Persistent Coverage Control for Teams of Heterogeneous Agents

Mellone, Alberto; Franzini, Giovanni; Pollini, Lorenzo; Innocenti, Mario

doi:10.1109/cdc.2018.8619469

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…Cooperative coverage with a team of heterogeneous agents has been discussed in [Mel+18]. The authors have introduced a distributed cooperative control strategy that utilises a descriptor function framework and permits flexibility in agent sensing capabilities.…”

Section: Multi-agent Coveragementioning

confidence: 99%

Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field

Ivić¹,

Sikirica²,

Crnković³

2021

Preprint

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Heat Equation Driven Area Coverage (HEDAC) is a state-of-the-art multi-agent ergodic motion control guided by a gradient of a potential field. A finite element method is hereby implemented to obtain a solution of Helmholtz partial di erential equation, which models the potential field for surveying motion control. This allows us to survey arbitrarily shaped domains and to include obstacles in an elegant and robust manner intrinsic to HEDAC's fundamental idea. For a simple kinematic motion, the obstacles and boundary avoidance constraints are successfully handled by directing the agent motion with the gradient of the potential. However, including additional constraints, such as the minimal clearance dsitance from stationary and moving obstacles and the minimal path curvature radius, requires further alternations of the control algorithm. We introduce a relatively simple yet robust approach for handling these constraints by formulating a straightforward optimization problem based on collision-free escapes route maneuvers. This approach provides a guaranteed collision avoidance mechanism, while being computationally inexpensive as a result of the optimization problem partitioning. The proposed motion control is evaluated in three realistic surveying scenarios simulations, showing the e ectiveness of the surveying and the robustness of the control algorithm. Furthermore, potential maneuvering di culties due to improperly defined surveying scenarios are highlighted and we provide guidelines on how to overpass them. The results are promising and indiacate real-world applicability of proposed constrained multi-agent motion control for autonomous surveying and potentially other HEDAC utilizations.

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Section: Multi-agent Coveragementioning

confidence: 99%

Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field

Ivić¹,

Sikirica²,

Crnković³

2021

Preprint

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“…Franco et al [23] proposes a persistent coverage algorithm based on the combination of local and global strategies in order to guarantee full coverage. In the same line of work, Mellone et al [24] provide a distributed control law for persistent coverage of a team of heterogeneous robots in a structured environment, while Zhou et al [25] propose an event-driven solution for one-dimensional persistent monitoring problems. Other solutions for one-dimensional persistent monitoring are proposed in [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A unified Lloyd-based framework for multi-agent collective behaviours

Boldrer

Palopoli

Fontanelli

2022

Robotics and Autonomous Systems

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“…Research work has largely focused on planar surfaces (e.g., for floor cleaning robots) [2], [3], [4]. There are numerous works on multi-robot coverage [5], [6], [7]; however, very few of them consider actual moving obstacles (not just considering other robots as moving obstacles) [8]. There have also been many CPP algorithms applicable to 3D coverage [9], [10], [11], few of which consider multiple robots [12], but moving obstacles are typically not considered.…”

Section: Introductionmentioning

confidence: 99%

Dec-PPCPP: A Decentralized Predator–Prey-based Approach to Adaptive Coverage Path Planning Amid Moving Obstacles

Hassan

Mustafic

Liu

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Enabling multiple robots to collaboratively perform coverage path planning on complex surfaces embedded in R 3 with the presence of moving obstacles is a challenging problem that has not received much attention from researchers. As robots start to be practically deployed, it is becoming important to address this problem. A novel decentralized multirobot coverage path planning approach is proposed that is adaptive to unexpected stationary and moving obstacles while aiming to achieve complete coverage with minimal cost. The approach is inspired by the predator-prey relation. For a robot (a prey), a virtual stationary predator enforces spatial ordering on the prey, and dynamic predators (other robots) cause the prey to be repelled resulting in better task allocation and collision-avoidance. The approach makes the best use of both worlds: offline global planning for tuning of model parameters based on a prior map of the surface, and real-time local planning for adaptive and swift decision making amid moving obstacles and other robots while preserving global behavior. Comparisons with other approaches and extensive testing and validation using different number of robots, different surfaces and obstacles, and various scenarios are conducted.

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Persistent Coverage Control for Teams of Heterogeneous Agents

Cited by 10 publications

References 13 publications

Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field

Constrained multi-agent ergodic area surveying control based on finite element approximation of the potential field

A unified Lloyd-based framework for multi-agent collective behaviours

Dec-PPCPP: A Decentralized Predator–Prey-based Approach to Adaptive Coverage Path Planning Amid Moving Obstacles

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