A decentralized approach to space deconfliction

Scerri, Paul; Owens, Sean; Yu, Bin; Sycara, Katia

doi:10.1109/icif.2007.4408182

Cited by 14 publications

(8 citation statements)

References 15 publications

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“…Scerri et al (2007) propose a deconfliction strategy in which the team must reach consensus on the feasibility of each agent's updated plan. While this reduces the amount of local knowledge required to check for path feasibility, it also introduces considerable delay into the planning process and is better suited to sparse environments where only infrequent planning is required.…”

Section: Decentralized Planningmentioning

confidence: 99%

Decentralized path planning for multi-agent teams with complex constraints

2012

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This paper presents a novel approach to address the challenge of planning paths for multi-agent systems subject to complex constraints. The technique, called the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT) algorithm, extends the Closed-loop RRT (CL-RRT) algorithm to handle multiple agents while retaining its ability to plan quickly. A core component of the DMA-RRT algorithm is a merit-based token passing coordination strategy that makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents replan. The reordering is based on a measure of each agent's incentive to change the plan and allows agents with a greater potential improvement to replan sooner, which is demonstrated to improve the team's overall performance compared to a traditional, scripted replan order. The main contribution of the work is a version of the algorithm, called Cooperative DMA-RRT, which introduces a cooperation strategy that allows an agent to modify its teammates' plans in order to select paths that reduce their combined cost. This modification further improves team performance and avoids certain common deadlock scenarios. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and numerous simulation and experimental results are presented to demonstrate their performance.

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Section: Decentralized Planningmentioning

confidence: 99%

Decentralized path planning for multi-agent teams with complex constraints

2012

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“…We added simple models of human cognitive processes to these models and looked at how human characteristics might influence largescale fusion [5]. Finally, we began to look at lightweight algorithms that could be used to effectively share information in large networks, while respecting bandwidth constraints [7,8].…”

Section: \ Ao°ilm^\mentioning

confidence: 99%

Large Scale Distributed Computation for Human Agent Team Coordination in Dynamic Environments Coordination in Dynamic Environments

Sycara¹

2010

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“…With the addition of resources available across multiple domains, the workload increases substantially. Human planners lack the cognitive bandwidth to coordinate and synchronize operations across the unique complexities of water, air [2] and space [1].…”

Section: Introductionmentioning

confidence: 99%

Emergency Relief Planning and Management Through Dynamic Agent-based Reallocation and Tasking (DART)

Johnson

2015

Procedia Engineering

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Disaster and emergency management poses a tremendous challenge on the human planners coordinating the emergency services. With an ever changing operating environment during a crisis, coupled with differing capabilities and availabilities of resources, creates a significant problem. With the addition of accounting for a level of uncertainty, this quickly becomes a complex problem for humans to solve. The problem grows exponentially demanding when the coordination and synchronization of response efforts must determine the optimal resource allocation options across multiple domains such as air, water and space. To provide a means of solving these types of problems we have developed the Dynamic Agent-based Reallocation and Tasking (DART) system. This system offers a framework that allows for maintaining focus on mission objectives by optimizing resource allocation decisions across multiple diverse domains.

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A decentralized approach to space deconfliction

Cited by 14 publications

References 15 publications

Decentralized path planning for multi-agent teams with complex constraints

Decentralized path planning for multi-agent teams with complex constraints

Large Scale Distributed Computation for Human Agent Team Coordination in Dynamic Environments Coordination in Dynamic Environments

Emergency Relief Planning and Management Through Dynamic Agent-based Reallocation and Tasking (DART)

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