Research on Terminal Distance Index-Based Multi-Step Ant Colony Optimization for Mobile Robot Path Planning

Aiming at the path planning problem of mobile robot, when the traditional ant colony optimization (ACO) is simulated in grid model, it is assumed that ants can only move to adjacent nodes, that is, the step size is 1 and there are 8 movable directions. However, in practice, the moving direction of the ant is completely free. Therefore, an improved terminal distance index-based multi-step ant colony optimization (TDI-MSACO) for mobile robot path planning is proposed in this paper. A multi-step ant colony optimization (MSACO) is firstly used to improve the flexibility of ant’s movement and the path obtained by using MSACO is shorter and more in line with the actual situation, through the simulation of a large number of cases, it is determined that the optimal step size is 2 or 3. In addition, aiming at the problems of pheromone updating mechanism in MSACO, a concept of terminal distance index (TDI) is proposed to replace pheromone concentration and accelerate the convergence speed of the MSACO. In order to verify the effectiveness of the improved TDI-MSACO, the simulations are tested on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\times 20$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30\times 30$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$50\times 50$ </tex-math></inline-formula> grid models and the results show that the improved TDI-MSACO has faster convergence speed and shorter path. Note to Practitioners—the motivation of this paper comes from the need to develop a fast and efficient path planning method for practical applications such as material transport in shop floor, cleaning, monitoring of dangerous radioactive sites and military applications, and so on. The ant colony optimization (ACO), inspired from the foraging behavior of ant species, is a swarm intelligence algorithm for solving hard combinatorial optimization problems. The ACO is widely used in robot path planning areas because of its characteristics of positive feedback, distributed computation. However, it has the weaknesses of premature convergence and low search speed, which greatly hinder its application. To improve the performance of the ACO, an improved terminal distance index-based multi-step ant colony optimization (TDI-MSACO) for mobile robot path planning is proposed in this paper. Multi-step and terminal distance index are used to improve the flexibility of ant movement and accelerate the convergence speed, respectively. The effectiveness of the TDI-MSACO method for solving mobile path planning problems is proved by comparing the simulation results with those previously presented in the literature. Furthermore, the proposed method can be expanded to the dynamic environment containing multiple static and dynamic obstacles of many sizes and forms, it also can be used to solve the energy-efficient path planning problems. Meanwhile, it can be further modified and applied to the simultaneous scheduling of machines and transport robots in an actual flexible job shop environment.