A novel global optimal path planning and trajectory method based on adaptive dijkstra-immune approach for mobile robot

Abstract

In this paper a new method to find global optimal path is obtained. Utilization of standard graph searching methods leads to eliminate uncertainness of heuristic algorithms. By using graph searching method a suboptimal solution is obtained, it causes to increase speed, precision and performance of heuristic algorithms. Firstly, the environment is defined with using a useful graph theory. Then by adaptive Dijkstra algorithm a suboptimal path is obtained. Finally, Continuous Clonal Selection Algorithm (CCSA) that is combined with negative selection algorithm, improves this suboptimal path and derives global optimal path. The simulation results show that this suggested method in compression with ant colony and elistic genetic algorithms, has more accuracy and precision, with competitive speed. Also, our suggested algorithm can be used for solving more complicated dynamic problems. Moreover, this proposed approach can be used as standard method in optimization problems especially in path planning and trajectory.

Path Planning of a Mobile Delivery Robot Operating in a Multi-Story Building Based on a Predefined Navigation Tree

“…Dijkstra's algorithm [88] provides the shortest path from one node to every other node on directed graphs [116]. The advantage of Dijkstra's algorithm is that it provides minimally spanning trees as it explores all possible alternative paths and thus eliminates the uncertainness of heuristic algorithms [117,118]. Dijkstra's algorithm is commonly used to estimate the shortest trajectory in many mobile robot applications [119][120][121].…”

Three Dimensional Route Planning for Multiple Unmanned Aerial Vehicles using Salp Swarm Algorithm

“…The problem of route planning by autonomous UAVs in a 2D environment has been solved by many approaches like cell decomposition method [2], Voronoi diagram, visibility graph [3], potential field approach, and rapidly exploring random trees (RRTs) [4], deterministic search algorithm Dijkstra [5] and heuristic based algorithms (A* and D*) [6]. The algorithms mentioned above are proactive, so they are not effective solutions for route planning and suffer from local minima stagnation and considerable time complexity.…”

Immune–wavelet optimization for path planning of large-scale robots

“…Unlike the genetic algorithm, in the AIS algorithm the new generation usually has more adjustment with the environment. Besides, the Continuous Clonal Selection Algorithm (CCSA) 12 has better performance in comparison to other similar evolutionary techniques.…”

“…Furthermore, wavelet mutation was used in differential evolution to control scaling factor. 11 The excellence of the AIS algorithm over other methods has been proved, 12 but it is still not applied on path planning of robots with considering dynamic equations. The proposed method is completely based on an evolutionary algorithm and does not have other methods' problem, such as numerical explosion, 13 while analytical dynamic equations are used.…”