HRA&amp;#x2217;: Hybrid randomized path planning for complex 3D environments

Teniente, Ernesto H.; Andrade‐Cetto, Juan

doi:10.1109/iros.2013.6696588

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…Furthermore, it does not require explicit terrain surface reconstruction, which allows trajectories to be directly planned on point cloud maps using only local terrain assessment. Regarding this set of characteristics, the methods of Kobilarov & Sukhatme (2005) and Teniente & Andrade-Cetto (2013) are most closely related to ours. Kobilarov & Sukhatme (2005) use a PRM planner while locally approximating the terrain surface by small planar patches.…”

Section: Motion Planning In Nonplanar Mapsmentioning

confidence: 89%

“…Kobilarov & Sukhatme (2005) use a PRM planner while locally approximating the terrain surface by small planar patches. Teniente & Andrade-Cetto (2013) propose a "hybrid randomized" planner called HRA * , which expands a tree of kinematically feasible trajectory segments, combining random sampling with a constraint-aware cost-to-goal heuristic. The trajectory segments are computed by forward-simulating a robot model and projecting the resulting planar path segment to a 3D point cloud map.…”

Section: Motion Planning In Nonplanar Mapsmentioning

confidence: 99%

See 1 more Smart Citation

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

123

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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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Section: Motion Planning In Nonplanar Mapsmentioning

confidence: 89%

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

123

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“…ToF cameras, on the other hand, have limited range. To accommodate dense long‐range sensing, we have built instead a series of 3D scanners (Teniente & Andrade‐Cetto, ; Valencia et al., ), and used them to build large and dense maps of the environment.…”

Section: Related Workmentioning

confidence: 99%

“…The local planner, on the contrary, is a simpler planner that plans small paths to avoid obstacles locally as observed by the ToF camera. Since the global planner was previously reported in Teniente and Andrade‐Cetto (), it is not fully developed here. The main contribution of this section is on the use of a B‐spline smoother in the local planner to account for the nonholonomic constraints of our vehicle.…”

Section: Robot Navigationmentioning

confidence: 99%

Terrain Classification in Complex Three‐dimensional Outdoor Environments

Santamaria-Navarro

Teniente

Morta

et al. 2014

Journal of Field Robotics

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This paper presents two techniques to detect and classify navigable terrain in complex 3D environments. The first method is a low level on-line mechanism aimed at detecting obstacles and holes at a fast frame rate using a time-of-flight camera as the main sensor. The second technique is a high-level off-line classification mechanism that learns traversable regions from larger 3D point clouds acquired with a laser range scanner. We approach the problem using Gaussian Processes as a regression tool, in which the terrain parameters are learned, and also for classification, using samples from traversed areas to build the traversable terrain class. The two methods are compared against unsupervised classification, and sample trajectories are generated in the classified areas using a non-holonomic path planner. We show results of both the low-level and the high-level terrain classification approaches in simulations and in real-time navigation experiments using a Segway RMP400 robot.

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“…In addition, most mobile robots have non-holonomic restrictions. A second option explored in this work is to use a fast and optimal planner like RRT* [9], [16], [17] to drive the robot to the configuration with largest entropy decrease. This method will renounce to the most entropy decreasing path and choose instead the shortest path in the free configuration space meeting nonholonomic restrictions if needed.…”

Section: B Rrt* To the Configuration With Largest Entropy Decreasementioning

confidence: 99%

Dense entropy decrease estimation for mobile robot exploration

Vallvé¹,

Andrade‐Cetto²

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We propose a method for the computation of entropy decrease in C-space. These estimates are then used to evaluate candidate exploratory trajectories in the context of autonomous mobile robot mapping. The method evaluates both map and path entropy reduction and uses such estimates to compute trajectories that maximize coverage whilst minimizing localization uncertainty, hence reducing map error. Very efficient kernel convolution mechanisms are used to evaluate entropy reduction at each sensor ray, and for each possible robot position and orientation, taking frontiers and obstacles into account. In contrast to most other exploration methods that evaluate entropy reduction at a small number of discrete robot configurations, we do it densely for the entire C-space. The computation of such dense entropy reduction maps opens the window to new exploratory strategies. In this paper we present two such strategies. In the first one we drive exploration through a gradient descent on the entropy decrease field. The second strategy chooses maximal entropy reduction configurations as candidate exploration goals, and plans paths to them using RRT*. Both methods use PoseSLAM as their estimation backbone, and are tested and compared with classical frontier-based exploration in simulations using common publicly available datasets.

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HRA∗: Hybrid randomized path planning for complex 3D environments

Cited by 7 publications

References 16 publications

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

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

Terrain Classification in Complex Three‐dimensional Outdoor Environments

Dense entropy decrease estimation for mobile robot exploration

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