Dynamic obstacle avoidance minimizing energy consumption

The goal of this paper is to solve the problem of dynamic obstacle avoidance for a mobile platform using the stochastic optimal control framework to compute paths that are optimal in terms of safety and energy efficiency under constraints. We propose a three-dimensional extension of the Bayesian Occupancy Filter (BOF) [1] to deal with the noise in the sensor data. We reduce the computational cost of BOF by estimating the velocity of each cell using a Kalman filter. While several obstacle avoidance systems have been presented in the literature addressing safety and optimality of the robot motion separately, we have applied the approximate inference framework to this problem to combine multiple goals, constraints and priors in a structured way. It is important to remark that the obstacles can be moving, therefore classical techniques based on reactive control are not feasible from the point of view of energy consumption. Some experimental results and conclusions will be presented.