Manipulation at optimum locations for maximum force transmission with mobile robots under environmental disturbances

Abstract

Remote manipulation plays a key role for applications in hazardous conditions, yet designing a robust controller enabling safe interaction with unknown environment and under the influence of disturbances is a challenge. In this study, we propose effective control and optimization methods for mobile robotic manipulator systems that can increase effort transmission to a task in desired directions. The vehicle position is optimized by utilizing constrained particle swarm optimization where the objective is to enhance directional manipulability of the robotic arm within the system. A forward dynamic controller is implemented to eliminate undesired excessive motions near singular joint configurations. A reset control algorithm along with an admittance type controller are developed for stable interaction with an unknown object under environmental disturbances. The experimentally validated results show that the proposed method phase out undesired position disturbances and increase the directional manipulability for the required task enabling augmented effort transmission for the task execution.