Global path planning for explosion-proof robot based on improved ant colony optimization
Honglei Che, Zongzhi Wu, Rongxue Kang, Chao Yun · 2016
The Explosion-Proof Robot (EPR) is widely applied in environmental monitoring of uncertain danger sources, path planning is the premise for fulfilling the task successfully. A global path planning approach based on improved Ant Colony Optimization (ACO) is proposed to find the optimal path in the uncertain environment. Grid method is used to establish environment modeling of the robot. The global information of working environment is adopted to establish target attraction function, which guide the ant colony to improve the probability of selecting the optimal path to the target point (danger source). Meanwhile, a rule updating the pheromone based on the assignment rule of wolf colony is proposed, which solves the problem of getting into local optimum and increasing the convergence speed. Finally, comparing with classical ACO through simulation in simple and complicated environment, the proposed algorithm is verified to have a good dynamic performance and could converge to the shortest path quickly.