Human-Aware Robot Collaborative Task Planning Using Artificial Potential Field and DQN Reinforcement Learning

Prakash, J; Altnji, S; Thiyagarajan, K; Nanda, JS; Biswas, A; Chowdhury, AR

Human-Aware Robot Collaborative Task Planning Using Artificial Potential Field and DQN Reinforcement Learning

Prakash, J Altnji, S Thiyagarajan, K

Nanda, JS Biswas, A Chowdhury, AR

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE ACCESS, 2025, 13, pp. 140889-140899
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Prakash, J
dc.contributor.author	Altnji, S
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711
dc.contributor.author	Nanda, JS
dc.contributor.author	Biswas, A
dc.contributor.author	Chowdhury, AR
dc.date.accessioned	2025-09-03T08:00:33Z
dc.date.available	2025-09-03T08:00:33Z
dc.date.issued	2025
dc.identifier.citation	IEEE ACCESS, 2025, 13, pp. 140889-140899
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/189633
dc.description.abstract	This paper presents a novel way for Robot-Robot-Human interaction in a shared workspace for collaborative tasks and uses a multi-modal means of communication that includes hand gestures, voice commands, end-effector gestures, and marker tracking. The system consists of a human operator working along with a Task robot (UR5) and a helper robot (OpenManipulatorX) to perform assembly and disassembly tasks. A Deep Q Network (DQN) reinforcement learning model is used to train the robot to perform the goal reaching task while avoiding obstacles to ensure safety. The DQN algorithm makes use of the end-effector position and the relative positions with the goal and obstacles to train a policy that guides the robot arm safely. Then 4 different training models are created and their ability to avoid obstacles and reach the goal are compared along with the point-to-point Bezier interpolation path planning method in different scenarios such as varying height, size, and number of obstacles. The proposed system has been simulated and then experimentally validated. Experimental results show that DQN trained model performed better than Bezier interpolation in reaching the final goal position with an accuracy of 74mm while avoiding obstacles at the same time in a shared environment. It is also observed that of the different trained models, the model with a larger action space and reduced observation space gave better results compared to others in terms of accuracy and goal completion rate. Also, from experimental data its observed that Improved Artificial Potential Field (IAPF) only took 4.7s as the median time to reach the goal whereas Goal Directed Approach (GDA) took 7.62s and Rapidly Exploring Random Tree Star (RRT*) took 6.22s in different scenarios.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE ACCESS
dc.relation.isbasedon	10.1109/ACCESS.2025.3595995
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Human-Aware Robot Collaborative Task Planning Using Artificial Potential Field and DQN Reinforcement Learning
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-09-03T08:00:31Z
pubs.publication-status	Published
pubs.volume	13

Abstract:

This paper presents a novel way for Robot-Robot-Human interaction in a shared workspace for collaborative tasks and uses a multi-modal means of communication that includes hand gestures, voice commands, end-effector gestures, and marker tracking. The system consists of a human operator working along with a Task robot (UR5) and a helper robot (OpenManipulatorX) to perform assembly and disassembly tasks. A Deep Q Network (DQN) reinforcement learning model is used to train the robot to perform the goal reaching task while avoiding obstacles to ensure safety. The DQN algorithm makes use of the end-effector position and the relative positions with the goal and obstacles to train a policy that guides the robot arm safely. Then 4 different training models are created and their ability to avoid obstacles and reach the goal are compared along with the point-to-point Bezier interpolation path planning method in different scenarios such as varying height, size, and number of obstacles. The proposed system has been simulated and then experimentally validated. Experimental results show that DQN trained model performed better than Bezier interpolation in reaching the final goal position with an accuracy of 74mm while avoiding obstacles at the same time in a shared environment. It is also observed that of the different trained models, the model with a larger action space and reduced observation space gave better results compared to others in terms of accuracy and goal completion rate. Also, from experimental data its observed that Improved Artificial Potential Field (IAPF) only took 4.7s as the median time to reach the goal whereas Goal Directed Approach (GDA) took 7.62s and Rapidly Exploring Random Tree Star (RRT*) took 6.22s in different scenarios.

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http://hdl.handle.net/10453/189633