Application of Improved Butterfly Optimization Algorithm in Mobile Robot Path Planning

Abstract

An improved butterfly optimization algorithm (IBOA) is proposed to overcome the disadvantages, including slow convergence, generation of local optimum solutions, and deadlock phenomenon, of the optimization algorithm in the path planning of mobile robots. A path-planning grid model is established based on an improved obstacle model. First, the population diversity is improved by introducing kent mapping during population position renewal in the normal butterfly optimization algorithm (BOA) to enhance the global search ability of the butterfly population. Second, an adaptive weight coefficient is introduced in the renewal process of each generation to increase the convergence speed and accuracy. An opposition-based learning strategy based on convex lens imaging is introduced to help the butterfly population jump out of the local optimum. Finally, a mutation strategy is introduced to solve the path planning problem. On this basis, two path simplification strategies are proposed to make up for the shortcomings of planning paths in grid maps. The shortest path lengths solved by IBOA, BOA, and GA in the 20 × 20 map are 30.97, 31.799, and 31.799, respectively. The numbers of iterations for the shortest paths searched by IBOA, BOA, and GA are 14, 24, and 38 in that order. The shortest path lengths solved by IBOA, BOA and GA in the 40 × 40 map are 63.84, 65.60, and 65.84, respectively. The number of iterations for the shortest paths searched by IBOA, BOA and GA are 32, 40, and 46, respectively. Simulation results show that IBOA has a strong ability to solve robot path planning problems and that the proposed path simplification strategy can effectively reduce the length of the optimal path in the grid map to solve the path planning problem of mobile robots. The shortest paths solved by IBOA in 20 × 20 and 40 × 40 maps are simplified to lengths of 30.2914 and 61.03, respectively.

Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture

“…From the tractors that till the fields to the autonomous drones that monitor crops from above, agricultural machinery must traverse these landscapes with precision and efficacy. The A-star algorithm, renowned for route planning and pathfinding, is pivotal in enabling these machines to perform their tasks efficiently [10][11][12][13][14]. By leveraging the A-star algorithm, agricultural equipment can map out optimal paths, thus avoiding obstacles and minimizing soil compaction.…”