Research Article
Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot
@ARTICLE{10.4108/airo.v1i.656, author={Mubashir Usman Ijaz and Ameer Tamoor Khan and Shuai Li }, title={Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={1}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2022}, month={6}, keywords={Meta-heuristic optimization, Trajectory Tracking, Path planning, Obstacle avoidance, Bio-inspired algorithm}, doi={10.4108/airo.v1i.656} }
- Mubashir Usman Ijaz
Ameer Tamoor Khan
Shuai Li
Year: 2022
Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot
AIRO
EAI
DOI: 10.4108/airo.v1i.656
Abstract
Trajectory tracking and obstacle avoidance lies at the heart of autonomous navigation for mobile robots. In this paper, a control architecture for trajectory tracking while avoiding obstacles and controller tuning is proposed for a differential drive mobile robot (DMR). The framework of optimization algorithm is inspired by the food search behavior of beetles using their antennae. Path planning and controller tuning remain computationally demanding tasks despite of the proposed algorithms existing today. Our bio inspired approach unifies these two problems by minimizing the respective cost functions and solving the optimization problem efficiently. Trajectory tracking problem is based on the difference of the current and next pose of the robot while obstacle avoidance is achieved on the principle of maximizing the minimum distance between the robot and obstacle in the path of the robot. The proposed architecture is simulated in V-REP environment using MATLAB. Simulation results have verified that beetle antennae search can successfully plan and track the reference path by tuning the PID controller efficiently.
Copyright © 2022 Mubashir Usman Ijaz et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.