Path planning with uncertainty: Voronoi Uncertainty Fields

Ok, Kyel; Ansari, Sameer A.; Gallagher, Billy; Sica, William; Dellaert, Frank; Stilman, Mike

doi:10.1109/icra.2013.6631230

Cited by 39 publications

(20 citation statements)

References 18 publications

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“…2) Navigation Planning: A two-level approach to collision-free navigation using artificial potential fields on the lower layer is proposed in [22]. Similar to our work, completeness of the path planner is guaranteed by an allocentric layer on top of local collision avoidance.…”

Section: Related Workmentioning

confidence: 92%

Obstacle detection and navigation planning for autonomous micro aerial vehicles

Nieuwenhuisen

Droeschel

Beul

et al. 2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-Obstacle detection and real-time planning of collision-free trajectories are key for the fully autonomous operation of micro aerial vehicles in restricted environments.In this paper, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a 3D laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. We generate trajectories in a multi-layered approach: from mission planning to global and local trajectory planning, to reactive obstacle avoidance.We evaluate our approach in simulation and with the real autonomous micro aerial vehicle.

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Section: Related Workmentioning

confidence: 92%

Obstacle detection and navigation planning for autonomous micro aerial vehicles

Nieuwenhuisen

Droeschel

Beul

et al. 2014

2014 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…, p K ), which are the intervals that allow responders to pass through the roads. For example, for a user who defines risk level as follows: full-safe levels={L1, L2}, partial-safe levels={L3, L4}, and non-safe levels = {L5}, a timeline, as shown in Figure 6, can be converted into a list of full-safe intervals ([0, 9], [20, +∞]), a list of partial-safe intervals ( [13,20]), and a list of open intervals ([0, 9], [13, +∞]). It should be noted that we do not consider the non-safe intervals in the routing as they are excluded from the search space.…”

Section: A Defining Safe Intervalsmentioning

confidence: 99%

“…Ok et al [13] develop a path planner called Voronoi Uncertainty Fields, which uses Voronoi diagrams and potential fields to deal with map uncertainties. Neumany and Likhachevy [12] design a generalization to the PPCP (Probabilistic Planning with Clear Preferences) algorithm, which allows a robot to reason about uncertainty in the trajectories of dynamic obstacles.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for First Responders in the Presence of Moving Obstacles With Uncertain Boundaries

Wang

Zlatanova

Oosterom

2017

IEEE Trans. Intell. Transport. Syst.

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In this paper, we study the path planning for first responders in the presence of uncertain moving obstacles. To support the path planning, in our research we use hazard simulation to provide the predicted information of moving obstacles. A major problem in using hazard simulation is that the simulation results may involve uncertainty due to model errors or noise in the real measurements. To address this problem, we provide an approach to handle the uncertainty in the information of moving obstacles, and apply it to the case of toxic plumes. Our contribution consists of two parts: 1) a spatial data model that support representation of uncertain obstacles from hazard simulations and their influence on the road network; 2) a modified A* algorithm that can deal with the uncertainty and generate fast and safe routes passing though the obstacles. The experimental results show the routing capability of our approach and its potential for the application to real disasters.

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“…Same works attempt to combine potential fields with a global guiding structure that is estimated from a known environment to attain global path planning (Ok et aL 2013). Other types of fields such as gradient fields of harmonic scalar fields have also been studied [Shade and Newman 2011).…”

Section: Related Workmentioning

confidence: 99%

Autonomous reconstruction of unknown indoor scenes guided by time-varying tensor fields

Zheng

Yan

et al. 2017

ACM Trans. Graph.

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(a) (b) (c) (d) Fig. 1. We introduce an algorithm for the autonomous reconstruction of indoor scenes, based on time-varying tensor fields (a). Given the partially scanned scene, a 2D tensor field is computed on-the-fly over the floor plane, constrained by the partial reconstruction (b). The robot is guided by the field with smooth paths, which are locally formed with field advection (red curve in (a)) and globally planned with the help of the field topology (see the curve networks in (b) and (c), with reconstruction uncertainty color-coded along the curves). The topological structure is well-defined for incomplete scenes (b), suited for guiding exploratory reconstruction of unknown scenes. The output is a full 3D reconstruction (d), at which the topology of the field reflects the scene layout (c).Autonomous reconstruction of unknown scenes by a mobile robot inherently poses the question of balancing between exploration efficacy and reconstruction quality. We present a navigation-by-reconstruction approach to address this question, where moving paths of the robot are planned to account for both global efficiency for fast exploration and local smoothness to obtain high-quality scans. An RGB-D camera, attached to the robot arm, is dictated by the desired reconstruction quality as well as the movement of the robot itself. Our key idea is to harness a time-varying tensor field to guide robot movement, and then solve for 3D camera control under the constraint of the 2D robot moving path. The tensor field is updated in real time, conforming to the progressively reconstructed scene. We show that tensor fields are well suited for guiding autonomous scanning for two reasons: first, they contain sparse and controllable singularities that allow generating a locally smooth robot path, and second, their topological structure can be used for globally efficient path routing within a partially reconstructed scene. We have conducted numerous tests with a mobile robot, and demonstrate that our method leads to a smooth exploration and high-quality reconstruction of unknown indoor scenes.

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Path planning with uncertainty: Voronoi Uncertainty Fields

Cited by 39 publications

References 18 publications

Obstacle detection and navigation planning for autonomous micro aerial vehicles

Obstacle detection and navigation planning for autonomous micro aerial vehicles

Path Planning for First Responders in the Presence of Moving Obstacles With Uncertain Boundaries

Autonomous reconstruction of unknown indoor scenes guided by time-varying tensor fields

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