A learning system for motion planning of free-float dual-arm space manipulator towards non-cooperative object

Wang, Shengjie; Cao, Yuxue; Zheng, Xiang; Zhang, Tao

doi:10.1016/j.ast.2022.107980

Cited by 11 publications

(2 citation statements)

References 23 publications

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“…Wu et al (2020) implemented target capture of a floating base dual-arm robot with deep deterministic policy gradient (DDPG), and Liang et al (2021) designed a DRL algorithm based on actor–critic architecture to achieve target capture based on space manipulators. Wang et al (2022) proposed a Constrained Hindsight Experience Replay method to control the free-floating dual-arm space manipulators for target capture. A trajectory planning algorithm of FFSM based on PPO is proposed in Wang et al (2021), which induce the action ensembles method.…”

Section: Introductionmentioning

confidence: 99%

End-to-end active non-cooperative target tracking of free-floating space manipulators

Lei

2023

Transactions of the Institute of Measurement and Control

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Active non-cooperative target tracking is an important ability for free-floating space manipulator (FFSM) systems. Aiming at the difficulty of pose and posture estimation of non-cooperative targets and the complexity of FFSM planning and control, the paper proposed an end-to-end target tracking algorithm using deep reinforcement learning (DRL) algorithm. The algorithm uses raw camera images as input and directly outputs the manipulator joint velocities. To deal with the partially observable Markov decision processes (POMDP) problem caused by single image input, we combine the soft actor–critic algorithm with recurrent neural networks. Therefore, the velocity information of the target is introduced to transform the task into a fully observable Markov decision-making process (MDP). Our approach is completely data-driven. It avoids the position and posture estimation of non-cooperative target and the complex modeling motion planning process of FFSM. We trained and tested the algorithm by building a simulation environment in CoppeliaSim. The results showed that our approach achieved good results in active non-cooperative target tracking of FFSM systems and demonstrated the great potential of DRL algorithms in solving space tasks.

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Section: Introductionmentioning

confidence: 99%

End-to-end active non-cooperative target tracking of free-floating space manipulators

Lei

2023

Transactions of the Institute of Measurement and Control

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“…The use of reinforcement learning and deep learning in robotic arm path planning is being widely applied. Reinforcement learning [ 1 , 2 ] is a method of learning optimal behavior through interactions between an agent and its environment, while deep learning [ 3 ] is a machine learning technique that learns complex patterns and representations through multi-layer neural networks. However, these methods also present challenges.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Dynamic Motion Planning for Dual Manipulator Arms Based on RRT*Smart-AD Algorithm

Long,

Li,

Zhou

et al. 2023

Sensors

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Intelligent manufacturing requires robots to adapt to increasingly complex tasks, and dual-arm cooperative operation can provide a more flexible and effective solution. Motion planning serves as a crucial foundation for dual-arm cooperative operation. The rapidly exploring random tree (RRT) algorithm based on random sampling has been widely used in high-dimensional manipulator path planning due to its probability completeness, handling of high-dimensional problems, scalability, and faster exploration speed compared with other planning methods. As a variant of RRT, the RRT*Smart algorithm introduces asymptotic optimality, improved sampling techniques, and better path optimization. However, existing research does not adequately address the cooperative motion planning requirements for dual manipulator arms in terms of sampling methods, path optimization, and dynamic adaptability. It also cannot handle dual-manipulator collaborative motion planning in dynamic scenarios. Therefore, in this paper, a novel motion planner named RRT*Smart-AD is proposed to ensure that the dual-arm robot satisfies obstacle avoidance constraints and dynamic characteristics in dynamic environments. This planner is capable of generating smooth motion trajectories that comply with differential constraints and physical collision constraints for a dual-arm robot. The proposed method includes several key components. First, a dynamic A* cost function sampling method, combined with an intelligent beacon sampling method, is introduced for sampling. A path-pruning strategy is employed to improve the computational efficiency. Strategies for dynamic region path repair and regrowth are also proposed to enhance adaptability in dynamic scenarios. Additionally, practical constraints such as maximum velocity, maximum acceleration, and collision constraints in robotic arm applications are analyzed. Particle swarm optimization (PSO) is utilized to optimize the motion trajectories by optimizing the parameters of quintic non-uniform rational B-splines (NURBSs). Static and dynamic simulation experiments verified that the RRT*Smart-AD algorithm for cooperative dynamic path planning of dual robotic arms outperformed biased RRT* and RRT*Smart. This method not only holds significant practical engineering significance for obstacle avoidance in dual-arm manipulators in intelligent factories but also provides a theoretical reference value for the path planning of other types of robots.

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On the starting point in designing Kresling origami

Alipour

Arghavani

2023

Aerospace Science and Technology

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A learning system for motion planning of free-float dual-arm space manipulator towards non-cooperative object

Cited by 11 publications

References 23 publications

End-to-end active non-cooperative target tracking of free-floating space manipulators

End-to-end active non-cooperative target tracking of free-floating space manipulators

Cooperative Dynamic Motion Planning for Dual Manipulator Arms Based on RRT*Smart-AD Algorithm

On the starting point in designing Kresling origami

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