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一种用于平面机械臂的无碰撞同伦路径规划新方法。

A Novel Collision-Free Homotopy Path Planning for Planar Robotic Arms.

机构信息

Electronics Department, National Institute for Astrophysics, Optics and Electronics, Luis Enrique Erro 1, Santa María Tonantzintla, Cholula 72840, Puebla, Mexico.

Facultad de Instrumentacion Electronica, Universidad Veracruzana, Cto. Gonzalo Aguirre Beltran S/N, Xalapa 91000, Veracruz, Mexico.

出版信息

Sensors (Basel). 2022 May 26;22(11):4022. doi: 10.3390/s22114022.

Abstract

Achieving the smart motion of any autonomous or semi-autonomous robot requires an efficient algorithm to determine a feasible collision-free path. In this paper, a novel collision-free path homotopy-based path-planning algorithm applied to planar robotic arms is presented. The algorithm utilizes homotopy continuation methods (HCMs) to solve the non-linear algebraic equations system (NAES) that models the robot's workspace. The method was validated with three case studies with robotic arms in different configurations. For the first case, a robot arm with three links must enter a narrow corridor with two obstacles. For the second case, a six-link robot arm with a gripper is required to take an object inside a narrow corridor with two obstacles. For the third case, a twenty-link arm must take an object inside a maze-like environment. These case studies validated, by simulation, the versatility and capacity of the proposed path-planning algorithm. The results show that the CPU time is dozens of milliseconds with a memory consumption less than 4.5 kB for the first two cases. For the third case, the CPU time is around 2.7 s and the memory consumption around 18 kB. Finally, the method's performance was further validated using the industrial robot arm CRS CataLyst-5 by Thermo Electron.

摘要

实现任何自主或半自主机器人的智能运动都需要一种有效的算法来确定可行的无碰撞路径。本文提出了一种应用于平面机械臂的基于无碰撞路径同伦的路径规划算法。该算法利用同伦连续方法(HCM)来求解模型机器人工作空间的非线性代数方程组(NAES)。该方法通过三个具有不同构型的机械臂的案例研究进行了验证。对于第一个案例,一个具有三个连杆的机械臂必须进入一个带有两个障碍物的狭窄通道。对于第二个案例,一个带有抓手的六连杆机械臂需要在一个带有两个障碍物的狭窄通道内取一个物体。对于第三个案例,一个二十连杆的手臂必须在一个迷宫般的环境中取一个物体。这些案例研究通过仿真验证了所提出的路径规划算法的通用性和能力。结果表明,对于前两个案例,CPU 时间为几十毫秒,内存消耗小于 4.5 kB。对于第三个案例,CPU 时间约为 2.7 s,内存消耗约为 18 kB。最后,使用 Thermo Electron 的工业机械臂 CRS CataLyst-5 进一步验证了该方法的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9a8/9183049/ec71fee1bd03/sensors-22-04022-g001.jpg

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