Vector Field Based Formation Control of Multi-Robot System

M, Sruthi; Rao, K. Koteswara; Jisha, V. R.

doi:10.1016/j.ifacol.2016.03.051

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Several optimization and machine learning techniques were proposed such as GA [90], convolutional neural network, graph neural network algorithm [91], datagram congestion control protocol, and control barrier and Lyapunov function [92]. Potential field-based techniques were implemented to a number of works with different algorithms such as vector field algorithm [93, 98], switched local potentials [96], and multi-stencils FM technique [97] to deal with the path planning task. The main objective in ref.…”

Section: Decision-making-based Path Planningmentioning

confidence: 99%

“…[92] DCCP and CBF-CLF. [93], [94], [95], [96], [97], [98] 2016, 2017,2020,2005,2016,2006 Potential field [93,96] Both, [94,95] Sim, and [97,98] Exp [93] Vector field method.…”

Section: Decentralized Decision Path Planningmentioning

confidence: 99%

“…[143] proposed an optimization path planning algorithm called the online optimization perceptual strategy. On the other hand, different decentralized decision path planning techniques have been applied to UGV systems, including optimization and machine learning [90][91][92], potential field [93][94][95][96][97][98], cellular automaton (CA) method [99,100], hybrid algorithms [101,102], sampling-based [103], and other techniques [104][105][106].…”

Section: 2014mentioning

confidence: 99%

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A comprehensive review of the latest path planning developments for multi-robot formation systems

et al. 2023

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There has been a continuous interest in multi-robot formation systems in the last few years due to several significant advantages such as robustness, scalability, and efficiency. However, multi-robot formation systems suffer from well-known problems such as energy consumption, processing speed, and security. Therefore, developers are continuously researching for optimal solutions that can gather the benefits of multi-robot formation systems while overcoming the possible challenges. A backbone process required by any multi-robot system is path planning. Thus, path planning for multi-robot systems is a recent top research topic. However, the literature lacks a recent comprehensive review of path planning works designed for multi-robot systems. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot formation systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot formation systems. Finally, an illustrative comparative example is presented at the end of the paper to show the advantages and disadvantages of some popular path planning techniques.

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Section: Decision-making-based Path Planningmentioning

confidence: 99%

“…[92] DCCP and CBF-CLF. [93], [94], [95], [96], [97], [98] 2016, 2017,2020,2005,2016,2006 Potential field [93,96] Both, [94,95] Sim, and [97,98] Exp [93] Vector field method.…”

Section: Decentralized Decision Path Planningmentioning

confidence: 99%

Section: 2014mentioning

confidence: 99%

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A comprehensive review of the latest path planning developments for multi-robot formation systems

et al. 2023

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“…For the problem of multirobot formation and path planning, Chen et al [10] proposed a new leader-following control framework by introducing the RH method, which enables fast convergence of a formation task for a group of mobile robots. Based on the path planning of a single robot, Sruthi et al [11] designed a nonlinear controller for tracking to achieve the multirobot formation. The above two methods require rigorous modeling of the system and cumbersome theory, which are weak in the application.…”

Section: Introductionmentioning

confidence: 99%

ImprovedQ‐Learning Method for Multirobot Formation and Path Planning with Concave Obstacles

et al. 2021

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Aiming at the formation and path planning of multirobot systems in an unknown environment, a path planning method for multirobot formation based on improved Q -learning is proposed. Based on the leader-following approach, the leader robot uses an improved Q -learning algorithm to plan the path and the follower robot achieves a tracking strategy of gravitational potential field (GPF) by designing a cost function to select actions. Specifically, to improve the Q-learning, Q -value is initialized by environmental guidance of the target’s GPF. Then, the virtual obstacle-filling avoidance strategy is presented to fill non-obstacles which is judged to tend to concave obstacles with virtual obstacles. Besides, the simulated annealing (SA) algorithm whose controlling temperature is adjusted in real time according to the learning situation of the Q -learning is applied to improve the action selection strategy. The experimental results show that the improved Q -learning algorithm reduces the convergence time by 89.9% and the number of convergence rounds by 63.4% compared with the traditional algorithm. With the help of the method, multiple robots have a clear division of labor and quickly plan a globally optimized formation path in a completely unknown environment.

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Event-triggered control for sampled-data cluster formation of multi-agent systems

et al. 2017

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Vector Field Based Formation Control of Multi-Robot System

Cited by 10 publications

References 6 publications

A comprehensive review of the latest path planning developments for multi-robot formation systems

A comprehensive review of the latest path planning developments for multi-robot formation systems

ImprovedQ‐Learning Method for Multirobot Formation and Path Planning with Concave Obstacles

Event-triggered control for sampled-data cluster formation of multi-agent systems

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