Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics

Pauplin, Olivier; Louchet, Jean; Lutton, Évelyne; Fortelle, Arnaud de La

doi:10.20965/jaciii.2005.p0622

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…less significant flies. More details about the fitness function used can be found in (Pauplin et al, 2005;Pauplin, 2007). …”

Section: Discussionmentioning

confidence: 99%

“…The Fly Algorithm (Louchet, 2000;Boumaza & Louchet, 2003;Pauplin et al, 2005) is an evolutionary algorithm based on the individual approach, and used in the domain of computer vision (Jähne, 1999). The aim of the algorithm is to drive the population of individuals, defined as 3-D points (the "flies") in front of a pair of cameras, into suitable areas of the search space, corresponding to the surfaces of objects present in the scene.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Evolution to Obstacle Detection and Mobile Robot Control

Pauplin¹,

Fortelle²

2008

Frontiers in Evolutionary Robotics

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“…less significant flies. More details about the fitness function used can be found in (Pauplin et al, 2005;Pauplin, 2007). …”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Artificial Evolution to Obstacle Detection and Mobile Robot Control

Pauplin¹,

Fortelle²

2008

Frontiers in Evolutionary Robotics

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“…Cooperative coevolution methods (e.g. Parisian evolution) have also produced good results for obstacle detection [26] and 3D reconstruction, the latter used either for computing the 3D coordinates from a pair of images [25], or for optimizing the placement of the different cameras [5]. A recent tutorial on evolutionary computer vision was given by Cagnoni [2].…”

Section: Related Workmentioning

confidence: 99%

Evolution of visual controllers for obstacle avoidance in mobile robotics

Barate

Manzanera

2009

Evol. Intel.

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The purpose of this work is to automatically design vision algorithms for a mobile robot, adapted to its current visual context. In this paper we address the particular task of obstacle avoidance using monocular vision. Starting from a set of primitives composed of the different techniques found in the literature, we propose a generic structure to represent the algorithms, using standard resolution video sequences as an input, and velocity commands to control a wheel robot as an output. Grammar rules are then used to construct correct instances of algorithms, that are then evaluated using different protocols: evaluation of trajectories performed in a goal reaching task, or imitation of a hand-guided trajectory. A genetic program is applied to evolve populations of algorithms in order to optimize the performances of the controllers. The first results obtained in a simulated environment show that the evolution produces algorithms that can be easily interpreted and which are clearly adapted to the visual context. However, the resulting trajectories are often erratic, and the generalization capacities are poor. To improve the results, we propose to use a two-phase evolution combining imitation and goal reaching evaluations, and to add some constraints in the grammar rules to enforce a more generic behavior. The results obtained in simulation show that the evolved algorithms are more efficient and more generic. Finally, we apply the imitation based evolution on real sequences and test the evolved algorithms on a real robot. Though simplified by dropping the goal reaching constraint, the resulting algorithms behave well in a corridor centering task, and show certain generalization capacities.

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“…Ebner also developed a navigation system for a robot using range sensors [15] but he didn't combine the two approaches for a visionbased navigation system. Parisian evolution has also been shown to produce very good results for obstacle detection and 3D reconstruction but those systems need two calibrated cameras [16,17].…”

Section: Vision In Evolutionary Roboticsmentioning

confidence: 99%

Automatic Design of Vision-Based Obstacle Avoidance Controllers Using Genetic Programming

Barate

Manzanera

Lecture Notes in Computer Science

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Abstract. The work presented in this paper is part of the development of a robotic system able to learn context dependent visual clues to navigate in its environment. We focus on the obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an offline procedure to automatically design algorithms adapted to the visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming hand-designed controllers.

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Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics

Cited by 10 publications

References 7 publications

Application of Artificial Evolution to Obstacle Detection and Mobile Robot Control

Application of Artificial Evolution to Obstacle Detection and Mobile Robot Control

Evolution of visual controllers for obstacle avoidance in mobile robotics

Automatic Design of Vision-Based Obstacle Avoidance Controllers Using Genetic Programming

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