Motion planning of a mobile robot as a discrete optimization problem

Igarashi and Ioi (2000) proposed a solution to motion planning of a mobile robot. They formulated the problem as a discrete optimization problem at each time step. To solve the optimization problem, they used an objective function consisting of a goal term, a smoothness term and a collision term. We propose a theoretical method using reinforcement learning for adjusting weight parameters in the objective functions. However, the conventional Q-learning method cannot be applied to a non-Markov decision process, which is caused by the smoothness term. Thus, we applied Williams's (1992) learning algorithm, episodic REINFORCE, to derive a learning rule for the weight parameters. This maximizes a value function stochastically. We verified the learning rule by some experiments.