A Cooperative Path Planning Algorithm for a Multiple Mobile Robot System in a Dynamic Environment

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\u27s 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

Multi-Objective Pathfinding in Dynamic Environments

Traditional pathfinding techniques are known for calculating the shortest path from a given start point to a designated target point on a directed graph. These techniques, however, are inapplicable to pathfinding problems where the shortest path may prove to be hazardous for traversal, or where multiple costs of differing unit-types lie along the same path. Moreover, the shortest path may not be optimal if it requires forfeiting a valuable resource. While strategic methods have been proposed in the past to completely avoid paths determined to be dangerous, these methods lack the functionality to provide agents the ability to decide which resources are more valuable for conservation, and which resources possess the greatest risk at being lost. For environments where risk varies dynamically across edges, we propose a solution that can determine a path of least expected weight based on multiple properties of edges. With this Multi-Objective Pathfinding technique, agents can make decisions influenced by highest priority objectives and their preferences to trading off some resources for others. The solution is based on traditional pathfinding techniques, extending their usability to cover strategic and dynamic scenarios where additional properties contained within the search map could render them useless. Nevertheless, our solution is compatible with problems where the goal is to simply find the least weighted path, otherwise known as the objectively resource-conservative path among a set of vertices in a graph