A hybrid IBAS-ISCA route optimization model for mobile robots in static environments

Amidst the fast-paced advancements in mobile robot technologies, route optimization, which forms the crux of autonomous navigation, must strike a balance between obstacle avoidance safety, travel distance, and movement efficiency. This study proposes a dual-layer hybrid route optimization model that combines an improved Beetle Antennae Search and an improved Sine Cosine Algorithm. The model uses Opposition-Based Learning to optimize the initial population. It applies an improved Sine Cosine Algorithm with nonlinear decay and adaptive inertia weight to enhance global exploration ability. It also integrates an improved Beetle Antennae Search mechanism with dynamic step size and multi-beetle parallel search to strengthen local refinement. Simulation results show that under standard benchmark functions, the model achieves higher convergence accuracy and better stability than comparison methods. In a simple grid environment, the optimized robot route length is 29.22 m, which is reduced by 16.73% to 23.83% compared with other methods. In a more complex obstacle environment, the length of the optimized route is 36.98 m, featuring fewer turns, smoother routes, and relatively low computational cost. Such results demonstrate that the proposed model achieves an effective balance between global exploration and local exploration. The new design exhibits better stability and flexibility, making it a viable option for obstacle avoidance in static grid-based scenarios.