Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot

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.

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

“…BAS, along with PSO(Khan 2022), ACO (Ijaz et al 2022), GA , Firefly Algorithm (FA) (Yang and Deb 2014), Bat Algorithm (BA) (Geem et al 2001), ABC (Wei et al 2021) and similar algorithms, belong to the category of metaheuristic swarm intelligence optimization algorithms. This section embarks upon an exploration of the relative merits and drawbacks of the BAS algorithm vis-à-vis its counterparts, as well as delves into the specific problem domains wherein each algorithm finds its niche.…”

“…In its execution, the BHA draws inspiration from the gravitational dynamics of celestial black holes. Similarly, the ant colony optimization (ACO) (Ijaz et al 2022) leverages principles of collective intelligence, emulating the foraging behavior of ant colonies through the dissemination of pheromones and cooperative actions. This approach has proven particularly efficacious in addressing combinatorial optimization problems.…”

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

“…BAS is a single particle searching algorithm, where the particle optimizes an objective function by searching the search space iteratively. The utility of BAS has expanded to several real-world problems [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ], including the portfolio optimization. Ref.…”