Shared Control with Flexible Obstacle Avoidance for Manipulator

Wei, Zhixuan; Chen, Weidong; Wang, Hesheng

doi:10.1007/978-3-319-48036-7_17

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Several researchers have proposed methods about continuum robot path planning. The traditional methods for manipulator path planning include rapid expansion of random tree [2][3], polynomial interpolation [4], artificial potential field method [5], probability roadmap method [6], machine learning [7][8], etc. In reference [9], the authors evaluated four trajectory generation strategies in terms of the resulting Cartesian paths and spatial extent of the course of motion.…”

Section: Introductionmentioning

confidence: 99%

Path planning based on Q-learning and three-segment method for aircraft fuel tank inspection robot

Niu¹,

Kailu²

2018

Filomat

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In order to realize the path planning of continuum robot for inspecting defects in the aircraft fuel tank compartment, an approach based on Q-learning and Three-segment Method was proposed, and the posture of the robot meeting the inherent and spatial structure constraint requirements was planned. Firstly, the simulation model of the aircraft fuel tank was established. Moreover, the workspace was rasterized to decrease the computing complexity. Secondly, the Q-learning algorithm was applied and the path from the initial point to the target was generated. In terms of target guided angle and three-segment method, the joint variables corresponding to each transition point on the path could be obtained. Finally, the robot reached the target by progressively updating the joint variables. Simulation experiments were implemented, and the results verified the effectiveness and feasibility of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Path planning based on Q-learning and three-segment method for aircraft fuel tank inspection robot

Niu¹,

Kailu²

2018

Filomat

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“…Different from the above-mentioned pushing method, we don't consider the accurate force analysis between the manipulator and the obstacle, since in our method the contact part is not limited. In our previous work, 10 we presented a control method to implement FOA. Instead of analyzing the forces of obstacle, a cost space was build to represent the risk while the robot interacts with the obstacle.…”

Section: Introductionmentioning

confidence: 99%

Manipulator motion planning using flexible obstacle avoidance based on model learning

Wei

Chen

Wang

et al. 2017

International Journal of Advanced Robotic Systems

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Traditional manipulator motion planning methods aim to find collision-free paths. But in highly cluttered environments, it is hard to find available solutions. We present a novel motion planning strategy which integrates the sampling-based path planning algorithm with the flexible obstacle avoidance approach for finding the efficient path through changing poses of movable obstacles. Following the resulting path, the manipulator can push the obstacles away and move to the target simultaneously. For dealing with the safety issue of the interaction between manipulator and obstacles, a learning-based motion modeling method is proposed for motion prediction of the obstacles being pushed by manipulator, and then the trained models are utilized in the motion planning. The results from both simulations and real robot experiments show that the proposed method can generate efficient paths which cannot be solved by traditional method.

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Shared Control with Flexible Obstacle Avoidance for Manipulator

Cited by 2 publications

References 7 publications

Path planning based on Q-learning and three-segment method for aircraft fuel tank inspection robot

Path planning based on Q-learning and three-segment method for aircraft fuel tank inspection robot

Manipulator motion planning using flexible obstacle avoidance based on model learning

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