Probabilistic Decision Model for Adaptive Task Planning in Human-Robot Collaborative Assembly Based on Designer and Operator Intents

Cramer, Martijn; Kellens, Karel; Demeester, Eric

doi:10.1109/lra.2021.3095513

Cited by 14 publications

(6 citation statements)

References 16 publications

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“…Robot vision is often supported by attaching a fiducial marker to each object (e.g. [302], [303], [311], [312]) or by using distinctly coloured objects for more robust segmentation (e.g. [7], [10], [25], [223]).…”

Section: Discussionmentioning

confidence: 99%

“…As discussed in Section IV-E, these problems can be reduced by shifting experiments into virtual spaces. In case of user studies with physical demonstrators, synthetic tasks offer further beneficial properties also with regard to reproducibility: Product mock-ups can easily be made with 3D printers [25], [72], [219], [302], [311], [312], [315] which are a broadly available resource in the meantime. Unfortunately, prior publications with interesting model sets are not always accompanied by the required CAD models (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Other exemplary tasks are gear meshing and gearboxes [72], [219], electrical circuitry [310], a jet engine mock-up [315], bicycle subassembly [314], sanding machine [313], or flange assembly from metal parts and standard screws [12]. In addition to these tasks created by abstracting systematically from real products and production processes, fully synthetic tasks such as the Cranfield Benchmark [312], Bourjault's Pen [311], or simple 3-component products [222] have been used.…”

Section: B Tasks Used In Joint Action Experimentsmentioning

confidence: 99%

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Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

2023

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Human-robot teaming receives an ever-increasing level of attention in research, development and industry. Novel approaches to task sharing in hybrid teams range from optimized schedules to intelligent cobot assistants with a high degree of autonomy. These approaches must prove their usefulness and benefits compared to manual work or full automation -particularly when it comes to assessing their potential for productive industrial use. This leverages demand for standardized, repeatable benchmarks to compare approaches and measure improvements in a reproducible way. Designing such benchmarks is challenging as numerous aspects, from safety considerations to human factors and team performance, must be considered. This survey seeks to contribute to the future development of benchmarks for the field of collaborative assembly, handling, and industrial cobot applications by giving a comprehensive overview of relevant metrics, evaluation strategies, and tasks for human-robot teams.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: B Tasks Used In Joint Action Experimentsmentioning

confidence: 99%

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Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

2023

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“…3) Combinatorial Optimization Model for Collaborative Role Allocation: The main limitation of the allocation method in subsubsection II-B2, as well as most of SoA methods [11], [18], [19], [39], consists in the fact that it is suitable only for synchronized/cooperative tasks, where agents work simultaneously in the shared workspace, but it does not model properly human-robot collaborative tasks, i.e. tasks where agents work on the same workpiece at the same time, balancing their efforts to achieve a shared goal.…”

Section: B Role Allocationmentioning

confidence: 99%

A Comprehensive Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams

Lamon¹,

Fusaro²,

Momi³

et al. 2023

Preprint

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The growing deployment of human-robot collaborative processes in several industrial applications, such as handling, welding, and assembly, unfolds the pursuit of systems which are able to manage large heterogeneous teams and, at the same time, monitor the execution of complex tasks. In this paper, we present a novel architecture for dynamic role allocation and collaborative task planning in a mixed human-robot team of arbitrary size. The architecture capitalizes on a centralized reactive and modular task-agnostic planning method based on Behavior Trees (BTs), in charge of actions scheduling, while the allocation problem is formulated through a Mixed-Integer Linear Program (MILP), that assigns dynamically individual roles or collaborations to the agents of the team. Different metrics used as MILP cost allow the architecture to favor various aspects of the collaboration (e.g. makespan, ergonomics, human preferences). Human preference are identified through a negotiation phase, in which, an human agent can accept/refuse to execute the assigned task. In addition, bilateral communication between humans and the system is achieved through an Augmented Reality (AR) custom user interface that provides intuitive functionalities to assist and coordinate workers in different action phases. The computational complexity of the proposed methodology outperforms literature approaches in industrial sized jobs and teams (problems up to 50 actions and 20 agents in the team with collaborations are solved within 1 s). The different allocated roles, as the cost functions change, highlights the flexibility of the architecture to several production requirements. Finally, the subjective evaluation demonstrating the high usability level and the suitability for the targeted scenario.

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“…Many studies have addressed similar problems using deep learning or reinforcement learning approaches to enable the correct processing of boundedly rational behavior. For example, Cramer [11] et al used partially observable Markov decision processes (POMDP) to develop a framework for planning collaborative robot tasks in assembly, considering both the designer and operator's intents. From the designer's CAD data, a set of potential assembly plans is automatically derived and translated into a state graph from which the operator's intentions follow.…”

Section: Related Workmentioning

confidence: 99%

Research on Cobot Action Decision-Making Method Based on Intuitionistic Fuzzy Set and Game Theory

Liu

Wang

et al. 2022

IEEE Access

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The bounded rational properties of humans in human-robot collaboration (HRC) is a fundamental reason for collisions in proximity HRC. As HRC scenarios in manufacturing become increasingly popular, robot action decision-making needs to consider such properties of humans. In previous studies, humans are usually considered as rational agents whose behaviors are predictable and planned, but humans are susceptible to distractions caused by external disturbances, and different cognitive processes of the task can produce unpredictable behaviors. To better simulate human bounded rational behavior, based on an intuitionistic fuzzy (IF) multi-attribute decision algorithm, we propose a cobot action decision-making method that integrates human intention, safety, and efficiency, to produce a human-like decision. The proposed decision-making method integrates the bounded rational behavior of humans and establishes the IF set of human action intention for efficiency and comfort using cumulative prospect theory(CPT) and IF set. The proposed method lights on the human decision-making mechanism for proximity HRC actions. We use IF set to calculate the score and accuracy values of two Nash equilibria of a static chicken game, which can predict human action intentions with collision risk and provide optimal action decisions for the robot simultaneously. We generated 10,000 sets of data using the Monte Carlo method and validated the effectiveness of our proposed method by comparing it with MDP and POMDP methods. The results showed that the decision-making method could effectively perform the task of making action decisions for the robot. Simulation experiments and Turing test results show that our proposed method can predict a human's subjective action decision intention in a situation with potential collision risk with 87.53% accuracy. At the same time, the experimental participants believe that the robot can get 4.83 out of 5 satisfaction points for an action decision.

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Probabilistic Decision Model for Adaptive Task Planning in Human-Robot Collaborative Assembly Based on Designer and Operator Intents

Cited by 14 publications

References 16 publications

Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

Benchmarking Teamwork of Humans and Cobots—An Overview of Metrics, Strategies, and Tasks

A Comprehensive Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams

Research on Cobot Action Decision-Making Method Based on Intuitionistic Fuzzy Set and Game Theory

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