To address the optimization requirements in mobile robot path planning and enhance the performance limitations of the beetle swarm optimization algorithm regarding convergence precision and application scope, this paper introduces an adaptive elite mutation-based beetle swarm optimization (AEM-BSO) algorithm. The methodological innovations manifest in three principal aspects. Firstly, the implementation of good point set initialization ensures uniform population distribution, effectively mitigating the risk of local optima entrapment. Subsequently, a non-linearly decreasing inertia weight strategy enhances global exploration capabilities during initial iterations while accelerating convergence rates in later stages.Furthermore,incorporation of elite mutation mechanisms that strategically perturb high-performing individuals during iterative processes to prevent premature convergence.For practical implementation in mobile robot navigation, cubic spline interpolation optimizes waypoint connections in generated paths, ensuring kinematic feasibility and smooth trajectory formation.Comprehensive validation across 10 benchmark functions and diverse environmental maps demonstrates the algorithm’s superior optimization precision and robust stability.Experimental comparisons reveal that AEM-BSO achieves respective path length reductions of 0.24%, 18.12%, and 8.41% compared to primitive BSO, PSO and BA, accompanied by significant standard deviation decreases of 25.8%, 96.73%, and 14.13%.These quantitative improvements substantiate the proposed algorithm’s effectiveness in balancing exploration-exploitation trade-offs and enhancing solution quality for complex path planning tasks.