Adaptive Motion Planning for a Collaborative Robot Based on Prediction Uncertainty to Enhance Human Safety and Work Efficiency

Kanazawa, Akira; Kinugawa, Jun; Kosuge, Kazuhiro

doi:10.1109/tro.2019.2911800

Cited by 100 publications

(64 citation statements)

References 37 publications

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“…The simulation results in Figs. 8, 9, 11 and Table 1 show that the proposed method is superior to the bi-modal control strategy and Hwang's method in that it allows smoother speed changes, produces less jerk, allows safer working when there are short distances between humans and robots [34]- [36] and faster movement from the initial pose to the destination pose.…”

Section: B Comparative Simulationsmentioning

confidence: 94%

Collision-Free Speed Alteration Strategy for Human Safety in Human-Robot Coexistence Environments

Chan

Tsai

2020

IEEE Access

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This paper presents a novel real-time collision-free speed alteration strategy using a danger index and an elite real-coded genetic algorithm (ERGA) for environments in which humans and robots coexist or cooperate, in order to guarantee the safety of an operator who works with a collaborative robot. A danger index based on ellipsoid modeling of the operator and robot describes the degree of safety during human-robot interactions. The ERGA and a penalty function are used to solve the constrained nonlinear optimization problem to change the handling speed of the robot. Comparative simulation results show the superiority of the proposed method by comparing to two existing methods. The applicability of the proposed method is verified using two experiments involving a 6-DoF industrial manipulator with an EtherCAT network protocol, an RGB-D sensor and a real-time operation system. INDEX TERMS Collaborative robot, danger index, ellipsoid modeling, elite real-coded genetic algorithm (ERGA), human-robot coexistence, human-robot cooperation, RGB-D sensor, speed alteration.

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Section: B Comparative Simulationsmentioning

confidence: 94%

Collision-Free Speed Alteration Strategy for Human Safety in Human-Robot Coexistence Environments

Chan

Tsai

2020

IEEE Access

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“…By solving this optimisation problem with two equality constraints, we calculate the optimal state of the robot at each sampling time of the sensor, that is, the optimal trajectory of the robot ( q (t) , q (t+1) , ..., q (t+To) ) T . This optimisation problem can be solved by a method proposed in our previous study (Kanazawa et al, 2019).…”

Section: Trajectory Planningmentioning

confidence: 99%

“…The efficiency of the delivery task has been further improved by the predictions of the worker's movements in Tanaka et al (2012). The prediction-based concept has been enhanced by developing a new motion planning system which calculates the motion trajectory taking the uncertainty of prediction into account in Kanazawa et al (2019).…”

Section: Introductionmentioning

confidence: 99%

“…However, the human worker cannot always repeat the same motion. Since the delivery position is determined heuristically based on the pre-planned work schedule of the human worker in Tanaka et al (2012) and Kanazawa et al (2019), therefore the optimal delivery position may be different from the heuristically determined position. This could prevent the worker from smoothly carrying out the task after receiving the parts and tools from the robot.…”

Section: Introductionmentioning

confidence: 99%

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A real-time motion planning scheme for collaborative robots using HRI-based cost function

Kosuge

Fahad

Kinugawa

et al. 2021

IJMA

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In this paper, we propose a novel scheme for real-time motion planning of a collaborative robot that assists its fellow human worker in performing assembly tasks by delivering parts and tools to the worker. In the proposed scheme, the delivery position of the parts and tools is determined first, based on a cost function relating to the safety, visibility, and arm comfort terms of the worker by solving the non-convex optimisation problem in real-time using a random sampling-based algorithm in the vicinity of the worker's current position. Then, model predictive control (MPC)-based trajectory planner directly generates a collision-free robot trajectory from the current robot configuration to the delivery configuration under velocity and acceleration constraints of the robot. The proposed motion planning scheme is implemented in the actual collaborative robot system and the effectiveness of the proposed scheme is illustrated experimentally.

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“…It supports a worker executing assembly-line tasks under a vehicle body by delivering necessary tools and parts to the worker. It predicts the progress of the work being performed by a worker and delivers tools and parts to the worker when he/she needs them [9][10][11][12]. It was installed in an automobile assembly line, and its effectiveness for a real-life assembly process was also demonstrated [7].…”

Section: Introductionmentioning

confidence: 99%

Reactive motion planning using time-layered C-spaces for a collaborative robot PaDY

2021

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A reactive motion-planning for collaborative robots using the time-layered C-spaces (TLC-spaces) is proposed in this paper. First, the time-augmented C-space (TAC-space) is introduced. TAC-space is an implementation of the configuration-time space with multiple moving obstacles [Latombe JC. Robot motion planning. Kluwer Academic; 1991. p. 22, 23]. The TAC-space is obtained by stacking the current and predicted future C-spaces along the time axis using predicted motions of the obstacles. Then, TLC-spaces is constructed as the collection of only those C-spaces in the TAC-space that are relevant to the motion planning with moving obstacles. The trajectory that reaches the goal configuration at the specified target time is generated under dynamic constraints including robot velocity and acceleration. We focus on a collaborative robot, PaDY, whose task is to deliver tools and parts to the worker in a factory. Similar to an actual assembly process in an automobile production system, six scenarios are selected for the evaluation of the proposed motion planning method. The simulation results using the real-life motion of workers show that the computation time required for the proposed motion planning using TLC-spaces is shorter than that of our previous method using TACspace. The experimental results show that the proposed method is applicable to PaDY in human environments.

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Adaptive Motion Planning for a Collaborative Robot Based on Prediction Uncertainty to Enhance Human Safety and Work Efficiency

Cited by 100 publications

References 37 publications

Collision-Free Speed Alteration Strategy for Human Safety in Human-Robot Coexistence Environments

Collision-Free Speed Alteration Strategy for Human Safety in Human-Robot Coexistence Environments

A real-time motion planning scheme for collaborative robots using HRI-based cost function

Reactive motion planning using time-layered C-spaces for a collaborative robot PaDY

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