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Title
Abstract
Introduction
Swarm Robotics-An Overview
Objective Metric to Optimise
Comparative Analysis
Conclusions and Future Research
References
All Topics
Engineering
Control and Systems Engineering

Search and tracking algorithms for swarms of robots: A survey

Profile image of Jan Carlo BarcaJan Carlo Barca

2016, Robotics and Autonomous Systems

https://doi.org/10.1016/J.ROBOT.2015.08.010
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Abstract

h i g h l i g h t s • Surveys algorithms applicable to swarm robotic systems for target search and tracking. • Identifies variations of the search and tracking problem addressed in the literature. • Discusses desired capabilities of search and tracking algorithms for robot swarms.

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2021

Target searching is a well-known but difficult problem in many research domains, including computational intelligence, swarm intelligence, and robotics. The main goal is to search for the targets within the specific boundary with the minimum time that is required and the obstacle avoidance that has been equipped in place. Swarm robotics (SR) is an extension of the multi-robot system that particularly discovers a concept of coordination, collaboration, and communication among a large number of robots. Because the robots are collaborating and working together, the task that is given will be completed faster compared to using a single robot. Thus, searching for single or multiple targets with swarm robots is a significant and realistic approach. Robustness, flexibility, and scalability, which are supported by distributed sensing, also make the swarm robots strategy suitable for target searching problems in real-world applications. The purpose of this article is to deliver a systematic ...

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Comparison of the Behavior of Swarm Robots with their Computer Simulations Applying Target-Searching Algorithms
Thomas Hanne

International Journal of Mechanical Engineering and Robotics Research, 2018

This paper investigates the functionality and quality of the implementation of a search-and targetsurrounding swarm robotic algorithm using physical swarm robots named Kilobots. The implementation was developed and tested in the simulator V-REP, then transferred onto the actually running Kilobots: Ten Kilobots were used for the experiment, where one Kilobot acts as the target and nine Kilobots act as the searchers. The algorithm allows the searchers to swarm out to find the target while avoiding collisions with other searchers, to orbit around other searchers, which are closer to the target, and finally to surround the target once it is found. The results of the implementation using the physical Kilobots are compared with the results of two adjusted computer simulations. Differences between the simulations and the real robot implementation are investigated: Discrepancies regarding the locomotion and the communication capabilities are identified and discussed.

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Effects of algorithmic parameters on swarm robotic search
Jeng-shyang Pan

The 2010 IEEE International Conference on Information and Automation, 2010

As one of valid modeling and coordinated controlling tools, the particle swarm optimization algorithm can be extended for applying to task of swarm robotic search. To gain an insight into effects of key parameters on distributed swarm search, a series of simulations are conducted. In such control algorithm, main parameters including communication range, detection radius, and swarm size are paid close attentions. Similar to ideal "particles", swarm robots are modeled at an abstract level with the extended particle swarm optimization method. Also, individual robots are designed to be controlled under three-state finite state machine mechanism having SingleSearch, SwarmSearch, and Declaration states. Then, control strategy and algorithm are developed. The statistical results from simulating data indicate the validity of modeling approach. Further, the effects of three parameters on overhead and efficiency of system running come out in compared analysis both of different size swarms and of specific size swarm.

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Swarm Intelligence for Collective Robotic Search
Kumar Venayagamoorthy

Studies in Computational Intelligence, 2009

This chapter presents three strategies for the navigation of a swarm of robots for a target search application in a hazardous environment. The strategies explored include greedy search and two computational intelligence techniques-particle swarm optimization and fuzzy logic. Results for the collective search are presented for simulated environments containing single and multiple targets, with and without obstacles. The proposed navigation strategies can be further developed and applied to real-world applications such as aiding in disaster recovery, detection of hazardous materials, and many other high-risk tasks.

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A New Multi-Robot Search Algorithm Using Probabilistic Finite State Machine and Lennard-Jones Potential Function
Tarem Ahmed

2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018

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Related topics

  • Mechanical Engineering
  • Robotics
  • Computer Science
  • Multi Robot Systems
  • Electrical and Electronic Engine...

