A real-time optimized approach to path planning for mobile robot in unknown environment

For safe path planning of mobile robot in unknown environment, a method is proposed based on improved neural network and simulated annealing algorithm. Neural network is built to describe the working space of the mobile robot, which connection weights are optimized by the back propagation(BP) learning algorithm to study the structural features and information representation of the environment. Simulated annealing (SA) algorithm by using the combination of detectors to reduce the search area is adopted to get the best negative gradient direction of cost function. A strategy of back strategy and ″virtual target″ is introduced to deal with the problem of local minimum, which often occurs in local path planning. The result of the simulation experiment proves the effectiveness and feasibility of the proposed approach.