Mobile Robot Path Planning Based On Swarm Intelligence Algorithms In The Dynamic Environments

In the global static environment, in order to obtain a higher convergence speed, a new robot path planning algorithm based on shuffled frog leaping is proposed. In this algorithm, frogs choose grids from the optional grid sets either randomly or heuristically. The worst frog updates its path according to the grids which intersect the paths of the best sub-group frogs or the global optimal frog. To further speed up the searching, a scoring method and a search mechanism of bi-directional frog-swarms is introduced. Then the frog’s path will be updated only when corresponding score is under a threshold. A large number of simulation experiments show that the algorithm has very fast convergence speed and stronger environmental adaptability in seeking the optimized path. Moreover, it can avoid collision and plan an optimal path rapidly in a complex static obstacle environment.And in order to increase the velocity of convergence and adaptation ability to environment, a robot path planning algorithm based on multi-artificial fish-swarm is proposed. In our approach, the distance between a fish and a goal is defined as food concentration and the distance between two neighbor grids is defined as step length. The preying behavior of fishes is regard as default behavior and perform clusters act or rear-end act is activated in some certain condition.The fishes search for the optimal location in their visual domain and choose the optimal node in the current feasible neighborhoods. Then the optimal path in static environment is planned by the search mechanism of bi-directional fish-swarms. Many simulation experiments have shown that this algorithm has a fast convergence speed and strong search capability.To solve the problem for dynamic obstacle avoidance, a dynamic obstacle avoidance algorithm based on the rule-base of collision avoidance in dynamic environment is proposed.After obtaining the global optimal path, the rule-base of dynamic collision avoidance is established, from which the robot can obtain effective collision avoidance behavior according to the obtained dynamic obstacle information through sensors. Many simulation experiments have shown that it can avoid collision safely and plan an optimal path rapidly at the same time.Finally, in order to verify the practicality of the algorithms, a large number of experiments have done on the real robot, and the interval strategy on controlling robot walking is proposed.