Neural network dynamics for path planning and obstacle avoidance
Neural Networks
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Mathematics and Computers in Simulation
Approximating shortest path for the skew lines problem in time doubly logarithmic in 1/epsilon
Theoretical Computer Science - Algebraic and numerical algorithm
The visibility-Voronoi complex and its applications
Computational Geometry: Theory and Applications - Special issue on the 21st European workshop on computational geometry (EWCG 2005)
Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network
Expert Systems with Applications: An International Journal
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
A base function for generating contour traversal paths in stereolithography apparatus applications
Expert Systems with Applications: An International Journal
Sampling-Based Roadmap of Trees for Parallel Motion Planning
IEEE Transactions on Robotics
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
A neural network for shortest path computation
IEEE Transactions on Neural Networks
Nonconvex maximization for communication systems based on particle swarm optimization
Computer Communications
Expert Systems with Applications: An International Journal
Multi-objective path planning in discrete space
Applied Soft Computing
A Hopfield neural network applied to the fuzzy maximum cut problem under credibility measure
Information Sciences: an International Journal
| Hi-index | 12.05 |
This paper deals with motion planning in plane for a mobile robot with two freedom degrees through some polygonal unmoved obstacles. Applying Minkowski sum, we can represent the robot as a point. Then, by using traditional approaches such as visibility graphs, simple and generalized Voronoi diagrams, decomposition methods, etc, it is possible to provide a graph covering obstacles, say roadmap. In order to find a real-time collision-free robot motion planning between two arbitrary source and target configurations through the roadmap, an adoptive Hopfield neural network is considered. Maximizing the clearance of path together with minimizing the length of path are pursued in a bi-objective framework. For treating with multiple objectives TOPSIS method, as a kind of goal programming techniques, is provided to find the efficient solutions. Because of capability of parallel computation through hardware implementation of neural networks, the presented approach is a reasonable technique in mobile robot navigation and traveler guidance systems. The advantages of the proposed system are confirmed by simulation experiments. This approach can be directly extended in unknown environment including time-varying conditions.