Mobile robot global path planning in a static environment is an important problem all along. The paper proposes a method of global path planning based on neural network and genetic algorithm. The neural network model of environmental information in the workspace for a robot is constructed. Using this model, the relationship between a collision avoidance path and the output of the model is estab...

Explosive ordnance disposal robot path planning is a nondeterministic polynomial time (NP) problem, traditional optimization methods are not very effective to it, which are easy to plunge into local minimum. In this paper, a new algorithm-Danger Model Immune Wavelet Neural Network (DIWNN) is improved. It is proposed to initialize the weights and biases of wavelet neural network (WNN), the ergod...

This paper presents an approach to mobile robot path planning using case-based reasoning together with map-based path planning. The map-based path planner is used to seed the case-base with innovative solutions. The casebase stores the paths and the information about their traversability. While planning the route those paths are preferred that according to the former experience are least risky.

Path planning is one of the most vital elements of mobile robotics. With a priori knowledge of the environment, global path planning provides a collision-free route through the workspace. The global path plan can be calculated with a variety of informed search algorithms, most notably the A* search method, guaranteed to deliver a complete and optimal solution that minimizes the path cost. Path ...