Abstract
A practical path planning method for a multiple mobile robot system (MMRS) requires handling both the collision-free constraint and the kinematic constraint of real robots, the latter of which has to date been neglected by most path planning methods. In this paper, we present a practical cooperative path planning algorithm for MMRS in a dynamic environment. First, each robot uses an analytical method to plan an obstacle-avoidance path. Then, a distributed prioritized scheme is introduced to realize cooperative path planning. In the scheme, each robot calculates a priority value according to its situation at each instant in time, which will determine the robot's priority. Higher-priority robots can ignore lower-priority robots, whereas lower-priority robots should avoid collisions with higher-priority robots. To minimize the path length for MMRS, a least path length constraint is added. The priority value is also calculated by a path cost function that takes the path length into consideration. Unlike other priority methods, the algorithm proposed is not time consuming; therefore, it is suitable for dynamic environments. Simulation results are presented to verify the effectiveness of the proposed algorithm.
Highlights
As autonomous mobile robots are being deployed in increasingly complex scenarios, it is essential for a team of robots to be able to work in parallel and perform more complex tasks than can be accomplished by a single robot [1]
A well-known and essential requirement for any multiple mobile robot system (MMRS) is that the robots must be able to navigate safely through the environment in which the tasks are to be performed [2]; proper path planning must be developed
The grid-based method combined with the A * algorithm is considered a good choice for path planning [6]
Summary
As autonomous mobile robots are being deployed in increasingly complex scenarios, it is essential for a team of robots to be able to work in parallel and perform more complex tasks than can be accomplished by a single robot [1]. The potential field method is a classic and useful method for path planning. The grid-based method combined with the A * algorithm is considered a good choice for path planning [6]. This method uses a grid-based representation of the environment. The breadth-first search is a graph search algorithm that is suitable for grid-based path planning. This algorithm begins at the root node and explores all neighbouring nodes, explores all neighbouring nodes of each nearest node. Grid-based methods can effectively represent the environment; a shortcoming of grid-based path planning is that higher precision requires more computation, making this method unsuitable for complex environments [8]
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