Dynamic Obstacle Avoidance Using Bayesian Occupancy Filter and Approximate Inference

Ángel Llamazares, Vladimir Ivan, Eduardo Molinos, Manuel Ocaña, Sethu Vijayakumar

Research output: Contribution to journalArticlepeer-review

Abstract

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 threedimensional extension of the Bayesian Occupancy Filter (BOF) (Cou´e et al. Int. J. Rob. Res. 2006, 25, 19–30) to deal with the noise in the sensor data, improving the perception stage. We reduce the computational cost of the perception stage by estimating the velocity of each obstacle using optical flow tracking and blob filtering. 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 problem involves obstacles that can be moving, therefore classical techniques based on reactive control are not optimal from the point of view of energy consumption. Some experimental results, including comparisons against classical algorithms that highlight the advantages, are presented.
Original languageEnglish
Pages (from-to)2929-2944
Number of pages16
JournalSensors
Volume13
Issue number3
DOIs
Publication statusPublished - 1 Mar 2013

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