Path Planning Collision Avoidance of a SCARA Manipulator using PRM with Fuzzy

Emerson V. A. Dias; Josias G. Batista; Catarina G. B. P. Silva; Geraldo L. B. Ramalho; Darielson A. Souza; José Leonardo N. Silva; André P. Moreira

doi:10.20906/CBA2022/3587

Emerson V. A. Dias Mobile Robotics Laboratory - Department of Industry, Federal Institute of Education, Science and Technology of Ceará - IFCE, Campus Fortaleza, Fortaleza, CE
Josias G. Batista Mobile Robotics Laboratory - Department of Industry, Federal Institute of Education, Science and Technology of Ceará - IFCE, Campus Fortaleza, Fortaleza, CE
Catarina G. B. P. Silva Mobile Robotics Laboratory - Department of Industry, Federal Institute of Education, Science and Technology of Ceará - IFCE, Campus Fortaleza, Fortaleza, CE
Geraldo L. B. Ramalho Mobile Robotics Laboratory - Department of Industry, Federal Institute of Education, Science and Technology of Ceará - IFCE, Campus Fortaleza, Fortaleza, CE
Darielson A. Souza Research Group on Automation, Control and Robotics - Department of Electrical Engineering, Federal University of Ceará, Fortaleza, CE
José Leonardo N. Silva Mobile Robotics Laboratory - Department of Industry, Federal Institute of Education, Science and Technology of Ceará - IFCE, Campus Fortaleza, Fortaleza, CE
André P. Moreira Mobile Robotics Laboratory - Department of Industry, Federal Institute of Education, Science and Technology of Ceará - IFCE, Campus Fortaleza, Fortaleza, CE

DOI: https://doi.org/10.20906/CBA2022/3587

Keywords: Path planning, collision avoidance, probabilistic roadmap, SCARA manipulator, PRM-Fuzzy

Abstract

Robotics was introduced in the world with the objective of automating industrial processes, that is, facilitating human work, and with this new algorithms have been emerging to increasingly improve the use of robots in an autonomous way. This paper compares the Probabilistic Road-map (PRM) and PRM-Fuzzy applied for a SCARA manipulator (Selective Compliance Assembly Robot Arm). The PRM-Fuzzy algorithm is used to optimize the path generated by the PRM, as it makes the path smoother and shorter. Are used in collision avoidance path planning and is compared by computational cost (processing time), multiple correlation coefficient, (R2), the mechanical energy consumption of the motors, and the shortest collision avoidance paths. The results show the trajectories generated by the algorithms in the Cartesian space and also the trajectories of each joint of the manipulator, calculated from the inverse kinematics. Velocities, accelerations, and torques for trajectories are also shown. Several scenarios with different obstacles were used and in all cases, the PRM-Fuzzy algorithm performed better than the conventional PRM for the comparisons performed.