TY - GEN
T1 - Task space planning with complementarity constraint-based obstacle avoidance
AU - Sinha, Anirban
AU - Sarker, Anik
AU - Chakraborty, Nilanjan
N1 - Publisher Copyright:
© 2021 American Society of Mechanical Engineers (ASME). All rights reserved.
PY - 2021
Y1 - 2021
N2 - In this paper, we present a task space-based local motion planner that incorporates collision avoidance and constraints on end-effector motion during the execution of a task. Our key technical contribution is the development of a novel kinematic state evolution model of the robot where the collision avoidance is encoded as a complementarity constraint. We show that the kinematic state evolution with collision avoidance can be represented as a Linear Complementarity Problem (LCP). Using the LCP model along with Screw Linear Interpolation (ScLERP) in SE(3), we show that it may be possible to compute a path between two given task space poses by directly moving from the start to the goal pose, even if there are potential collisions with obstacles. Scalability of the planner is demonstrated with experiments using a physical robot. We present simulation and experimental results with both collision avoidance and task constraints to show the efficacy of our approach.
AB - In this paper, we present a task space-based local motion planner that incorporates collision avoidance and constraints on end-effector motion during the execution of a task. Our key technical contribution is the development of a novel kinematic state evolution model of the robot where the collision avoidance is encoded as a complementarity constraint. We show that the kinematic state evolution with collision avoidance can be represented as a Linear Complementarity Problem (LCP). Using the LCP model along with Screw Linear Interpolation (ScLERP) in SE(3), we show that it may be possible to compute a path between two given task space poses by directly moving from the start to the goal pose, even if there are potential collisions with obstacles. Scalability of the planner is demonstrated with experiments using a physical robot. We present simulation and experimental results with both collision avoidance and task constraints to show the efficacy of our approach.
KW - Collision avoidance
KW - Complementarity constraints
KW - Motion planning
KW - Task-space planning
UR - https://www.scopus.com/pages/publications/85119971539
U2 - 10.1115/DETC2021-72009
DO - 10.1115/DETC2021-72009
M3 - Conference contribution
AN - SCOPUS:85119971539
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 45th Mechanisms and Robotics Conference (MR)
PB - American Society of Mechanical Engineers (ASME)
T2 - 45th Mechanisms and Robotics Conference, MR 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021
Y2 - 17 August 2021 through 19 August 2021
ER -