Moving obstacle avoidance for cable-driven parallel robots using improved RRT
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Moving obstacle avoidance for cable-driven parallel robots using improved RRT Jiajun Xu1 • Kyoung-Su Park1 Received: 22 October 2020 / Accepted: 1 November 2020 Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Moving obstacle avoidance is one of the most challenging problems for cable-driven parallel robots (CDPRs) due to various constraints. In this work, the improved rapidly exploring random tree (RRT) method is proposed to address moving obstacle avoidance for CDPRs. Compared with the conventional RRT method which mainly focused on the static environment, the suggested method is goal-biased with dynamic step size makes it possible to implement in a dynamic environment. To deal with the particularity of CDPRs, the improved RRT method considers various constraints caused by the cable particularity, which include cable interference and the twist feasible workspace. The swept volume of CDPRs is considered to convert the spatiotemporal problem into a pure spatial problem, therefore the Gilbert–Johnson–Keerthi algorithm was applied to collision detection. Additionally, the velocity information is utilized to estimate time and distance of the closest approach, which also prevents the local minima. The simulation is conducted to illustrate the suggested method and the simulation results are compared with our previous APG-RRT method using batch evaluation. According to the simulation results, the suggested method finds an optimized collision-free path with the average path cost reduced by 32%, and the oscillation time is reduced by 36%. Finally, the suggested method is verified by the experiment.
1 Introduction Cable-driven parallel robots (CDPRs) are a special type of parallel robot developed in the past few decades. They use flexible cables to manipulate the end-effector, and the cable can be extended or retracted using winches and pulleys (Qian et al. 2018). As a result of cable actuation, CDPRs has several advantages such as low moving inertia, heavy payloads capabilities, and especially, the very large workspace (Wang et al. 2019; Abbasnejad et al. 2018; Cui et al. 2018). Moreover, the lightweight cable also guarantees the fast motions of the end-effector, e.g. (Zhang et al. 2020). However, their large workspace means that CDPRs can easily interfere with the external environment. An unknown moving obstacle, such as a particle, drone, or bird, may enter the workspace and cause a collision (Nan et al. 2011). For CDPRs, any such collision might cause an instantaneous change in cable tension, which can distort the & Kyoung-Su Park [email protected] 1
Department of Mechanical Engineering, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do 461-701, South Korea
trajectory of the end-effector and lead directly to the task failure (Choi et al. 2017). Accordingly, researchers have developed algorithms so that unmanned aerial vehicles (UAV), autonomous cars, and even unmanned surface vehicles (USV) can avoid moving objects (Chen
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