DisCoverage for non-convex environments with arbitrary obstacles

Haumann, Dominik; Breitenmoser, Andreas; Willert, Volker; Listmann, Kim D.; Siegwart, Roland

doi:10.1109/icra.2011.5980415

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…This algorithm was also combined with the geodesic distance in Bhattacharya et al (2014), together with a graph-based path planning for exploration. Frontier-based exploration of a non-convex environment was treated in Haumann et al (2011). Power diagrams were considered by Thanou et al (2013) for heterogeneous teams.…”

Section: Environment Partitioningmentioning

confidence: 99%

Equitable persistent coverage of non-convex environments with graph-based planning

Palacios-Gasós¹,

Tardioli

Montijano

et al. 2019

The International Journal of Robotics Research

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In this article, we tackle the problem of persistently covering a complex non-convex environment with a team of robots. We consider scenarios where the coverage quality of the environment deteriorates with time, requiring every point to be constantly revisited. As a first step, our solution finds a partition of the environment where the amount of work for each robot, weighted by the importance of each point, is equal. This is achieved using a power diagram and finding an equitable partition through a provably correct distributed control law on the power weights. Compared with other existing partitioning methods, our solution considers a continuous environment formulation with non-convex obstacles. In the second step, each robot computes a graph that gathers sweep-like paths and covers its entire partition. At each planning time, the coverage error at the graph vertices is assigned as weights of the corresponding edges. Then, our solution is capable of efficiently finding the optimal open coverage path through the graph with respect to the coverage error per distance traversed. Simulation and experimental results are presented to support our proposal.

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Section: Environment Partitioningmentioning

confidence: 99%

Equitable persistent coverage of non-convex environments with graph-based planning

Palacios-Gasós¹,

Tardioli

Montijano

et al. 2019

The International Journal of Robotics Research

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“…Similar approach has been presented by Burgard et al 23 In both cases, the cost function produces a new target point on the selected frontier and a path planning-usually shortest path-method is used to guide the robot towards the selected point in the frontier. In Haumann et al 24,25 instead of selecting a frontier, the authors propose a control law, where all candidate frontiers have an impact on the movement and the frontier exploration is coupled directly with the path planning.…”

Section: Introductionmentioning

confidence: 99%

Synergistic exploration and navigation of mobile robots under pose uncertainty in unknown environments

Arvanitakis

Tzes

Giannousakis

2018

International Journal of Advanced Robotic Systems

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Path planning under uncertainty in an unknown environment is an arduous task as the resulting map has inaccuracies and a safe path cannot always be found. A path planning method is proposed in unknown environments towards a known target position and under pose uncertainty. A limited range and limited field of view range sensor is considered and the robot pose can be inferred within certain bounds. Based on the sensor measurements a modified map is created to be used for the exploration and path planning processes, taking into account the uncertainty via the calculation of the guaranteed visibility and guaranteed sensed area, where safe navigation can be ensured regardless of the pose-error. A switching navigation function is used to initially explore the space towards the target position, and afterwards, when the target is discovered to navigate the robot towards it. Simulation results highlighting the efficiency of the proposed scheme are presented.

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“…The robot attempts to move towards the closest frontier to its position [14], referred to as the MinDist approach in the literature. The DisCoverage algorithm [15] utilizes the concept of frontier based approach by selecting appropriate target points along the frontiers for convex environments and then the authors extend it for non-convex environments [16] by transforming the non convex domain to a star shaped domain. The authors in [17], solve the coverage problem on non-Euclidean spaces through the generalization of Lloyd's algorithm, by proper selection of coverage functional, and present the application of this method for exploration.…”

Section: Introductionmentioning

confidence: 99%