A neural network approach to real-time path planning with safety consideration

In this paper, a neural network approach is proposed for real-time path planning of robots with safety consideration. The neural network is topologically organised, which is based on a previous biologically inspired model for dynamical trajectory generation of a mobile robot in a nonstationary environment. The state space of the neural network can be the joint space of multilink robot manipulators or the Cartesian workspace. This model is capable of dealing with multiple target problems as well. The target globally attracts the robot, while the obstacles push the robot away locally to avoid collisions. By taking into account of the clearance from obstacles, the planned "comfortable" path does not suffer either the "too close" or the "too far" problems. Each neuron has only local lateral connections. The optimal path is generated in real-time through the dynamics of the neural activity landscape without explicitly optimising any cost function. Therefore, it is computationally efficient. The stability of the network is guaranteed by the existence of a Lyapunov function. The effectiveness and efficiency are demonstrated through simulation studies.