Frontier-led swarming: Robust multi-robot coverage of unknown environments

Tran, Vu Phi; Garratt, Matthew A.; Kasmarik, Kathryn; Anavatti, Sreenatha G.; Abpeikar, Shadi

doi:10.1016/j.swevo.2022.101171

Cited by 18 publications

(11 citation statements)

References 35 publications

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“…One class of approach assumes each robot will cover an independent region of the environment. More recently, there has been interest in swarming approaches where robots stay together so that there is redundancy if some robots fail (Tran et al, 2022 ).…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…To automatically generate collective behavior for robots, Toshiyuki et al ( 2016 ) developed a self-organized flocking behavior for leader and follower movement of two-wheeled robots. Tran et al ( 2022 ) provided a frontier-led swarming behavior for multi-robot coverage problems. Firat et al ( 2020 ) proposed self-organized aggregation using informed robots.…”

Section: Introductionmentioning

confidence: 99%

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Iterative transfer learning for automatic collective motion tuning on multiple robot platforms

2023

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This paper proposes an iterative transfer learning approach to achieve swarming collective motion in groups of mobile robots. By applying transfer learning, a deep learner capable of recognizing swarming collective motion can use its knowledge to tune stable collective motion behaviors across multiple robot platforms. The transfer learner requires only a small set of initial training data from each robot platform, and this data can be collected from random movements. The transfer learner then progressively updates its own knowledge base with an iterative approach. This transfer learning eliminates the cost of extensive training data collection and the risk of trial-and-error learning on robot hardware. We test this approach on two robot platforms: simulated Pioneer 3DX robots and real Sphero BOLT robots. The transfer learning approach enables both platforms to automatically tune stable collective behaviors. Using the knowledge-base library the tuning procedure is fast and accurate. We demonstrate that these tuned behaviors can be used for typical multi-robot tasks such as coverage, even though they are not specifically designed for coverage tasks.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative transfer learning for automatic collective motion tuning on multiple robot platforms

2023

Self Cite

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“…In Section IV.B, we conduct a series of comparative experiments using our novel coverage path planning with formation control (CPPF), the coverage path planning with swarming, and the frontier-led swarming [30] on simulated Jackal UGVs. These experiments investigate the impact of UGV speed and time budget on path following performance while maintaining the group and order of the UGV formation.…”

Section: Methodsmentioning

confidence: 99%

“…The leader-follower approach also allows the vehicles to adapt to changes in the environment or mission requirements, which is not possible with fixed area allocation. This approach also enables the formation to be more robust to failures or disturbances, as the leader can quickly adjust the formation to account for the loss of a vehicle, making the system more resilient [30].…”

Section: Literature Reviewmentioning

confidence: 99%

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Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

Tran

Garratt

Kasmarik

et al. 2023

IEEE/CAA J. Autom. Sinica

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This paper proposes an innovative approach to coverage path planning and obstacle avoidance for multiple Unmanned Ground Vehicles (UGVs) in a changing environment, taking into account constraints on the time, path length, number of UGVs and obstacles. Our approach leverages deformable virtual leader-follower formations to enable UGVs to adapt their formation based on both planned and real-time sensor data. A hierarchical block algorithm is employed to identify areas in the environment where UGV formations can spread out to meet time and budget constraints. Additionally, we introduce a novel control scheme that allows each UGV to generate a local steering force to dodge any static and mobile obstacles based on the closest safe angle. Results from simulations and real UGV experiments demonstrate that our approach achieves a higher coverage percentage than rule-based and reactive swarming approaches without planning. Our approach offers a promising solution for efficient coverage path planning and obstacle avoidance in complex environments with multiple UGVs.

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An introductory review of swarm technology for spacecraft on‐orbit servicing

Asri,

Zhu

2024

Int Journal of Mech Sys Dyn

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This review paper presents a comprehensive evaluation and forward‐looking perspective on the underexplored topic of servicing target objects using spacecraft swarms. Such targets can be known or unknown, cooperative or uncooperative, and pose significant challenges in modern space operations due to their inherent complexity and unpredictability. Successfully servicing space objects is vital for active debris removal and broader on‐orbit servicing tasks such as satellite maintenance, repair, refueling, orbital assembly, and construction. Significant effort has been invested in the literature to explore the servicing of targets using a single spacecraft. Given its advantages and benefits, this paper expands the discussion to encompass a swarm approach to the problem. This review covers various single‐spacecraft approaches and presents a critical examination of the existing, although limited, body of work dedicated to servicing orbital objects using multiple spacecraft. The focus is also broadened to include some influential studies concerning the characterization, capture, and manipulation of physical objects by general multiagent systems, a subject with significant parallels to the core interest of this manuscript. Furthermore, this article also delves into the realm of simultaneous localization and mapping, highlighting its application within close‐proximity operations in space, especially when dealing with unknown uncooperative targets. Special attention is paid to the benefits that this field can receive from distributed multiagent architectures. Finally, an exploration of the promising field of swarm robotics is presented, with an emphasis on its potential to revolutionize the servicing of orbital target objects. Concurrently, a survey of general research directly engaging swarms in the orbital context is conducted. This review aims to bridge the knowledge gap and stimulate further research in the underexplored domain of servicing space targets with spacecraft swarms.

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Frontier-led swarming: Robust multi-robot coverage of unknown environments

Cited by 18 publications

References 35 publications

Iterative transfer learning for automatic collective motion tuning on multiple robot platforms

Iterative transfer learning for automatic collective motion tuning on multiple robot platforms

Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

An introductory review of swarm technology for spacecraft on‐orbit servicing

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