Evolutionary Trajectory Planning with Obstacles for a Mobile Manipulator

Gonçalves, Antônio R.C.; Guerrero-Peña, Elaine; Araújo, A.F.R.; Durand-Petiteville, Adrien

doi:10.1016/j.ifacol.2020.12.2109

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Zhang et al 8 designed a novel soft gripper for the mobile manipulator and proposed the autonomous grasp planning for the finger based on the visual recognition. Gonc xalves et al 9 compared the efficiency and effectiveness of four state-of-the-art evolutionary algorithms considering the collision avoidance for the mobile manipulator, and found that Parato-based algorithms are suitable for off-line trajectory planning. Xu et al 10 presented a base positions planning method to search for the shortest path for a mobile manipulator, including steps of IK solutions, reachability database construction, base positioning uncertainty analysis and path planning.…”

Section: Introductionmentioning

confidence: 99%

HCDR-based motion planning for mobile manipulators with the continuous trajectory task

Xu,

Tao,

Pei

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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Mobile manipulators have been widely used in modern industrial fields due to their advantages of large operating workspace and high motion dexterity. For the application, the path planning has been a core foundation for its motion control. However, as the environmental complexity and performance requirements increase, existing methods are limited by low execution efficiency, and lack of comprehensive consideration of motion performances. A motion planning technique is proposed for mobile manipulators with continuous trajectory task. In this technique, a hierarchical constraints dimension reduction (HCDR) strategy is presented based on the task decomposition and performance requirements analysis of each stage. HCDR strategy aims to improve the execution efficiency of the algorithm through local operations of performance constraints and the local simplification of the distance calculation at different stages of the task. Models of inverse kinematics and performance indexes are established to provide basis for constraint conditions, including distance models with different accuracy, manipulability index model and trajectory tracing error model. Two optimization strategies are presented to solve the motion planning problem considering the overall performance of the system. And simulation results show that the three-level strategy has the higher convergence efficiency than the bi-level strategy, as well as can guarantee the motion performance of the system.

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Section: Introductionmentioning

confidence: 99%