Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots

Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches.