Spatial distribution results for efficient multi-agent navigation

Mot, Jan De; Kulkarni, Vishwesh V.; Gentry, Sommer E.; Féron, Éric

doi:10.1109/cdc.2002.1184952

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…De Mot et al (2002) address a different problem where multiple units with limited local sensing seek to arrive at the same target, and mobile units are allowed to share the same vertex. Finding a legal solution is simple, and work has focused on optimality.…”

Section: Related Workmentioning

confidence: 99%

Spatially Distributed Multiagent Path Planning

Wilt

Botea²

2014

ICAPS

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Multiagent path planning is important in a variety of fields, ranging from games to robotics and warehouse management. Although centralized control in the joint action space can provide optimal plans, this often is computationally infeasi- ble. Decoupled planning is much more scalable. Traditional decoupled approaches perform a unit-centric decomposition, replacing a multi-agent search with a series of single-agent searches, one for each mobile unit. We introduce an orthogonal, significantly different approach, following a spatial distribution that partitions a map into high- contention, bottleneck areas and low-contention areas. Lo- cal agents called controllers are in charge with one local area each, routing mobile units in their corresponding area. Dis- tributing the knowledge across the map, each controller can observe only the state of its own area. Adjacent controllers can communicate to negotiate the transfer of mobile units. We evaluate our implemented algorithm, SDP, on real game maps with a mixture of larger areas and narrow, bottleneck gateways. The results demonstrate that spatially distributed planning can have substantial benefits in terms of makespan quality and computation speed.

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Section: Related Workmentioning

confidence: 99%

Spatially Distributed Multiagent Path Planning

Wilt

Botea²

2014

ICAPS

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“…In [6], we consider a cylindrically shaped graph. The spatial invariance in circumferential direction reduces the computational complexity significantly.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of two-agent clusters for efficient navigation

Mot

Féron

42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475)

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Abstract-Coordinated navigation by two cooperating sensor-equipped agents in a partially known static environment is investigated. Each agent observes a local part of the otherwise unknown environment and shares the gathered data with the other agents. In general, dynamic programming techniques suitably model the navigation problem, but are computationally hard to solve. We propose a combined dynamic and linear programming framework to circumvent the curse of dimensionality and establish in the process a firm upper bound on the spatial separation of a two-agent cluster navigating on a structured arbitrarily large graph.

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The navigation of autonomous vehicles in uncertain dynamic environments: a case study

Murai

Chaudhry

D’Andrea

Proceedings of the 41st IEEE Conference on Decision and Control, 2002.

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This paper proposes a method for building with multiple vehicles a probability map of uncertain dynamic environments. It is assumed that each vehicle has a limited sensor range and therefore lacks global information. The vehicles share their measurement information to build a probability map. The probability map is updated using sensor information and a priori statistics of the dynamic environment.

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Spatial distribution results for efficient multi-agent navigation

Cited by 6 publications

References 12 publications

Spatially Distributed Multiagent Path Planning

Spatially Distributed Multiagent Path Planning

Spatial distribution of two-agent clusters for efficient navigation

The navigation of autonomous vehicles in uncertain dynamic environments: a case study

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