A Behavior Control Scheme for Multi-Robot Systems under Human Intervention

Huang, Jie; Wu, Wenhua; Ning, Yingying; Zhou, N.; Xu, Zezhuang

doi:10.23919/chicc.2019.8866251

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…the human intervention timing during the local minima problem period, which is between 6.22s and 9.4s. This shows the advantage of the proposed HDDM when compared to tte traditional method in [20], where the timing of human intervention does not match that of the local minima problem period.…”

Section: B Robots Finish Tasks With Human Interventionmentioning

confidence: 98%

“…2. Comparing to [20] where the human decision-making is a simple process to evaluate whether human intervention behavior is needed, we analyze the human decision-making process by introducing a dataprocessing station and a human cognitive system. The dataprocessing station receives incoming feedback information from robots sensors, and the human decision-making process is modeled if decision-making information has been received from the incoming feedback.…”

Section: Human Decision-making Behavior Modeling and Intervention Task Designmentioning

confidence: 99%

“…Results in constructing HRI structures can be found in [13]- [16]. In addition, human intervention methods in HRI have been studied in the leader-follower framework [17]- [19] and have been firstly introduced into the behavioral control framework in [20]. Although successful in specific applications of interest, the methods aforementioned lack an accurate human model.…”

Section: Introductionmentioning

confidence: 99%

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A Human Decision-Making Behavior Model for Human-Robot Interaction in Multi-Robot Systems

Huang

Zhang

2020

IEEE Access

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In this paper, a novel human decision-making behavior model is proposed for human-robot interaction (HRI) for the control of multi-robot systems (MRS). The proposed human drift diffusion model (HDDM) combines the traditional drift diffusion model (DDM) and the null-space-based behavioral control (NSBC) method by introducing a data-processing station and a human cognitive system. In the HDDM, the evolution of human-decision information is computed. By using a threshold of such information to trigger human interaction, accurate human decision-making timing can be obtained. In addition, a cooperative controller is designed for robots to follow human instructions. Simulations under various scenarios show that by using the proposed HDDM and the controller, robots can complete human instructions more accurately comparing to traditional methods. An experiment using a group of quadrotors subject to external wind disturbances also demonstrate the effectiveness of the proposed HDDM in real-world uncertain environments.

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Section: B Robots Finish Tasks With Human Interventionmentioning

confidence: 98%

Section: Human Decision-making Behavior Modeling and Intervention Task Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Human Decision-Making Behavior Model for Human-Robot Interaction in Multi-Robot Systems

Huang

Zhang

2020

IEEE Access

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“…Huang et al [86] study the behavioral control of a group of autonomous robots under human intervention, and each agent is modeled by an single integrator. They design a behavior control under human intervention, where the highest priority task is the human-dominated task (human task), and all the robot self-determination tasks are turned into the second or even lower priority.…”

Section: Coalition Formationmentioning

confidence: 99%

Recent Advances in Formations of Multiple Robots

Cohen

Agmon

2021

Curr Robot Rep

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Purpose of Review Formation control is a canonical problem in multi-robot systems, which focuses on the ability of a group of robots to travel in coordination through an area, while maintaining a certain shape or a particular behavior. The robot groups vary in their communication, computation, and sensing capabilities. Moreover, the formation control task itself may have various objectives. These divergences force the use of different models for controlling the formation and for analyzing the task performance. In this paper, we describe the formation control problem and survey recent advances focusing on aspects of maintaining a formation by a group of robots distinguished by the means of analysis.Recent Findings Various approaches may be applied for the sake of formation maintenance, whereas each approach possesses a different perspective in regard with formation control. Recent research focuses on combining those approaches, due to their applicability regarding certain scenarios. For instance, consensus-based control and collision avoidance are usually intertwined together for the sake of reaching a consensus in a manner which is collision-free. Furthermore, machine learning (ML)-based methods for navigating a robot team through unknown complex environments can be incorporated, where the robot team aims to reach a goal position while avoiding collisions and maintaining connectivity. Moreover, recent approaches focus on developing new mechanisms or adapt existing ones for formation control for tolerating limitations in sensing, communication, and coordination, preferably distributively while providing performance guarantees. ConclusionSuch combined approaches yield that the means of analysis, which can be applied to each one separately, can also be utilized in an intertwined manner, and thus provide us with novel methods for preserving formation. Whereas some approaches were vastly investigated (e.g., consensus-based formation control) and need to be adapted to distributed imperfect settings, others still require further insight for unveiling brand new architectures and tools (e.g., ML-based formation control).

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“…This case is more complicated than the traditional local minimum problem where agents stop moving because of two opposite velocities from different tasks. For the traditional method, additional algorithms, such as adding small measurement noises [25] and introducing human intervention [27], are required to handle the local minimum problem. However, the proposed MPC method in the form of (11) can handle dynamic obstacles and local minimum problems explicitly and systematically by incorporating constraint (11c) and (11d).…”

Section: A Optimal Control Formulationmentioning

confidence: 99%

Dynamic Task Priority Planning for Null-Space Behavioral Control of Multi-Agent Systems

Zhang

Huang

2020

IEEE Access

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The task priority planning problem is addressed in the task supervisor of null-space behavioral (NSB) control for multi-agent systems. Traditional methods rely on pre-defined logic-based or fuzzy rules to adjust task priority. In this work, a novel task supervisor is proposed using model predictive control (MPC) techniques. At each sampling instant, the task priority planning problem is formulated as a switching mode optimal control problem (OCP), which can be solved by efficient mixed-integer optimal control algorithms. The optimal task priority order is obtained based on current and predictive information of agents, without the need for a pre-defined rule. By explicitly introducing slack variables into constraints, the proposed MPC method is flexible to cope with dynamic obstacles in unknown environments. Simulations with static and dynamic obstacles show that the proposed method can provide significantly better control performance than the traditional logic-based method using less priority switchings.INDEX TERMS Multi-agent systems, behavioral control, task supervisor, model predictive control.

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A Behavior Control Scheme for Multi-Robot Systems under Human Intervention

Cited by 6 publications

References 10 publications

A Human Decision-Making Behavior Model for Human-Robot Interaction in Multi-Robot Systems

A Human Decision-Making Behavior Model for Human-Robot Interaction in Multi-Robot Systems

Recent Advances in Formations of Multiple Robots

Dynamic Task Priority Planning for Null-Space Behavioral Control of Multi-Agent Systems

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