This paper proposes a new path planning method called the priority order navigation algorithm (PONA) for multi-robot navigation in a large flat space. The PONA can guarantee collision-free and efficient travel in the space with fixed or/and dynamic obstacles. The priority order of robots is assigned by the user based on the importance degree of the robots' tasks and the objective is to make the higher priority robot reach its target faster than the lower priority robot. This study uses the generalized Voronoi diagram (GVD) to establish the initial map for PONA and links the navigation points in GVD to plan the path for each robot. Further, we modify the navigation point links to shorten feasible paths for the lower priority robot and its shortest two feasible paths can be switched to each other based on a certain condition to avoid hitting the higher priority robot. The proposed PONA is compared to several benchmark path planning methods, which are the shortest distance algorithm (SDA) and reciprocal orientation algorithm (ROA), in the simulation section and it is found that the PONA can reduce the average length of the trajectory by more than 10% compared with ROA and SDA.INDEX TERMS Voronoi diagram, Yen's algorithm, multi-robot path planning, collision-free, path-priority order.
This study proposes a novel multi-robot navigation algorithm with priority order called, in short, PONA2.0. This algorithm is based on the generalized Voronoi diagram and contains an adjustable multipath switching mechanism and a collision prevention strategy, such that the arrival order of robots is in line with the priority order as much as possible, and the average trajectories length is as short as possible. The given average trajectories length of all robots (ATLA) and arrival order (AO) are used to be the two performance indices for the comparison between the proposed algorithm and recent existing algorithms NSPP (Huang et al., 2021), PONA (Huang et al., 2022), ROA, and SDA (Ali et al., 2016). The comparison shows that the PONA2.0 can reduce the average AO by more than 56% compared with NSPP and PONA and reduce the average ATLA between 5% and 17% compared with ROA and SDA.INDEX TERMS Voronoi diagram, Yen's algorithm, multi-robot path planning, collision-free, path-priority order.
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