Path planning based on fluid mechanics for mobile robots using unstructured terrain models

Gingras, David R.; Dupuis, Érick; Payre, G.; Lafontaine, Jean de

doi:10.1109/robot.2010.5509679

Cited by 20 publications

(17 citation statements)

References 14 publications

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“…Further similar research has been published by Li et al [10], Ge et al [11], Waydo and Murray [12], Daily and Bevly [13], Sugiyama [14,15], Gingras et al [16], and Owen et al [17]. Most of these approaches use a point source/point sink combination for flow construction.…”

Section: Introductionmentioning

confidence: 68%

Fluid Mechanics for Path Planning and Obstacle Avoidance of Mobile Robots

Palm

Driankov

2014

Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics

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Abstract:Obstacle avoidance is an important issue for off-line path planning and on-line reaction to unforeseen appearance of obstacles during motion of a non-holonomic mobile robot along a predefined trajectory. Possible trajectories for obstacle avoidance are modeled by the velocity potential using a uniform flow plus a doublet representing a cylindrical obstacle. In the case of an appearance of an obstacle in the sensor cone of the robot a set of streamlines is computed from which a streamline is selected that guarantees a smooth transition from/to the planned trajectory. To avoid collisions with other robots a combination of velocity potential and force potential and/or the change of streamlines during operation (lane hopping) are discussed.

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Section: Introductionmentioning

confidence: 68%

Fluid Mechanics for Path Planning and Obstacle Avoidance of Mobile Robots

Palm

Driankov

2014

Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics

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“…Kim and Khosla continued this work with the use of the velocity potential function to avoid obstacles in real time [12]. Further similar research has been published by Li et al [13], Ge et al [14], Waydo and Murray [15], Daily and Bevly [16], Sugiyama [17], and Gingras et al [18]. Most of these approaches use a point source/point sink combination for flow construction.…”

Section: Introductionmentioning

confidence: 77%

Velocity potentials and fuzzy modeling of fluid streamlines for obstacle avoidance of mobile robots

Palm

Driankov

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Iagnemma et al (1999) and Ishigami, Otsuki, & Kubota (2013) use physical vehicle models and discretized elevation maps to assess the feasibility of rover configurations along planned paths. Gingras, Dupuis, Payre, & de Lafontaine (2010) and Rekleitis, Bedwani, Dupuis, Lamarche, & Allard (2013) plan trajectories for rovers in triangular meshes, using the potential field approach or graph search, respectively. Apart from ensuring safety-avoiding tipover and slipping hazards-knowledge of the 3D terrain shape on the large scale can be exploited to plan energy-minimizing paths (Ganganath, Cheng, & Tse, 2015;Rowe & Ross, 1990;Shum, Morris, & Khajepour, 2015;Sun & Reif, 2005).…”

Section: Motion Planning In Nonplanar Mapsmentioning

confidence: 99%

Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments

Krüsi

Furgale

Bosse

et al. 2016

Journal of Field Robotics

140

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We present a practical approach to global motion planning and terrain assessment for ground robots in generic three-dimensional (3D) environments, including rough outdoor terrain, multilevel facilities, and more complex geometries. Our method computes optimized six-dimensional trajectories compliant with curvature and continuity constraints directly on unordered point cloud maps, omitting any kind of explicit surface reconstruction, discretization, or topology extraction. We assess terrain geometry and traversability on demand during motion planning, by fitting robot-sized planar patches to the map and analyzing the local distribution of map points. Our motion planning approach consists of sampling-based initial trajectory generation, followed by precise local optimization according to a custom cost measure, using a novel, constraint-aware trajectory optimization paradigm. We embed these methods in a complete autonomous navigation system based on localization and mapping by means of a 3D laser scanner and iterative closest point matching, suitable for both static and dynamic environments. The performance of the planning and terrain assessment algorithms is evaluated in offline experiments using recorded and simulated sensor data. Finally, we present the results of navigation experiments in three different environments-rough outdoor terrain, a two-level parking garage, and a dynamic environment, demonstrating how the proposed methods enable autonomous navigation in complex 3D terrain. C

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Path planning based on fluid mechanics for mobile robots using unstructured terrain models

Cited by 20 publications

References 14 publications

Fluid Mechanics for Path Planning and Obstacle Avoidance of Mobile Robots

Fluid Mechanics for Path Planning and Obstacle Avoidance of Mobile Robots

Velocity potentials and fuzzy modeling of fluid streamlines for obstacle avoidance of mobile robots

Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments

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