Coordination Strategies for Multi-robot Exploration and Mapping

Rogers, John G.; Nieto-Granda, Carlos; Christensen, Henrik I.

doi:10.1007/978-3-319-00065-7_17

Cited by 19 publications

(11 citation statements)

References 16 publications

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“…OmniMapper has been used in prior work to build maps of indoor and outdoor environments [17], to determine mapping performance with sensory degradation [11], for multi-robot mapping [12], [9], and multi-robot mapping with heterogenous sensory modalities [10]. The system has also been used to support semantic mapping, where additional high-level information is included within a map as it is observed by the robot.…”

Section: A Mapping Systemmentioning

confidence: 98%

Mapping with a ground robot in GPS denied and degraded environments

Rogers

Fink

Stump

2014

2014 American Control Conference

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A robot system operating in an unknown environment must be able to track its position to perform its mission. Vehicles with a consistent view of the sky, e.g., aerial or water surface platforms, can reliably make use of GPS signals to correct accumulated error from inertial measurements and feature-based mapping techniques. However, ground robots that must operate across a wide range of environments suffer from additional constraints which degrade the performance of GPS such as multipath and occlusion. In this paper, we present a methodology for incorporating GPS measurements into a feature-based mapping system for two purposes: providing georeferenced coordinates for high-level mission execution and correcting accumulated map error over long-term operation. We will present both the underlying system and experimental results from a variety of relevant environments such as military training facilities and large-scale mixed indoor and outdoor environments.

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Section: A Mapping Systemmentioning

confidence: 98%

Mapping with a ground robot in GPS denied and degraded environments

Rogers

Fink

Stump

2014

2014 American Control Conference

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“…Research in this area has focused mainly on multirobot cooperative exploration of varying environments (Rathnam and Birk, ). With respect to USAR applications, the focus has been on improving exploration efficiency (de Hoog et al., ; Rogers III et al., ; Ziparo et al., ).…”

Section: Related Workmentioning

confidence: 99%

Multirobot Cooperative Learning for Semiautonomous Control in Urban Search and Rescue Applications

Liu

Nejat

2015

Journal of Field Robotics

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The use of cooperative multirobot teams in urban search and rescue (USAR) environments is a challenging yet promising research area. For multirobot teams working in USAR missions, the objective is to have the rescue robots work effectively together to coordinate task allocation and task execution between different team members in order to minimize the overall exploration time needed to search disaster scenes and to find as many victims as possible. This paper presents the development of a multirobot cooperative learning approach for a hierarchical reinforcement learning (HRL) based semiautonomous control architecture in order to enable a robot team to learn cooperatively to explore and identify victims in cluttered USAR scenes. The proposed cooperative learning approach allows effective task allocation among the multirobot team and efficient execution of the allocated tasks in order to improve the overall team performance. Human intervention is requested by the robots when it is determined that they cannot effectively execute an allocated task autonomously. Thus, the robot team is able to make cooperative decisions regarding task allocation between different team members (robots and human operators) and to share experiences on execution of the allocated tasks. Extensive results verify the effectiveness of the proposed HRL‐based methodology for multi‐robot cooperative exploration and victim identification in USAR‐like scenes.

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“…Rogers et al [3] develop a centralized scheme to coordinate frontier-based exploration by large robot teams and investigate which robot coordinate strategies are effective. Decentralized schemes have been introduced by Visser & de Hoog [4] and Chand & Carnegie [5] for heterogeneous teams by assigning different roles to team members.…”

Section: Prior Workmentioning

confidence: 99%

Leveraging area bounds information for autonomous decentralized multi-robot exploration

Liu

Lyons

2015

Robotics and Autonomous Systems

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h i g h l i g h t s• A uniform, decentralized, potential-field based approach to dispersing a team of robots to explore an area quickly.• Leverages knowledge of the overall bounds of the area to be explored and includes a monotonic coverage factor in the field equations to avoid minima.• Allows robot team members to become disconnected from and reconnected with the team and ongoing dispersion strategy.• Introduces performance metrics for team speedup and efficiency in the case of adding members to teams.• Presents both simulation and experimental robot results. a r t i c l e i n f o t r a c tThis paper proposes a simple and uniform, decentralized approach to the problem of dispersing a team of robots to explore an area quickly. The Decentralized Space-Based Potential Field (D-SBPF) algorithm is a potential field approach that leverages knowledge of the overall bounds of the area to be explored. It includes a monotonic coverage factor in the potential field to avoid minima, realistic sensor bounds, and a distributed map exchange protocol.The D-SBPF approach yields a simple potential field control strategy for all robots but nonetheless has good dispersion and overlap performance in exploring areas with convex geometry while avoiding potential minima. Both simulation and robot experimental results are included as evidence, and performance, speedup and efficiency metrics for each are presented.

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Coordination Strategies for Multi-robot Exploration and Mapping

Cited by 19 publications

References 16 publications

Mapping with a ground robot in GPS denied and degraded environments

Mapping with a ground robot in GPS denied and degraded environments

Multirobot Cooperative Learning for Semiautonomous Control in Urban Search and Rescue Applications

Leveraging area bounds information for autonomous decentralized multi-robot exploration

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