Task Location for High Performance Human-Robot Collaboration

Sharkawy, Abdel-Nasser; Papakonstantinou, Charalampos; Papakostopoulos, Vassilis; Moulianitis, Vassilis C.; Aspragathos, Nikos Α.

doi:10.1007/s10846-020-01181-5

Cited by 8 publications

(23 citation statements)

References 26 publications

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“…Through the [ 30 ] in human and robot collaboration process. When the flexibility of human arm is given to the manipulator, the local optimal solution will appear due to the fluctuation of the human arm when entering the initial trajectory.…”

Section: Energy Optimization Methodsmentioning

confidence: 99%

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

Deng

Zhou

Zhao

et al. 2022

Sensors

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Energy saving in palletizing robot is a fundamental problem in the field of industrial robots. However, the palletizing robot often suffers from the problems of high energy consumption and lacking flexibility. In this work, we introduce a novel differential evolution algorithm to address the adverse effects caused by the instability of the initial trajectory parameters while reducing the energy. Specially, a simplified analytical model of the palletizing robot is firstly developed. Then, the simplified analytical model and the differential evolutionary algorithm are combined to form a planner with the goal of reducing energy consumption. The energy saving planner optimizes the initial parameters of the trajectories collected by the bionic demonstration system, which in turn enables a reduction in the operating power consumption of the palletizing robot. The major novelty of this article is the use of a differential evolutionary algorithm that can save the energy consumption as well as boosting its flexibility. Comparing with the traditional algorithms, the proposed method can achieve the state-of-the-art performance. Simulated and actual experimental results illustrate that the optimized trajectory parameters can effectively reduce the energy consumption of palletizing robot by 16%.

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Section: Energy Optimization Methodsmentioning

confidence: 99%

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

Deng

Zhou

Zhao

et al. 2022

Sensors

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“…The manipulability index [25] presents the ability of the system to perform a high TCP velocity motion with a low overall joint velocity. In our previous paper [26], the evaluation of the closed kinematic chain (CKC) system configuration was based on the manipulability index. Firstly, the condition number is used as an index to describe the accuracy/dexterity of the robot.…”

Section: Related Workmentioning

confidence: 99%

“…The results prove that the proposed approach effectively improved HRI and, particularly, ergonomics. Finally, a comparison between the current proposed approach and the previous approach where the task performance criterion was based on the CKC manipulability index [26] was included.…”

Section: The Main Contributionmentioning

confidence: 99%

Task Location to Improve Human–Robot Cooperation: A Condition Number-Based Approach

Sharkawy

2023

Automation

Self Cite

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This paper proposes and implements an approach to evaluate human–robot cooperation aimed at achieving high performance. Both the human arm and the manipulator are modeled as a closed kinematic chain. The proposed task performance criterion is based on the condition number of this closed kinematic chain. The robot end-effector is guided by the human operator via an admittance controller to complete a straight-line segment motion, which is the desired task. The best location of the selected task is determined by maximizing the minimum of the condition number along the path. The performance of the proposed approach is evaluated using a criterion related to ergonomics. The experiments are executed with several subjects using a KUKA LWR robot to repeat the specified motion to evaluate the introduced approach. A comparison is presented between the current proposed approach and our previously implemented approach where the task performance criterion was based on the manipulability index of the closed kinematic chain. The results reveal that the condition number-based approach improves the human–robot cooperation in terms of the achieved accuracy, stability, and human comfort, but at the expense of task speed and completion time. On the other hand, the manipulability-index-based approach improves the human–robot cooperation in terms of task speed and human comfort, but at the cost of the achieved accuracy.

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“…On the other hand, other works deal with maximizing the collaboration performance by promoting mutual adaptation ( Nikolaidis et al, 2017b ; Chen et al, 2020 ) or reconsidering the task allocation ( Malik and Bilberg, 2019 ). However, they only consider one or two unchangeable performance metrics for this evaluation in their utility function: postural or ergonomic optimization ( Sharkawy et al, 2020 ), time consumption ( Weitschat and Aschemann, 2018 ), trajectory optimization ( Fishman et al, 2019 ), cognitive aspects ( Tanevska et al, 2020 ), and reduction of the number of human errors ( Tabrez and Hayes, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

General Framework for the Optimization of the Human-Robot Collaboration Decision-Making Process Through the Ability to Change Performance Metrics

et al. 2021

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This paper proposes a new decision-making framework in the context of Human-Robot Collaboration (HRC). State-of-the-art techniques consider the HRC as an optimization problem in which the utility function, also called reward function, is defined to accomplish the task regardless of how well the interaction is performed. When the performance metrics are considered, they cannot be easily changed within the same framework. In contrast, our decision-making framework can easily handle the change of the performance metrics from one case scenario to another. Our method treats HRC as a constrained optimization problem where the utility function is split into two main parts. Firstly, a constraint defines how to accomplish the task. Secondly, a reward evaluates the performance of the collaboration, which is the only part that is modified when changing the performance metrics. It gives control over the way the interaction unfolds, and it also guarantees the adaptation of the robot actions to the human ones in real-time. In this paper, the decision-making process is based on Nash Equilibrium and perfect-information extensive form from game theory. It can deal with collaborative interactions considering different performance metrics such as optimizing the time to complete the task, considering the probability of human errors, etc. Simulations and a real experimental study on “an assembly task” -i.e., a game based on a construction kit-illustrate the effectiveness of the proposed framework.

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Task Location for High Performance Human-Robot Collaboration

Cited by 8 publications

References 26 publications

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

Task Location to Improve Human–Robot Cooperation: A Condition Number-Based Approach

General Framework for the Optimization of the Human-Robot Collaboration Decision-Making Process Through the Ability to Change Performance Metrics

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