Cooperative multi-agent search using Bayesian approach with connectivity maintenance

Xiao, Hu; Cui, Rongxin; Xu, Dehong

doi:10.1108/aa-10-2018-0152

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…Engaging a Bayesian approach into target search problems has been investigated by several authors [5], [21], [36]. In these papers, various features of the search are considered such as static or mobile target search, different search vehicle kinematics and sensor detection models.…”

Section: Other Approaches and Methodsmentioning

confidence: 99%

Motion control for autonomous heterogeneous multi-agent area search in uncertain conditions

Ivić

2019

Preprint

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Using multiple mobile robots in search missions offers a lot of benefits, but one needs a suitable and competent motion control algorithm which is able to consider sensors characteristics, the uncertainty of target detection and complexity of needed maneuvers in order to make a multi-agent search autonomous. This paper provides a methodology for an autonomous two-dimensional search using multiple unmanned search agents. The proposed methodology relies on an accurate calculation of target occurrence probability distribution based on the initial estimated target distribution and continuous action of spatial variant search agent sensors. The core of the autonomous search process is a high-level motion control for multiple search agents which utilizes the probabilistic model of target occurrence via Heat Equation Driven Area Coverage (HEDAC) method. This centralized motion control algorithm is tailored for handling a group of search agents which are heterogeneous in both motion and sensing characteristics. The motion of agents is directed by the gradient of the potential field which provides nearergodic exploration of the search space. The proposed method is tested on three realistic search mission simulations and compared with three alternative methods, where HEDAC outperforms all alternatives in all tests. Conventional search strategies need about double the time to achieve proportionate detection rate when compared to HEDAC controlled search. The scalability test showed that increasing the number of HEDAC controlled search agents, although somewhat deteriorating the search efficiency, provides needed speed-up of the search. This study shows the flexibility and competence of the proposed method and gives a strong foundation for possible real-world applications.

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Section: Other Approaches and Methodsmentioning

confidence: 99%

Motion control for autonomous heterogeneous multi-agent area search in uncertain conditions

Ivić

2019

Preprint

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“…Connectivity maintenance algorithms can be designed coupled with distributed control in multi-robot systems [100], or collision avoidance [101]. Xiao et al have recently presented a cooperative multi-agent search algorithm with connectivity maintenance [102]. Similar works aiming at cooperative search, surveillance or tracking with multi-robot systems focus on optimizing the data paths [103] or fallible robots [104], [105].…”

Section: E Communicationmentioning

confidence: 99%

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

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“…Within the literature in this area, global connectivity maintenance algorithms ensure that any two agents are able to communicate either directly or through a multi-hop path [108]. More in line with SAR robotics, Xiao et al have recently presented a cooperative multi-agent search algorithm with connectivity maintenance [109]. Similar works aiming at cooperative search, surveillance or tracking with multi-robot systems focus on optimizing the data paths [110] or fallible robots [111], [112].…”

Section: E Communicationmentioning

confidence: 99%

Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception

Queralta,

Taipalmaa,

Pullinen

et al. 2020

Preprint

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Autonomous or teleoperated robots have been playing increasingly important roles in civil applications in recent years. Across the different civil domains where robots can support human operators, one of the areas where they can have more impact is in search and rescue (SAR) operations. In particular, multi-robot systems have the potential to significantly improve the efficiency of SAR personnel with faster search of victims, initial assessment and mapping of the environment, real-time monitoring and surveillance of SAR operations, or establishing emergency communication networks, among other possibilities. SAR operations encompass a wide variety of environments and situations, and therefore heterogeneous and collaborative multirobot systems can provide the most advantages. In this paper, we review and analyze the existing approaches to multi-robot SAR support, from an algorithmic perspective and putting an emphasis on the methods enabling collaboration among the robots as well as advanced perception through machine vision and multi-agent active perception. Furthermore, we put these algorithms in the context of the different challenges and constraints that various types of robots (ground, aerial, surface or underwater) encounter in different SAR environments (maritime, urban, wilderness or other post-disaster scenarios). This is, to the best of our knowledge, the first review considering heterogeneous SAR robots across different environments, while giving two complimentary points of view: control mechanisms and machine perception. Based on our review of the state-of-the-art, we discuss the main open research questions, and outline our insights on the current approaches that have potential to improve the real-world performance of multi-robot SAR systems.

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Cooperative multi-agent search using Bayesian approach with connectivity maintenance

Cited by 7 publications

References 27 publications

Motion control for autonomous heterogeneous multi-agent area search in uncertain conditions

Motion control for autonomous heterogeneous multi-agent area search in uncertain conditions

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception

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