Information Exchange Design Patterns for Robot Swarm Foraging and Their Application in Robot Control Algorithms

Pitonakova, Lenka; Crowder, Richard; Bullock, Seth

doi:10.3389/frobt.2018.00047

Cited by 10 publications

(7 citation statements)

References 75 publications

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“…Direct sharing of information between neighboring robots is an enabler for swarm behaviors as well as relative sensing (Valentini, 2017 ; Hamann, 2018 ; Pitonakova et al, 2018 ). To achieve the desired effect, it needs to be implemented with scalability, robustness, and flexibility in mind.…”

Section: Intra-swarm Relative Sensing and Collision Avoidancementioning

confidence: 99%

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.

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Section: Intra-swarm Relative Sensing and Collision Avoidancementioning

confidence: 99%

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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“…A number of mechanisms for information exchange in foraging, that are a fundamental and highly reusable element of MRSs, have been proposed in Pitonakova et al ( 2018 ). The need of distinguish between individual knowledge and external repositories of information, such as the environment, has also been stressed in Pitonakova et al ( 2017 ).…”

Section: Other Workmentioning

confidence: 99%

Toward Formal Models and Languages for Verifiable Multi-Robot Systems

2018

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Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation.We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour.

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“…For a system to effectively carry out its assigned task in a fast-moving dynamic environment, there must be some form of adjustment of the balance between exploratory and exploitative actions throughout the duration of the task. Should an MAS bias its actions towards exploitation over time, such exploitative activity based on outdated information will inevitably result in poorer system performances (Jordehi, 2014;Pitonakova et al, 2018;Phan et al, 2020). Despite this, there is still merit in studying the methods used to control the level of exploration and exploitation carried out in MAS operating in static and quasi-static environments.…”

Section: Introductionmentioning

confidence: 99%

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

2022

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Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration–exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration–exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one’s ability to understand and manage the intricacies of the exploration–exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an multi-agent systems. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control algorithm.

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Information Exchange Design Patterns for Robot Swarm Foraging and Their Application in Robot Control Algorithms

Cited by 10 publications

References 75 publications

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

Toward Formal Models and Languages for Verifiable Multi-Robot Systems

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

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