Novel Augmented Tuna Swarm Optimization Algorithm for Mobile Robot Path Planning

Efficient path planning is a critical component for mobile robots to execute tasks in complex environments. This paper proposes an augmented Tuna Swarm Optimization algorithm (LCTSO) for optimal path planning. In algorithm design, Lévy flight based on lifetime mechanism is incorporated to enhance global exploration of feasible paths. The introduction of Cauchy mutation boosts population diversity, improving convergence performance in complex terrains with multiple obstacles. This algorithm’s enhancement is validated on selected CEC2017 benchmark functions. In comparative experiments across four terrain scenarios, the proposed algorithm LCTSO outperforms competing methods, generating shorter collision-free robot paths. This not only ensures efficiency but also contributes to cost savings during execution.