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    Collective expression: how robotic swarms convey information with group motion
    florent levillain

    Paladyn, Journal of Behavioral Robotics, 2019

    When faced with the need of implementing a decentralized behavior for a group of collaborating robots, strategies inspired from swarm intelligence often avoid considering the human operator, granting the swarm with full autonomy. However, field missions require at least to share the output of the swarm to the operator. Unfortunately, little is known about the users' perception of group behavior and dynamics, and there is no clear optimal interaction modality for swarms. In this paper, we focus on the movement of the swarm to convey information to a user: we believe that the interpretation of artificial states based on groups motion can lead to promising natural interaction modalities. We implement a grammar of decentralized control algorithms to explore their expressivity. We define the expressivity of a movement as a metric to measure how natural, readable, or easily understandable it may appear. We then correlate expressivity with the control parameters for the distributed behavior of the swarm. A first user study confirms the relationship between inter-robot distance, temporal and spatial synchronicity, and the perceived expressivity of the robotic system. We follow up with a small group of users tasked with the design of expressive motion sequences to convey internal states using our grammar of algorithms. We comment on their design choices and we assess the interpretation performance by a larger group of users. We show that some of the internal states were perceived as designed and discuss the parameters influencing the performance.

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    Metareasoning Structures, Problems, and Modes for Multiagent Systems: A Survey
    Samuel Langlois

    IEEE Access, 2020

    Autonomous multiagent systems can be used in different domains such as agriculture, search and rescue, and fire protection because they can accomplish large missions more quickly and robustly by dividing them into separate tasks. Using multiple agents introduces additional complexity, which makes autonomous reasoning and decision making more challenging, however. Because agents such as ground robots, unmanned air vehicles, and autonomous underwater vehicles may have limited computational resources, they may need computationally efficient yet powerful reasoning algorithms (decision-making processes that perform deliberation and means-end reasoning). Metareasoning, which is reasoning about these reasoning algorithms, offers a way to tackle these challenges by monitoring and controlling reasoning algorithms to improve agent and system performance. Although metareasoning approaches for individual computational agents have been studied, no survey of metareasoning in multiagent systems (M...

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    Cooperative Multi-robot Target Searching and Tracking Using Velocity Inspired Robotic Fruit Fly Algorithm
    vikram garg

    SN Computer Science

    Target searching and tracking using a swarm of robots is a classical problem in the domain of robotics. The cooperation among the swarm robots has got increasing attention lately due to the different versions of the problem and complex environment. In this paper, a centralized control algorithm is proposed which utilizes cooperation among the swarm of robots for searching and tracking targets which is the velocity inspired robotic fruit fly algorithm (VRFA). The particle velocity concept of the particle swarm optimization is added to the fruit fly algorithm to improve the parameters such as local extremum and low convergence. The simulation results of the proposed technique in different scenarios demonstrate the effectiveness of the algorithm and its ability to keep tracking the targets until the exit condition matched. At last, the simulation result is shown with different environments and parameter settings.

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    Particle Swarm Optimization—An Adaptation for the Control of Robotic Swarms
    George Rossides

    Robotics, 2021

    Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within a multidimensional space. The particles explore the space in an attempt to find minima or maxima to the optimization problem. The motion of the particles is linked, and the overall behavior of the particle swarm is controlled by several parameters. PSO has been proposed as a control strategy for physical swarms of robots that are localizing a source; the robots are analogous to the virtual particles. However, previous attempts to achieve this have shown that there are inherent problems. This paper addresses these problems by introducing a modified version of PSO, as well as introducing new guidelines for parameter selection. The proposed algorithm links the parameters to the velocity and acceleration of each robot, and demonstrates obstacle avoidance. Simulation results from both MATLAB and Gazebo show close agreement and demonstrate that the proposed algorithm is ...

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    Source Localisation Using Wavefield Correlation-Enhanced Particle Swarm Optimisation
    George Rossides

    Robotics

    Particle swarm optimisation (PSO) is a swarm intelligence algorithm used for controlling robotic swarms in applications such as source localisation. However, conventional PSO algorithms consider only the intensity of the received signal. Wavefield signals, such as propagating underwater acoustic waves, permit the measurement of higher order statistics that can be used to provide additional information about the location of the source and thus improve overall swarm performance. Wavefield correlation techniques that make use of such information are already used in multi-element hydrophone array systems for the localisation of underwater marine sources. Additionally, the simplest model of a multi-element array (a two-element array) is characterised by operational simplicity and low-cost, which matches the ethos of robotic swarms. Thus, in this paper, three novel approaches are introduced that enable PSO to consider the higher order statistics available in wavefield measurements. In sim...

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