Research on obstacle climbing gait structure design and gait control of hexapod wall climbing robot based on STM32F103 core controller

The hexapod wall climbing robots have the advantages of traversing complex surfaces. To traverse environments autonomously, it must possess capability to select gait parameters and paths appropriate for surface. Path planning optimization is a fundamental issue in aspect stable, energy efficient robot navigation with static dynamic obstacles. Traditional statistical models been developed get optimal path but result obtained was very poor. Metaheuristic algorithms are gaining importance robotic planning. In this paper, we proposed robust two stage approach predicting collision-free, distance-minimal, smooth ensuring patterns using hybrid metaheuristic algorithms. first stage, predicted Tri-objective Grey Wolf Optimization (TGWPO) based on obstacle target detection. second adaptive constructed optimized Adaptive multi-objective Particle swarm (AMPSO). designed STM32F103 as core controller modeled planner (using TDWPO) optimizer module AMPSO). commands controls climb according path. We analyzed efficacy TDWPO-AMPSO existing approaches terms length, time, stability, avoidance, efficiency. analysis showed that suggested over conventional strategies robots.