Aco Procedure

To sum up, specific steps of mobile robot path planning based on the improved ant colony algorithm are as follows:

Step 1: The working environment is modeled by the grid method, and the starting point start and the target point goal of the mobile robot are given.

Step 2: Initialize the ant system. Set the number m of ants, parameter α which determines the relative influence of the pheromone trail, parameter β which determines the heuristic value, the global pheromone volatilization coefficient ρ, pheromone intensity Q₁ and other related parameters.

Step 3: Update taboo table. Place the ant k (k = 1, 2, ⋯ , m) on the current node and add the current node to the corresponding taboo table.

Step 4: Process deadlock. According to the taboo table, it will judge whether ants are trapped in a deadlock state. If the ants are in a deadlock state, the retraction mechanism will be adopted and the deadlock node will be added to the taboo table. Conversely, it will judge whether the ants reach the target point. If the ants reach the target point, it will turn to Step 6, otherwise it will turn to Step 5.

Step 5: Select the next grid. It will calculate the heuristic function according to formula (6), and calculate the probability function according to formula (2). Finally, it will use the roulette method to select the next feasible grid. If the ants reach the target grid, it will turn to Step 6, otherwise it will turn to Step 3.

Step 6: If the ants reach the target node, it will repeat Step 3 until each ant completes the search target during its iteration process and then turn to Step 7.

Step 7: Update pheromone. After each iteration, if the number of iterations satisfies inequality N ≤ N_max, it will update the path pheromone and determine whether it meets the convergence conditions. If it meets the convergence conditions, it will withdraw. If it does not meet, it will turn to Step 3. If the number of iterations satisfies inequality N > N_max, it will be not counted further. The final result is output as long as the end condition is satisfied.

The implementation process of improved ant colony algorithm is as in Table 1.

Description of ACO algorithm for solving path planning.