Needs-driven Heterogeneous Multi-Robot Cooperation in Rescue Missions

Yang, Qin; Parasuraman, Ramviyas

doi:10.1109/ssrr50563.2020.9292570

Cited by 17 publications

(17 citation statements)

References 28 publications

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“…Comparing with the natural intelligent agent, when the artificial intelligence (AI) agent becomes more advanced and smart, it also represents more complex, multilayered, and diverse needs in evolution such as individual security, health, friendship, love, respect, recognition, and so forth. When we consider intelligent agents, like robots, working as a team or cooperating with human beings, organizing their needs building certain reliable and stable relationships such as trust is a precondition for robot-robot and human-robot collaboration in complex and uncertain environments [12].…”

Section: Resultsmentioning

confidence: 99%

“…Each robot is classified as one of the following: Carrier, Supplier, and Observer. Each type of robot has a specific role and functionality [12]. Multiple robots cooperate to fulfill this rescue mission within a limited time [12].…”

Section: Heterogeneous Multi-robot Systemsmentioning

confidence: 99%

“…Each type of robot has a specific role and functionality [12]. Multiple robots cooperate to fulfill this rescue mission within a limited time [12]. When they form a group, there must be robots from each of these three categories making the group heterogeneous in terms of functionalities.…”

Section: Heterogeneous Multi-robot Systemsmentioning

confidence: 99%

“…Considering the interactions between human agents and artificial intelligence agents like human-robot interaction (HRI), building stable and reliable relationships is of utmost importance in MAS cooperation, especially in adversarial environments and rescue missions [12]. Fig.…”

Section: Introductionmentioning

confidence: 99%

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How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model

Qin¹,

Parasuraman²

2021

Preprint

Self Cite

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Cooperation in multi-agent and multi-robot systems can help agents build various formations, shapes, and patterns presenting corresponding functions and purposes adapting to different situations. Relationship between agents such as their spatial proximity and functional similarities could play a crucial role in cooperation between agents. Trust level between agents is an essential factor in evaluating their relationships' reliability and stability, much as people do. This paper proposes a new model called Relative Needs Entropy (RNE) to assess trust between robotic agents. RNE measures the distance of needs distribution between individual agents or groups of agents. To exemplify its utility, we implement and demonstrate our trust model through experiments simulating a heterogeneous multi-robot grouping task in a persistent urban search and rescue mission consisting of tasks at two levels of difficulty. The results suggest that RNE trust-Based grouping of robots can achieve better performance and adaptability for diverse task execution compared to the stateof-the-art energy-based or distance-based grouping models.

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Section: Resultsmentioning

confidence: 99%

Section: Heterogeneous Multi-robot Systemsmentioning

confidence: 99%

Section: Heterogeneous Multi-robot Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model

Qin¹,

Parasuraman²

2021

Preprint

Self Cite

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show abstract

“…Moreover, the UUV system contains uncertainty that cannot be addressed initially, such that the heterogeneous vehicles of different model parameters, navigating velocities or safe distance are required to be studied. To step further, the topics on the formation control of heterogeneous vehicles between the UUV and the USV (or even unmanned aerial vehicles) have become more attractive, as the USV can help to instruct the UUV in real-time positioning through its less-affected GPS system and efficient communication above the water surface [127,128].…”

Section: Environmental Effect and Heterogeneous Vehiclesmentioning

confidence: 99%

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Zhu¹,

Yan²,

Yang³

2022

Intell Robot

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The motion planning and tracking control techniques of unmanned underwater vehicles (UUV) are fundamentally significant for efficient and robust UUV navigation, which is crucial for underwater rescue, facility maintenance, marine resource exploration, aquatic recreation, etc. Studies on UUV motion planning and tracking control have been growing rapidly worldwide, which are usually sorted into the following topics: task assignment of the multi-UUV system, UUV path planning, and UUV trajectory tracking. This paper provides a comprehensive review of conventional and intelligent technologies for motion planning and tracking control of UUVs. Analysis of the benefits and drawbacks of these various methodologies in literature is presented. In addition, the challenges and prospects of UUV motion planning and tracking control are provided as possible developments for future research.

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Hierarchical Reinforcement Learning

Huang¹

2020

Deep Reinforcement Learning

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The needs describe the necessities for a self-organizing system to survive and evolve, which arouses an agent to action toward a goal, giving purpose and direction to behavior. Based on Maslow's hierarchy of needs, an agent needs to satisfy a certain amount of needs at the current level as a condition to arise at the next stage -upgrade and evolution. Especially, Deep Reinforcement Learning (DAL) can help AI agents (like robots) organize and optimize their behaviors and strategies to develop diverse Strategies based on their current state and needs (expected utilities or rewards). This paper introduces the new hierarchical needs-driven Learning systems based on DAL and investigates the implementation in the single-robot with a novel approach termed Bayesian Soft Actor-Critic (BSAC). Then, we extend this topic to the Multi-Agent systems (MAS), discussing the potential research fields and directions.

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Needs-driven Heterogeneous Multi-Robot Cooperation in Rescue Missions

Cited by 17 publications

References 28 publications

How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model

How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

Hierarchical Reinforcement Learning

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