Optimal path planning and coverage control for multi-robot persistent coverage in environments with obstacles

Palacios-Gasós, José Manuel; Talebpour, Zeynab; Montijano, Eduardo; Sagüés, Carlos; Martinoli, Alcherio

doi:10.1109/icra.2017.7989156

Cited by 21 publications

(27 citation statements)

References 19 publications

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“…More recent proposals enable continuous adaptive coverage of changing environments (Schwager et al 2017) possibly containing obstacles (Palacios-Gass et al 2017). Furthermore, these systems can be extended to take into account both energy (Derenick et al 2011) and communication constraints (Orfanus et al 2016;Wang et al 2016).…”

Section: System Architecturementioning

confidence: 99%

“…e Coarse Grain Context Detector leverages established results in target detection and tracking using xed cameras, e.g., visual sensors for pedestrian tracking (Chen and Odobez 2012), or other contextual sensors, e.g., device-free RF-based techniques (Adib et al 2014). In addition, recent advances in persistent coverage (Palacios-Gass et al 2017;Schwager et al 2017) greatly extend the range of application contexts where such information can be robustly collected within complex dynamic environments. e OLS model is essentially proposed to obtain close-up views of relevant targets using mobile cameras and provide ne grain surveillance as needed.…”

Section: Modeling Assumptionsmentioning

confidence: 99%

See 1 more Smart Citation

On Realistic Target Coverage by Autonomous Drones

Ahmed

Abdelkader

Khan

et al. 2019

ACM Trans. Sen. Netw.

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Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of such drones, it is necessary to develop new enhanced formulations of both common and emerging sensing scenarios.Namely, several fundamental challenges in visual sensing remain unsolved including: 1) Fi ing sizable targets in camera frames; 2) E ective viewpoints matching target poses; 3) Occlusion by elements in the environment, including other targets. In this paper, we introduce Argus: an autonomous system that utilizes drones to incrementally collect target information through a two-tier architecture.To tackle the stated challenges, Argus employs a novel geometric model that captures both target shapes and coverage constraints.Recognizing drones as the scarcest resource, Argus aims to minimize the number of drones required to cover a set of targets. We prove this problem is NP-hard, and even hard to approximate, before deriving a best-possible approximation algorithm along with a competitive sampling heuristic which runs up to 100x faster according to large-scale simulations. To test Argus in action, we demonstrate and analyze its performance on a prototype implementation. Finally, we present a number of extensions to accommodate more application requirements and highlight some open problems.

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Section: System Architecturementioning

confidence: 99%

Section: Modeling Assumptionsmentioning

confidence: 99%

On Realistic Target Coverage by Autonomous Drones

Ahmed

Abdelkader

Khan

et al. 2019

ACM Trans. Sen. Netw.

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“…Specifically, [20] achieves collision avoidance through a bounded repulsive avoidance control law. [21] keeps robots at a safety distance from obstacles through speed functions. However, such methods of avoiding obstacles are not applicable in persistent monitoring.…”

Section: Introductionmentioning

confidence: 99%

Collision-Free Trajectory Design for 2-D Persistent Monitoring Using Second-Order Agents

Wang

Zhao

Yang

et al. 2020

IEEE Trans. Control Netw. Syst.

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This paper considers a two-dimensional persistent monitoring problem by controlling movements of second-order agents to minimize some uncertainty metric associated with targets in a dynamic environment. In contrast to common sensing models depending only on the distance from a target, we introduce an active sensing model which considers the distance between an agent and a target, as well as the agent's velocity. We propose an objective function which can achieve a collision-free agent trajectory by penalizing all possible collisions. Applying structural properties of the optimal control derived from the Hamiltonian analysis, we limit agent trajectories to a simpler parametric form under a family of 2D curves depending on the problem setting, e.g. ellipses and Fourier trajectories. Our collision-free trajectories are optimized through an event-driven Infinitesimal Perturbation Analysis (IPA) and gradient descent method. Although the solution is generally locally optimal, this method is computationally efficient and offers an alternative to other traditional time-driven methods. Finally, simulation examples are provided to demonstrate our proposed results.

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“…Solutions based on waypoint guidance have been proposed for agents with singleintegrator [4], [5] and unicycle kinematics [6]. Path planning techniques are proposed in [7], [8]. In the former reference, the authors discuss the geometric design of paths aimed at performing the task, that the agents must follow, whereas in [8] the single-integrator kinematics of the agents is explicitly taken into account in the planning.…”

mentioning

confidence: 99%

“…Path planning techniques are proposed in [7], [8]. In the former reference, the authors discuss the geometric design of paths aimed at performing the task, that the agents must follow, whereas in [8] the single-integrator kinematics of the agents is explicitly taken into account in the planning. A gradient based law is instead proposed in [9] for a team of singleintegrator agents.…”

mentioning

confidence: 99%

Persistent Coverage Control for Teams of Heterogeneous Agents

Mellone

Franzini

Pollini

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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A distributed cooperative control law for persistent coverage tasks is proposed, capable of coordinating a team of heterogeneous agents in a structured environment. Team heterogeneity is considered both at vehicles' dynamics and at coverage capabilities levels. More specifically, the general dynamics of nonholonomic vehicles are considered. Agent heterogeneous sensing capabilities are addressed by means of the descriptor function framework, a set of analytical tools for controlling agents involved in generic coverage tasks. By means of formal arguments, we prove that the team performs the task and no collision occurs between agents nor with obstacles. A numerical simulation validates the proposed strategy.

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Optimal path planning and coverage control for multi-robot persistent coverage in environments with obstacles

Cited by 21 publications

References 19 publications

On Realistic Target Coverage by Autonomous Drones

On Realistic Target Coverage by Autonomous Drones

Collision-Free Trajectory Design for 2-D Persistent Monitoring Using Second-Order Agents

Persistent Coverage Control for Teams of Heterogeneous Agents

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