A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

Abstract

Free space algorithms are kind of graphics-based methods for path planning. With previously known map information, graphics-based methods have high computational efficiency in providing a feasible path. However, the existing free space algorithms do not guarantee the global optimality because they always search in one connected domain but not all the possible connected domains. To overcome this drawback, this article presents an improved free space algorithm based on map decomposition with multiple connected domains and artificial bee colony algorithm. First, a decomposition algorithm of single-connected concave polygon is introduced based on the principle of concave polygon convex decomposition. Any map without obstacle is taken as single-connected concave polygon (the convex polygon map can be seen as already decomposed and will not be discussed here). Single concave polygon can be decomposed into convex polygons by connecting concave points with their visible vertex. Second, decomposition algorithm for multi-connected concave polygon (any map with obstacles) is designed. It can be converted into single-connected concave polygon by excluding obstacles using virtual links. The map can be decomposed into several convex polygons which form multiple connected domains. Third, artificial bee colony algorithm is used to search the optimal path in all the connected domains so as to avoid falling into the local minimum. Numerical simulations and comparisons with existing free space algorithm and rapidly exploring random tree star algorithm are carried out to evaluate the performance of the proposed method. The results show that this method is able to find the optimal path with high computational efficiency and accuracy. It has advantages especially for complex maps. Furthermore, parameter sensitivity analysis is provided and the suggested values for parameters are given.

Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

“…Inspired by ref. [20], a weight is set for the fitness function of each cost. The weight represents the importance of the cost.…”

“…The scout bee strategy refers to that in the iterative process. If a nectar source has gone through the employed bee strategy and the follower bee strategy and has not been updated, the drone path is deleted and a different UAV flight path is randomly generated globally, which then enters the iterative process again for optimization [20]. Each time the scout bee strategy is used to optimize the path, the scout bee replaces the employed bee to search.…”

Efficient Spatial Representation and Routing of Deformable One-Dimensional Objects for Manipulation

“…We should note that the idea of convex decomposition in planning has been explored in other contexts before [16], [17], [18]. However, here it is used differently and for defining the DOO configuration rather than planning itself.…”

Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

“…Wang used the global optimization ability of Artificial Bee Colony (ABC) algorithm to solve the problem that the traditional A* algorithm was difficult to obtain the optimal path [28], but ABC algorithm had the disadvantage of locally optimal solution and low stability. Li decomposes and connects the obstacle map to form multiple convex polygons based on the convex decomposition principle of concave polygons, and then uses the ABC algorithm to search for the optimal path in all the connected domains to avoid the local optimum [29]. However, none of the above methods can solve the problem of avoiding dynamic obstacles.…”