A Novel Hybrid Neural Network-Based Multirobot Path Planning With Motion Coordination
Buddhadeb Pradhan, Arijit Nandi, Nirmal Baran Hui, Diptendu Sinha Roy, Joel J. P. C. Rodrigues
Abstract
Multi-robot navigation is a challenging task, especially for many robots, since individual gains may more often than not adversely affect the global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace whereas the motion of every individual robot is deduced by a novel Particle Swarm Optimization (PSO) tuned Feed Forward Neural Network (FFNN). Motion coordination among the robots is implemented using a cooperative coordination algorithm that identifies critical robots and maintains cooperation count while actuating deviation in select robots. The contribution of this paper is twofold; firstly in hybridizing the Artificial Neural Network(ANN) by employing PSO, an evolutionary algorithm, to find optimal values of deviation for every critical robot using velocity and acceleration constraints, secondly ensuing the convergence of the PSO by carrying first and second order stability analysis. Experiments have been carried out to evaluate and validate the efficacy of the proposed coordination schemes by changing the number of robots under hundred different scenarios each, and the founded results demonstrate the efficacy of the proposed schemes.