scholarly journals Design and Ground Verification of Space Station Manipulator Control Method for Orbital Replacement Unit Changeout

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Bingshan Hu ◽  
Feng Chen ◽  
Liangliang Han ◽  
Huanlong Chen ◽  
Hongliu Yu
Keyword(s):  
Path Planning ◽  
Control Method ◽  
Impedance Control ◽  
Space Station ◽  
Open Loop ◽  
Control Algorithms ◽  
Experiment Platform ◽  
Planning Algorithm ◽  
Application Requirements ◽  
Path Planning Algorithm

Chinese space station has been in construction phase, and it will be launched around 2020. Lots of orbital replacement units (ORUs) are installed on the space station, and they need to be replaced on orbit by a manipulator. In view of above application requirements, the control method for ORU changeout is designed and verified in this paper. Based on the analysis of the ORU changeout task flow, requirements of space station manipulator’s control algorithms are presented. The open loop path planning algorithm, close loop path planning algorithm based on visual feedback, and impedance control algorithm are researched. To verify the ORU changeout task flow and corresponding control algorithms, a ground experiment platform is designed, which includes a 6-DOF manipulator with a camera and a force/torque sensor, an end effector with clamp/release and screwing function, ORU module, and ORU store. At last, the task flow and control algorithms are verified on the test platform. Through the research, it is found that the ORU changeout task flow designed in this paper is reasonable and feasible, and the control method can be used to control a manipulator to complete the ORU changeout task.

scholarly journals Research and Ground Verification of the Force Compliance Control Method for Space Station Manipulator

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bingshan Hu ◽  
Huanlong Chen ◽  
Liangliang Han ◽  
Hongliu Yu
Keyword(s):  
Real Time ◽  
Contact Force ◽  
Control Algorithm ◽  
Control Method ◽  
Impedance Control ◽  
Space Station ◽  
Gravity Compensation ◽  
Compliance Control ◽  
Experiment Platform ◽  
Dual Arm

The space station manipulator does lots of tasks with contact force/torque on orbit. To ensure the safety of the space station and the manipulator, the contact force/torque of manipulator must be controlled. Based on analyzing typical tasks’ working flows and force control requirements, such as ORU (orbit replacement unit) changeout and dual arm collaborative payload transport, an impedance control method based on wrist 6 axis force/torque feedback is designed. For engineering implementation of the impedance control algorithm, the discretization method and impedance control parameters selection principle are also studied. To verify the compliance control algorithm, a ground experiment platform adopting industrial manipulators is developed. In order to eliminate the influence of gravity, a real-time gravity compensation algorithm is proposed. Then, the correctness of real-time gravity compensation and force compliance control algorithm is verified on the experiment platform. Finally, the ORU replacement and dual arm collaborative payload transport experiments are done. Experimental results show that the force compliance control method proposed in this paper can control the contact force and torque at the end of the manipulator when executing typical tasks.

scholarly journals Minimum Energy Demand Locomotion on Space Station

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Wing Kwong Chung ◽  
Yangsheng Xu
Keyword(s):  
Path Planning ◽  
Energy Demand ◽  
Planning Process ◽  
Minimum Energy ◽  
Space Station ◽  
Space Manipulators ◽  
Planning Algorithm ◽  
Precious Resource ◽  
Path Planning Algorithm ◽  
Moving Path

The energy of a space station is a precious resource, and the minimization of energy consumption of a space manipulator is crucial to maintain its normal functionalities. This paper first presents novel gaits for space manipulators by equipping a new gripping mechanism. With the use of wheels locomotion, lower energy demand gaits can be achieved. With the use of the proposed gaits, we further develop a global path planning algorithm for space manipulators which can plan a moving path on a space station with a minimum total energy demand. Different from existing approaches, we emphasize both the use of the proposed low energy demand gaits and the gaits composition during the path planning process. To evaluate the performance of the proposed gaits and path planning algorithm, numerous simulations are performed. Results show that the energy demand of both the proposed gaits and the resultant moving path is also minimum.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation

2011 ◽  
Vol 142 ◽  
pp. 12-15
Author(s):  
Ping Feng
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Traffic Condition ◽  
Planning Time ◽  
Dynamic Path Planning ◽  
Short Period ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm ◽  
Chaos Genetic Algorithm

The paper puts forward the dynamic path planning algorithm based on improving chaos genetic algorithm by using genetic algorithms and chaos search algorithm. In the practice of navigation, the algorithm can compute at the best path to meet the needs of the navigation in such a short period of planning time. Furthermore,this algorithm can replan a optimum path of the rest paths after the traffic condition in the sudden.

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