Abstract
Collision avoidance implies that extra motion in joint space must be taken, which might exert unexpected influences on the execution of the desired end-effector tasks. In this paper, a novel framework for generating collision-free trajectories while respecting task priorities is proposed. Firstly, a data-driven approach is applied to learn an efficient representation of the distance decision function of the system. The function is then working as the collision avoidance constraints in the inverse kinematics (IK) solver, which avoids the collision between manipulators. To eliminate undesired influences of the extra motion for collision avoidance on the execution of tasks, task constraints are proposed to control the task priorities, offering the system with the ability to trade off between collision avoidance and task execution. Furthermore, the overall framework is formulated as a QP (quadratic programming), therein guarantees a real time performance. Numerical simulations are conducted to demonstrate the effectiveness and efficiency of the presented method.
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This work is supported by National Natural Science Foundation (NNSF) of China under Grant U1713203, 51729501 and 61803168.
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Luo, JW., Xu, J., Hou, Y., Xu, H., Wu, Y., Zhang, HT. (2020). Task-Oriented Collision Avoidance in Fixed-Base Multi-manipulator Systems. In: Chan, C.S., et al. Intelligent Robotics and Applications. ICIRA 2020. Lecture Notes in Computer Science(), vol 12595. Springer, Cham. https://doi.org/10.1007/978-3-030-66645-3_7
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