Hierarchical Needs Based Self-Adaptive Framework For Cooperative Multi-Robot System

Yang, Qin; Parasuraman, Ramviyas

doi:10.1109/smc42975.2020.9283249

Cited by 18 publications

(15 citation statements)

References 35 publications

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“…To evaluate our RNE Trust model for grouping, we implement the state-of-the-art energy-based (ENG) method [18] and use the distance-based (DIS) grouping approach as the baseline in the performance comparison. Besides, we combine the agent's HP level and distance (HP DIS ) as another grouping principle applied in the experiments.…”

Section: Compared Methods From the Literaturementioning

confidence: 99%

“…In Robot Needs Hierarchy [16], [18], the agent's needs expressed as the Needs Expectation, which can be calculated through its behaviors' weight and corresponding safety probability based on the data of perception and communication. The individual safety needs (Eq.…”

Section: B Robot Needs Vectormentioning

confidence: 99%

“…For the multi-robot grouping, we organize the group members and implement the bottom-up mechanism instead of partition without classification and hierarchy. More specifically, in the three homogeneous groups -Carrier, Supplier and Observer, we first consider separating them into two subgroups based on different combinations C The RNE grouping implementation is based on the Self-Adaptive Swarm System (SASS) [18]. The individual agent selecting a suitable group relies on the trust (RNE) calculation, which means that agents with similar needs levels will form a group executing the corresponding task.…”

Section: Heterogeneous Multi-robot Systemsmentioning

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

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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: Compared Methods From the Literaturementioning

confidence: 99%

Section: B Robot Needs Vectormentioning

confidence: 99%

Section: Heterogeneous Multi-robot Systemsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The parallel composition found use also in multi-robot applications, both with centralized [52] and distributed fashions [53], [54], resulting in improved fault tolerance and other performances. The parallel node involves multiple robots, each assigned to a specific task using a task-assignment algorithm.…”

Section: Related Workmentioning

confidence: 99%

Handling Concurrency in Behavior Trees

Colledanchise,

Natale

2021

Preprint

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This paper addresses the concurrency issues affecting Behavior Trees (BTs), a popular tool to model the behaviors of autonomous agents in the video game and the robotics industry.BT designers can easily build complex behaviors composing simpler ones, which represents a key advantage of BTs. The parallel composition of BTs expresses a way to combine concurrent behaviors that has high potential, since composing pre-existing BTs in parallel results easier than composing in parallel classical control architectures, as finite state machines or teleo-reactive programs. However, BT designers rarely use such composition due to the underlying concurrency problems similar to the ones faced in concurrent programming. As a result, the parallel composition, despite its potential, finds application only in the composition of simple behaviors or where the designer can guarantee the absence of conflicts by design.In this paper, we define two new BT nodes to tackle the concurrency problems in BTs and we show how to exploit them to create predictable behaviors. In addition, we introduce measures to assess execution performance and show how different design choices affect them. We validate our approach in both simulations and the real world. Simulated experiments provide statisticallysignificant data, whereas real-world experiments show the applicability of our method on real robots. We provide an opensource implementation of the novel BT formulation and published all the source code to reproduce the numerical examples and experiments.

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“…Assuming an infinite source of energy for the robot, which can be billed at 100% battery level regardless of the actual amount of energy required, is not realistically possible [11,12]. Moreover, the robot team can also be heterogeneous, meaning the battery capacities and speed can vary within the system [13,14].…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Multi-Robot Task Allocation in Persistent Tasks

Latif¹,

Gui²,

Munir³

et al. 2021

Preprint

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The applicability of the swarm robots to perform foraging tasks is inspired by their compact size and cost. A considerable amount of energy is required to perform such tasks, especially if the tasks are continuous and/or repetitive. Real-world situations in which robots perform tasks continuously while staying alive (survivability) and maximizing production (performance) require energy awareness. This paper proposes an energy-conscious distributed task allocation algorithm to solve continuous tasks (e.g., unlimited foraging) for cooperative robots to achieve highly effective missions. We consider efficiency as a function of the energy consumed by the robot during exploration and collection when food is returned to the collection bin. Finally, the proposed energy-efficient algorithm minimizes the total transit time to the charging station and time consumed while recharging and maximizes the robot's lifetime to perform maximum tasks to enhance the overall efficiency of collaborative robots. We evaluated the proposed solution against a typical greedy benchmarking strategy (assigning the closest collection bin to the available robot and recharging the robot at maximum) for efficiency and performance in various scenarios. The proposed approach significantly improved performance and efficiency over the baseline approach.

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Hierarchical Needs Based Self-Adaptive Framework For Cooperative Multi-Robot System

Cited by 18 publications

References 35 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

Handling Concurrency in Behavior Trees

Energy-Aware Multi-Robot Task Allocation in Persistent Tasks

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