Probabilistic Multi Robot Path Planning in Dynamic Environments: A Comparison between A* and DFS

Abstract

In this paper, a probabilistic roadmap planner algorithm with the multi robot path planning problem have been proposed by using the A* search algorithm in a dynamic environment. The whole process consists of two phases. In the first phase: Preprocessing phase, the work space is converted into the configuration space, constructing a probabilistic roadmap graph in the free space, and finding the optimal path for each robot using a global planner that avoids the collision with the static obstacles. The second phase: Moving phase, moves each robot in a prioritized manner from its starting point to its ending point through a near optimal path with avoiding collision with the moving obstacles and the other robots. A comparison has been done with the depth first algorithm to see the difference. The simulation results shows that choosing A* search algorithm affect positively the speed of the two phases together in comparison to the depth first search algorithm. General TermsArtificial intelligence, Robot path planning.

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

“…It can be observed that the main drawback of the above approach is the reduction of autonomy in mobile robots, i.e., mobile robots cannot decide the number of path elements by themselves and the human help is needed. Additionally, if the number of path elements is not well established, a large value can over-define the path (this can complicate the post-processing task aspects [38] such as path smoothing [39], control [40]) and energetic efficiency [41], while a lower one can reduce the chances of finding the optimal path [42]. A way to deal with the above difficulty is to treat the path-planning as a Variable-Length-Vector Optimization Problem (VLV-OP) to determine the number of the variables (points) and their values (Cartesian coordinates) enhancing the application performance.…”

Artificial Potential Field with Discrete Map Transformation for Feasible Indoor Path Planning

“…There are many techniques that has been adopted to solve the path planning problem. The blind search technique, i.e., Breath First Search (BFS) [7] and Depth First Search (DFS) [8], traverses every single state available until the feasible solution is found. They are typically used to solve maze problems.…”

AUV path planning for environment changes over time

“…Some methods meet only a few of these requirements, and some meet all requirements. Traditional path planning methods include depth first search(DFS) [3] , breadth first search(BFS) and swarm intelligence algorithms like genetic algorithms, ant colony algorithms, etc. However, these algorithms are often used to solve the problem that the solution to the problem with a constant risk range from the start point to the end point.…”