Path planning of unmanned vehicle based on multi-objective PSO-ACO fusion algorithm

Objective  There are problems in the course of river crab farming due to water level changes as well as slow convergence and low accuracy of the path planning algorithm of unmanned craft. Therefore, a multi-objective particle swarm-ant colony fusion algorithm for unmanned vehicle path planning was presented to improve the adaptability and optimization ability of the algorithm.

Method  Firstly, the factors such as crab pond environment and breeding law were analyzed, and the environmental model of static water depth in grid was established. Secondly, to cope with the issues of inadequate local point feeding and sub-optimal paths in coverage traversal baiting, a modified particle swarm optimization (PSO) algorithm based on multi-objective was presented by non-linear adjustment of inertia parameters and learning factors. The initial pheromone of the ant colony algorithm was adjusted, and the pheromone volatility factor and heuristic expectation function of the ant colony algorithm were improved to present an adaptive ant colony optimization (ACO) algorithm. Finally, to address the shortcomings of a single algorithm for finding the best, a fusion of PSO-ACO was utilized to realize multi-objective global path planning for baiting vessels.

Result  The simulation results showed that the PSO-ACO algorithm not only had good environmental adaptability but also improved the efficiency and accuracy of multi-target path finding under different environmental baiting strategies. The PSO-ACO algorithm saved the running time by 32%, shortened the path distance by 9.78%, reduced the number of iterations by 62.88% and reduced the number of inflection points by 44.45%.

Conclusion  The proposed multi-objective path planning algorithm is suitable for crab pond culture with variable environment, and has good application value.