results validated the feasibility of the proposed method with the target tracking precision improved significantly in complex non-Gaussian environment.