Developing a Multi-agent Based Modeling for Smart Search and Rescue Operation

Azimi, S.; Delavar, Mahmoud Reza; Rajabifard, Abbas

doi:10.1007/978-3-030-05330-7_6

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…We include the number of map points observed from current frame in feature vector. 2 The second of component of the feature vector are variables related to the key frames which are the selected frames according to strict requirements to ensure that they contain sufficient map information. Some key frames proved to contain insufficiently accurate map information will be gradually removed.…”

Section: Orbslam2 Feature Vectormentioning

confidence: 99%

“…Each agent's ability, attributes and structure are different, which provides a great space for multiagent collaboration. MAS have attracted many researchers to investigate the applicability of it in many pertinent areas closely related to human livelihood and industry such as security system [1], exploration and rescue [2], surveillance [3], humanitarian assistance [4], environment protection [5] [6] health care [7] [8]. Traditional SLAM has been considered as an independent behavior.…”

Section: Introductionmentioning

confidence: 99%

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Active collaboration in relative observation for multi-agent visual simultaneous localization and mapping based on Deep Q Network

Pei

Piao

Quan

et al. 2020

International Journal of Advanced Robotic Systems

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This paper proposes a unique active relative localization mechanism for multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be observed are considered as a task, which is performed by others assisting that agent by relative observation. A task allocation algorithm based on deep reinforcement learning are proposed for this mechanism. Each agent can choose whether to localize other agents or to continue independent SLAM on it own initiative. By this way, the process of each agent SLAM will be interacted by the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique observation function which models the whole MAS is obtained. Secondly, a novel type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence between Q Value and state-action pair, abstract representation of agents in MAS are learned in the process of collaboration among

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Section: Orbslam2 Feature Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active collaboration in relative observation for multi-agent visual simultaneous localization and mapping based on Deep Q Network

Pei

Piao

Quan

et al. 2020

International Journal of Advanced Robotic Systems

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An agent-based model reveals lost person behavior based on data from wilderness search and rescue

Hashimoto

Heintzman

Koester

et al. 2022

Sci Rep

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Thousands of people are reported lost in the wilderness in the United States every year and locating these missing individuals as rapidly as possible depends on coordinated search and rescue (SAR) operations. As time passes, the search area grows, survival rate decreases, and searchers are faced with an increasingly daunting task of searching large areas in a short amount of time. To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior defined as independent realizations of a random variable. The behavior random variable selects from a distribution of six known lost person reorientation strategies to simulate the agent’s trajectory. We systematically simulate a range of possible behavior distributions and find a best-fit behavioral profile for a hiker with the International Search and Rescue Incident Database. We validate these results with a leave-one-out analysis. This work represents the first time-discrete model of lost person dynamics validated with data from real SAR incidents and has the potential to improve current methods for wilderness SAR.

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Developing a Multi-agent Based Modeling for Smart Search and Rescue Operation

Cited by 2 publications

References 32 publications

Active collaboration in relative observation for multi-agent visual simultaneous localization and mapping based on Deep Q Network

Active collaboration in relative observation for multi-agent visual simultaneous localization and mapping based on Deep Q Network

An agent-based model reveals lost person behavior based on data from wilderness search and rescue

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