An inchworm-inspired motion strategy for robotic swarms

Eshaghi, Kasra; Kashino, Zendai; Yoon, Hyun Joong; Nejat, Goldie; Benhabib, B.

doi:10.1017/s0263574721000321

Cited by 4 publications

(4 citation statements)

References 59 publications

(82 reference statements)

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“…R i , i and can be used to estimate the position of (mapper) Robot R i , when it obtains the distance reading z t R i . Fusing the distance and bearing sensing data between the mapper robots and landmark robots, to obtain an estimate of the mapper robots' positions, follows the approach developed in our previous work [10], and can be summarized as follows. Using both sets of inter-robot sensing data from a data packet received by the central controller, an estimate of mapper Robot R i 's location with respect to the local frame of the landmark robots can be determined.…”

Section: Swarm Localizationmentioning

confidence: 99%

“…Numerous works have categorized common problems and applications specific to SRS [1][2][3][4][5]. Behaviors of SRSs have been, commonly, investigated using ground-based robots, including millimeter-scale robots known as millirobots [6][7][8][9][10][11]. As well, strategies have also been specifically developed for swarms comprising aerial vehicles [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy

et al. 2023

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This paper addresses the problem of building an occupancy grid map of an unknown environment using a swarm comprising resource-constrained robots, i.e., robots with limited exteroceptive and inter-robot sensing capabilities. Past approaches have, commonly, used random-motion models to disperse the swarm and explore the environment randomly, which do not necessarily consider prior information already contained in the map. Herein, we present a collaborative, effective exploration strategy that directs the swarm toward ‘promising’ frontiers by dividing the swarm into two teams: landmark robots and mapper robots, respectively. The former direct the latter, toward promising frontiers, to collect proximity measurements to be incorporated into the map. The positions of the landmark robots are optimized to maximize new information added to the map while also adhering to connectivity constraints. The proposed strategy is novel as it decouples the problem of directing the resource-constrained swarm from the problem of mapping to build an occupancy grid map. The performance of the proposed strategy was validated through extensive simulated experiments.

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Section: Swarm Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy

et al. 2023

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“…Leader-based motion control strategies [74][75][76][77] use leader robots that are equipped with enhanced localization technology, such as access to ground positioning systems (GPS) or complex onboard sensors. These leaders operate as support robots, and facilitate the movement of the remaining follower (worker) robots, that localize by fusing their measured distance to the leader, and the position of the leader [78][79][80]. This constrains the motion of the followers as they need to wait to be 'picked up' and accompanied to their destinations by a leader.…”

Section: Localization Limitationsmentioning

confidence: 99%

A Concurrent Mission-Planning Methodology for Robotic Swarms Using Collaborative Motion-Control Strategies

Eshaghi

Nejat

Benhabib

2023

J Intell Robot Syst

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Swarm robotic systems comprising members with limited onboard localization capabilities rely on employing collaborative motion-control strategies to successfully carry out multi-task missions. Such strategies impose constraints on the trajectories of the swarm and require the swarm to be divided into worker robots that accomplish the tasks at hand, and support robots that facilitate the movement of the worker robots. The consideration of the constraints imposed by these strategies is essential for optimal mission-planning. Existing works have focused on swarms that use leader-based collaborative motion-control strategies for mission execution and are divided into worker and support robots prior to mission-planning. These works optimize the plan of the worker robots and, then, use a rule-based approach to select the plan of the support robots for movement facilitation – resulting in a sub-optimal plan for the swarm. Herein, we present a mission-planning methodology that concurrently optimizes the plan of the worker and support robots by dividing the mission-planning problem into five stages: division-of-labor, task-allocation of worker robots, worker robot path-planning, movement-concurrency, and movement-allocation. The proposed methodology concurrently searches for the optimal value of the variables of all stages. The proposed methodology is novel as it (1) incorporates the division-of-labor of the swarm into worker and support robots into the mission-planning problem, (2) plans the paths of the swarm robots to allow for concurrent facilitation of multiple independent worker robot group movements, and (3) is applicable to any collaborative swarm motion-control strategy that utilizes support robots. A unique pre-implementation estimator, for determining the possible improvement in mission execution performance that can achieved through the proposed methodology was also developed to allow the user to justify the additional computational resources required by it. The estimator uses a machine learning model and estimates this improvement based on the parameters of the mission at hand. Extensive simulated experiments showed that the proposed concurrent methodology improves the mission execution performance of the swarm by almost 40% compared to the competing sequential methodology that optimizes the plan of the worker robots first and, then, the plan of the support robots. The developed pre-implementation estimator was shown to achieve an estimation error of less than 5%.

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“…Swarm robotic systems (SRSs) represent teams of large number of robots that collaborate to accomplish complex tasks [1][2][3][4] such as environmental monitoring [5], collective perception [6], and exploration [7]. Collaboration is, typically, achieved through exchange of information amongst the member robots as well as between the robots and possible external infrastructure via wireless communication devices and/or onboard sensors [8][9][10][11][12][13][14][15]. In order to achieve effective communication, however, swarm members must maintain a desired degree of connectivity, which specifies for each member what other teammates and/or external infrastructure it should be able to communicate with (e.g., [16]).…”

Section: Introductionmentioning

confidence: 99%

Restoring Connectivity in Robotic Swarms – A Probabilistic Approach

Eshaghi,

Sari,

Haigh

et al. 2024

J Intell Robot Syst

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Connectivity is an integral trait for swarm robotic systems to enable effective collaboration between the robots in the swarm. However, connectivity can be lost due to events that could not have been a priori accounted for. This paper presents a novel probabilistic connectivity-restoration strategy for swarms with limited communication capabilities. Namely, it is assumed that the swarm comprises a group of follower robots whose global connectivity to a base can only be achieved via a localized leader robot. In this context, the proposed strategy incrementally restores swarm connectivity by searching for the lost robots in regions-of-interest (RoIs) determined using probability theory. Once detected, newly found robots are either recruited to help the leader in the restoration process, or directly guided to their respective destinations through accurate localization and corrective motion commands. The proposed swarm-connectivity strategy, thus, comprises the following three stages: (i) identifying a discrete set of optimal RoIs, (ii) visitation of these RoIs, by the leader robot, via an optimal inter-region search path, and (iii) searching for lost robots within the individual RoIs via an optimal intra-region search path. The strategy is novel in its use of a probabilistic approach to guide the leader robot’s search as well as the potential recruitment of detected lost robots to help in the restoration process. The effectiveness of the proposed probabilistic swarm connectivity-restoration strategy is represented, herein, through a detailed simulated experiment. The significant efficiency of the strategy is also illustrated numerically via a comparison to a competing random-walk based method.

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An inchworm-inspired motion strategy for robotic swarms

Cited by 4 publications

References 59 publications

Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy

Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy

A Concurrent Mission-Planning Methodology for Robotic Swarms Using Collaborative Motion-Control Strategies

Restoring Connectivity in Robotic Swarms – A Probabilistic Approach

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