The Quadruped Robot Uses the Trajectory Planned by DIACO to Complete the Obstacle Avoidance Task

Abstract

The diffusion-improved ant colony optimization (DIACO) algorithm, as introduced in this paper, addresses the slow convergence speed and poor stability of the ant colony optimization (ACO) in obstacle avoidance path planning for quadruped robots. DIACO employs a nonuniformly distributed initial pheromone, which reduces the blind search time in the early stage. The algorithm updates the heuristic information in the transition probability, which allows ants to better utilize the information from the previous iteration during their path search. Simultaneously, DIACO adjusts the pheromone concentration left by ants on the path based on the map information and diffuses the pheromone within a specific range following the artificial potential field algorithm. In the global pheromone update, DIACO adjusts the pheromone on both the optimal path and the worst path generated by the current iteration, thereby enhancing the probability of ants finding the optimal path in the subsequent iteration. This paper designs a steering gait based on the tort gait to fulfill the obstacle avoidance task of a quadruped robot. The effectiveness of the DIACO algorithm and steering gait is validated through a simulation environment with obstacles constructed in Adams. The simulation results reveal that DIACO demonstrates improved convergence speed and stability compared to ACO, and the quadruped robot effectively completes the obstacle avoidance task using the path planning provided by DIACO in combination with the steering gait.