Real-time tactical motion planning and obstacle avoidance for multi-robot cooperative reconnaissance

Boeing, Adrian; Pangeni, Sushil; Bräunl, Thomas; Lee, Chang Su

doi:10.1109/icsmc.2012.6378270

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Over the last few decades, cooperative control of multiagent systems has been an active area of research, which has motivated various applications (Boeing, Pangeni, Braunl, & Lee, 2012;Dunbabin & Marques, 2012;Liu & Nejat, 2013;Nigam, Bieniawski, Kroo, & Vian, 2012;Tsugawa, 2011). Various multi-agent problems, such as formation flying (Mastellone, Mejia, Stipanovic, & Spong, 2011), navigation (van den Berg, Lin, & Manocha, 2008, flocking (Olfati-Saber, 2006), and rendezvous (Cortés, Martínez, & Bullo, 2006) have been studied in both science and engineering.…”

Section: Introductionmentioning

confidence: 99%

Avoiding the local-minimum problem in multi-agent systems with limited sensing and communication

Okamoto

Akella

2014

International Journal of Systems Science

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In this paper, we consider a control problem for nonholonomic multi-agent systems in which agents and obstacles operate within a circular-shaped work area. We assume that agents only have limited sensing and communication ranges. We propose a novel control scheme using potential functions that drives agents from the initial to the goal configuration while avoiding collision with other agents, obstacles, and the boundary of the work area. The control scheme employs an avoidance strategy that ensures that the agents are never trapped at local minima that are typically encountered with most potential functionbased approaches. A numerical simulation is presented to demonstrate the validity and effectiveness of the proposed control scheme.

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Section: Introductionmentioning

confidence: 99%

Avoiding the local-minimum problem in multi-agent systems with limited sensing and communication

Okamoto

Akella

2014

International Journal of Systems Science

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Supported Influence Mapping for Mobile Robot Pathfinding in Dynamic Indoor Environments

Stawarz,

Ozog,

Łabuński

2024

Sensors

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Pathfinding is the process of finding the lowest cost route between a pair of points in space. The aforementioned cost can be based on time, distance, the number of required turns, and other individual or complex criteria. Pathfinding in dynamic environments is a complex issue, which has a long history of academic interest. An environment is considered dynamic when its topology may change in real time, often due to human interference. Influence mapping is a solution originating from the field of video games, which was previously used to solve similar problems in virtual environments, but achieved mixed results in real-life scenarios. The purpose of this study was to find whether the algorithm could be used in real indoor environments when combined with information collected by remote sensors.

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Real-time tactical motion planning and obstacle avoidance for multi-robot cooperative reconnaissance

Cited by 2 publications

References 19 publications

Avoiding the local-minimum problem in multi-agent systems with limited sensing and communication

Avoiding the local-minimum problem in multi-agent systems with limited sensing and communication

Supported Influence Mapping for Mobile Robot Pathfinding in Dynamic Indoor Environments

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