Modeling and Control of Trust in Human-Robot Collaborative Manufacturing

Sadrfaridpour, Behzad; Saeidi, Hamed; Burke, Jennifer; Madathil, Kapil Chalil; Wang, Yue

doi:10.1007/978-1-4899-7668-0_7

Cited by 69 publications

(46 citation statements)

References 23 publications

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“…The actual realization and the sequence of the process, i.e., dynamic evolution of trust over time, is hidden. Based on our previous works involving creation of a time-series trust model [58,59,66], we identify three major factors impacting trust, i.e., robot performance P R,i , human performance P H,i , and joint human-robot system fault F i . These factors are shown in solid yellow ellipses in the figure.…”

Section: Human Inputsmentioning

confidence: 99%

“…(1) We develop a dynamic, quantitative, and probabilistic human-to-robot trust model to compute the evolution of trust during real-time robotic operations. Our proposed trust model integrates the time-series trust model [58,59,66] and the Online Probabilistic Trust Inference Model (OPTIMo) [68] to compute trust quantitatively -a critical first step to enable trust-based multi-robot symbolic motion planning; (2) We propose trust-based specification decomposition to address the scalability issue in distributed multi-robot symbolic motion planning. Compositional reasoning approaches are used to decompose the global task specification into local specifications for each robot.…”

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confidence: 99%

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Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

Wang

Humphrey

Liao

et al. 2018

ACM Trans. Interact. Intell. Syst.

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Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence. In this paper, distributed symbolic motion planning for multi-robot systems is developed to address scalability. More specifically, compositional reasoning approaches are developed to decompose the global planning problem, and atomic propositions for observation, communication, and control are proposed to address inter-robot collision avoidance.To improve solution quality and adaptability, a dynamic, quantitative, and probabilistic human-to-robot trust model is developed to aid this decomposition. Furthermore, a trust-based real-time switching framework is proposed to switch between autonomous and manual motion planning for tradeoffs between task safety and efficiency. Deadlock-and livelock-free algorithms are designed to guarantee reachability of goals with a human-in-the-loop. A set of non-trivial multi-robot simulations with direct human input and trust evaluation are provided demonstrating the successful implementation of the trust-based multi-robot symbolic motion planning methods.

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Section: Human Inputsmentioning

confidence: 99%

mentioning

confidence: 99%

Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

Wang

Humphrey

Liao

et al. 2018

ACM Trans. Interact. Intell. Syst.

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“…Each parameter is evaluated with a function of respective influence factors in task allocation and motion planning. The coefficients A, B 1 , B 2 ,C 1 ,C 2 , D 1 , D 2 , E 1 , E 2 are determined by data collected from human subject tests [15]. In our scenario, the accumulated performance evaluation P R,i (t) is modeled as a function of rewards on robot for its completion of actions as well as the avoidance of obstacles,…”

Section: T I (T)mentioning

confidence: 99%

Human-Robot Trust Integrated Task Allocation and Symbolic Motion Planning for Heterogeneous Multi-Robot Systems

Zheng

Liao

Wang

2018

Volume 3: Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control System

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This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of tasks concurrently. A set of task specifications in parallel are conjuncted with MRS to synthesize a task allocation automaton. Each transition of the task allocation automaton is associated with the total trust value of human in corresponding robots. Here, the human-robot trust model is constructed with a dynamic Bayesian network (DBN) by considering individual robot performance, safety coefficient, human cognitive workload and overall evaluation of task allocation. Hence, a task allocation path with maximum encoded humanrobot trust can be searched based on the current trust value of each robot in the task allocation automaton. Symbolic motion planning (SMP) is implemented for each robot after they obtain the sequence of actions. The task allocation path can be intermittently updated with this DBN based trust model. The overall strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask automata.

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“…Collaborative robots that are used in manufacturing plants are becoming more flexible and efficient [53]. Robots are now considered, by some, as an integral part of industries, due to their role in improving accuracy, repeatability reliability, preciseness, and efficiency [50].…”

Section: Manufacturing Automationmentioning

confidence: 99%

Concepts for 3D Printing-Based Self-Replicating Robot Command and Coordination Techniques

Jones

Straub

2017

Machines

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Self-replicating robots represent a new area for prospective advancement in robotics. A self-replicating robot can identify when additional robots are needed to solve a problem or meet user needs, and create them in response to this identified need. This allows robotic systems to respond to changing (or non-predicted) mission needs. Being able to modify the physical system component provides an additional tool for optimizing robotic system performance. This paper begins the process of developing a command and coordination system that makes decisions with the consideration of replication, repair, and retooling capabilities. A high-level algorithm is proposed and qualitatively assessed.

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Modeling and Control of Trust in Human-Robot Collaborative Manufacturing

Cited by 69 publications

References 23 publications

Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

Human-Robot Trust Integrated Task Allocation and Symbolic Motion Planning for Heterogeneous Multi-Robot Systems

Concepts for 3D Printing-Based Self-Replicating Robot Command and Coordination Techniques

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