Search

Path planning is an NP-hard problem that is aimed to satisfy multi-constraint optimization requirements. In autonomous robotics applications, path planning together with collision avoidance presents a challenging task. It necessitates the generation of possible search directions from a designated point to a fixed varying destination location satisfying spatial constraints. This paper presents a framework for the design of an intelligent multi-objective robotic path planning algorithm. The algorithm relies on the generation of way-points by hybridizing two meta-heuristics techniques, namely Grey Wolf Algorithm (GWO) and Particle Swarm Optimization (PSO). A frequency-based modification in GWO search operators is introduced to fasten the search process. An improvised search strategy is employed for collision detection and avoidance, which converts non-desired points into the desired point. Sensors are deployed around the robot vicinity for search optimization. Mutation operators are then introduced to improve path length by smoothing out the trajectory. The proposed algorithm's effectivity is then validated through extensive simulations, in which different condition environments are simulated. To validate the effectiveness of the proposed methodology, the results are compared with contemporary algorithms namely Minimum Angle Artificial Bee Colony (MAABC) algorithms, Hybrid Cuckoo Search-Bat Algorithm (BA-CSA), Bacterial Bolony (BC) and Genetic Algorithm (GA) algorithms. The results conclusively demonstrated that the proposed algorithm ensures effective performance in path smoothness and safety under a wide range of conditions. • A novel intelligent multi-objective framework for robotic path planning algorithm. • A hybridizing two meta-heuristics techniques to solve the path planning. • An improvised search strategy is employed for collision detection and avoidance. • The proposed algorithm's effectively validated through extensive simulations. [ABSTRACT FROM AUTHOR]