Introduction to the theory of neural computation
Introduction to the theory of neural computation
Robot Motion Planning
Heuristic Algorithm for Robot Path Planning Based on Real Space Renormalization
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
An Algorithm for Robot Path Planning with Cellular Automata
Proceedings of the Fourth International Conference on Cellular Automata for Research and Industry: Theoretical and Practical Issues on Cellular Automata
Spatial Planning: A Configuration Space Approach
IEEE Transactions on Computers
Robot Path Planning in Kernel Space
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Modeling floor-cleaning coverage performances of some domestic mobile robots in a reduced scenario
Robotics and Autonomous Systems
Hi-index | 0.00 |
A simple effective method for path planning based on a growing self-organizing elastic neural network, enhanced with a heuristic for the exploration of local directions is presented. The general problem is to find a collision-free path for moving objects among a set of obstacles. A path is represented by an interconnected set of processing units in the elastic self organizing network. The algorithm is initiated with a straight path defined by a small number of processing units between the start and goal positions. The two units at the extremes of the network are static and are located at the start and goal positions, the remaining units are adaptive. Using a local sampling strategy of the points around each processing unit, a Kohonen type learning and a simple processing units growing rule the initial straight path evolves into a collision free path. The proposed algorithm was experimentally tested for 2 DOF and 3 DOF robots on a workspace cluttered with random and non random distributed obstacles. It is shown that with very little computational effort a satisfactory free collision path is calculated.