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Sensors 2013, 13(3), 2929-2944; doi:10.3390/s130302929
Article
Dynamic Obstacle Avoidance Using Bayesian Occupancy Filter and Approximate Inference
1
Department of Electronics, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
2
Institute of Perception, Action and Behaviour, University of Edinburgh, Edinburgh EH8 9AB, UK
* Author to whom correspondence should be addressed.
Received: 31 January 2013; in revised form: 14 February 2013 / Accepted: 16 February 2013 / Published: 1 March 2013
(This article belongs to the Special Issue New Trends towards Automatic Vehicle Control and Perception Systems)
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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.
Keywords: autonomous navigation; obstacle avoidance; optimal control
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MDPI and ACS Style
Llamazares, Á.; Ivan, V.; Molinos, E.; Ocaña, M.; Vijayakumar, S. Dynamic Obstacle Avoidance Using Bayesian Occupancy Filter and Approximate Inference. Sensors 2013, 13, 2929-2944.
AMA StyleLlamazares Á, Ivan V, Molinos E, Ocaña M, Vijayakumar S. Dynamic Obstacle Avoidance Using Bayesian Occupancy Filter and Approximate Inference. Sensors. 2013; 13(3):2929-2944.
Chicago/Turabian StyleLlamazares, Ángel; Ivan, Vladimir; Molinos, Eduardo; Ocaña, Manuel; Vijayakumar, Sethu. 2013. "Dynamic Obstacle Avoidance Using Bayesian Occupancy Filter and Approximate Inference." Sensors 13, no. 3: 2929-2944.