A high-performance millirobot for swarm-behaviour studies: Swarm-topology estimation

Kim, Justin Y.; Kashino, Zendai; Pineros, Laura Marcela; Bayat, Sayeh; Colaco, Tyler; Nejat, Goldie; Benhabib, B.

doi:10.1177/1729881419892127

Cited by 9 publications

(12 citation statements)

References 82 publications

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“…where ρ ij and ϕ ij are, respectively, the distance and bearing of Robot j, with respect to Robot i, as observed by Robot i. Herein, it is proposed to fuse the inter-robot proximity measurements using a modified version of a swarm topology estimation approach previously developed in our lab at the University of Toronto [49]. In this approach, data fusion is achieved by clustering all observations of individual robots and calculating respective centroid positions.…”

Section: Phase 12: Swarm Topology Estimationmentioning

confidence: 99%

“…Herein, we consider an alternate formulation to the swarm topology estimation problem, developed in response to the computational demands of our previous approach [49].…”

Section: Appendix Amentioning

confidence: 99%

“…Approaches to swarm localization, mainly, focus on estimating the relative distances between robots [8,9,10,38,39], or estimating the changes to the swarm's formation using sample statistics from the swarm [40,41,42,43]. Methods of estimating the positions and orientations (poses) of all robots in the swarm by fusing sensing data and motion commands [44,45,46,47,48,49], and methods that propose the use of external infrastructure, such as stationary beacons [50,51,52,53] (e.g., GPS, NorthStar) and computer vision systems [54,55,56] (e.g., AprilTag, WhyCon, and OptiTrack) have also been suggested in the literature. The latter ones tend to be more accurate, as the use of external infrastructure would yield the same level of uncertainty independent of traveled distance.…”

Section: Introductionmentioning

confidence: 99%

“…Namely, it can localize the swarm with partial information acquired from robots regarding their respective neighbors, as first discussed in our work reported in ref. [49].…”

Section: Introductionmentioning

confidence: 99%

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An inchworm-inspired motion strategy for robotic swarms

et al. 2021

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Effective motion planning and localization are necessary tasks for swarm robotic systems to maintain a desired formation while maneuvering. Herein, we present an inchworm-inspired strategy that addresses both these tasks concurrently using anchor robots. The proposed strategy is novel as, by dynamically and optimally selecting the anchor robots, it allows the swarm to maximize its localization performance while also considering secondary objectives, such as the swarm’s speed. A complementary novel method for swarm localization, that fuses inter-robot proximity measurements and motion commands, is also presented. Numerous simulated and physical experiments are included to illustrate our contributions.

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Section: Phase 12: Swarm Topology Estimationmentioning

confidence: 99%

“…Herein, we consider an alternate formulation to the swarm topology estimation problem, developed in response to the computational demands of our previous approach [49].…”

Section: Appendix Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Namely, it can localize the swarm with partial information acquired from robots regarding their respective neighbors, as first discussed in our work reported in ref. [49].…”

Section: Introductionmentioning

confidence: 99%

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An inchworm-inspired motion strategy for robotic swarms

et al. 2021

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“…Collaborative distributed control of multirobot systems, such as swarm, formation, and collective missions, have raised widespread attention in the past decade. [1][2][3][4][5][6][7] Consensus is the basic theory in multirobot coordination control, which is often done by designing a distributed consensus protocol to make a set of robots to agree on a common state. [8][9][10][11][12] Target tracking is a typical application of multirobot systems, in which the robots' states reach consensus with the target.…”

Section: Introductionmentioning

confidence: 99%

Adaptive sliding mode control for disturbed multirobot systems performing target tracking under continuously time-varying topologies

Dong

Han

2020

International Journal of Advanced Robotic Systems

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This article studies coordination control of the multirobot system for target tracking problem with disturbances in time-varying communication environment. An adaptive sliding mode control is designed to complete the target tracking task with the effect of disturbances and continuously time-varying topologies. This means that the communication between the robots is not switching among constant topologies but changes continuously along with time. The continuously time-varying topology is constructed by a set of fixed Laplacian matrices and variable scheduling functions, which is known as polytopic model structure. The designed adaptive sliding mode control can effectively lower the influence of disturbance and improve tracking performance even under the continuously time-varying topology. Furthermore, numerical simulations are given to demonstrate the effectiveness of the obtained results.

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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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A high-performance millirobot for swarm-behaviour studies: Swarm-topology estimation

Cited by 9 publications

References 82 publications

An inchworm-inspired motion strategy for robotic swarms

An inchworm-inspired motion strategy for robotic swarms

Adaptive sliding mode control for disturbed multirobot systems performing target tracking under continuously time-varying topologies

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

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