Area-Coverage Planning for Spray-based Surface Disinfection with a Mobile Manipulator

Thakar, Shantanu; Malhan, Rishi K.; Bhatt, Prahar M.; Gupta, Satyandra K.

doi:10.1016/j.robot.2021.103920

Cited by 13 publications

(10 citation statements)

References 91 publications

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“…The developed system primarily focused on the integration of multiple technologies, such as the Internet of Things, SLAM, hand gesture recognition, and navigation. Thakar et al (2021) developed an areacoverage planning algorithm for a spray-based disinfection robot to compute a path for the nozzle to follow to completely disinfect surfaces. The remote operator needs to select the area to be disinfected and extract the corresponding point cloud for path calculation.…”

Section: Related Studies On Robotic Disinfectionmentioning

confidence: 99%

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Recognizing object surface materials to adapt robotic disinfection in infrastructure facilities

2022

Computer aided Civil Eng

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Existing disinfection robots are not intelligent enough to adapt their actions to object surface materials for precise and effective disinfection. To address this problem, a new framework is developed to enable the robot to recognize various object surface materials and to adapt its disinfection methods to be compatible with recognized object surface materials. Specifically, a new deep learning network is proposed that integrates multi‐level and multi‐scale features to classify the materials on contaminated surfaces requiring disinfection. The infection risk of contaminated surfaces is computed to choose the appropriate disinfection modes and parameters. The developed material recognition method demonstrates state‐of‐the‐art performance, achieving an accuracy of 92.24% and 91.84% on the Materials in Context Database validation and test datasets, respectively. The proposed method was also tested and evaluated in the context of healthcare facilities, where the material classification achieved an accuracy of 89.09%, and the adaptive robotic disinfection was successfully implemented.

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Section: Related Studies On Robotic Disinfectionmentioning

confidence: 99%

“…Thakar et al. (2021) developed an area‐coverage planning algorithm for a spray‐based disinfection robot to compute a path for the nozzle to follow to completely disinfect surfaces. The remote operator needs to select the area to be disinfected and extract the corresponding point cloud for path calculation.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Recognizing object surface materials to adapt robotic disinfection in infrastructure facilities

2022

Computer aided Civil Eng

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“…These results were extended by the authors in [17], where a multistaged warm started motion planner for a group of robots (up to three manipulators or a mobile manipulator with two robotic arms) was presented, including a deep analysis on the best sequence of introduction of the problem constraints (position, velocity, orientation of the end effector, collisions). The same multi-staged warm started motion planner was also used in [18] for surface disinfection with mobile manipulators, generating first a path to cover the goal area by means of a branch and bound-based tree search. This generated path was used as a constraint to generate the mobile manipulator actuators trajectory through the motion planner.…”

Section: Introductionmentioning

confidence: 99%

Multi-stage warm started optimal motion planning for over-actuated mobile platforms

Delgado

Pérez-del-Pulgar

Azkarate

et al. 2023

Intel Serv Robotics

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This work presents a computationally lightweight motion planner for over-actuated platforms. For this purpose, a general state-space model for mobile platforms with several kinematic chains is defined, which considers dynamics, nonlinearities and constraints. The proposed motion planner is based on a sequential multi-stage approach that takes advantage of the warm start on each step. Firstly, a globally optimal and smooth 2D/3D trajectory is generated using the Fast Marching Method. This trajectory is fed as a warm start to a sequential linear quadratic regulator that is able to generate an optimal motion plan without constraints for all the platform actuators. Finally, a feasible motion plan is generated considering the constraints defined in the model. In this respect, the sequential linear quadratic regulator is employed again, taking the previously generated unconstrained motion plan as a warm start. The motion planner has been deployed into the Exomars Testing Rover of the European Space Agency. This rover is an Ackermann-capable planetary exploration testbed that is equipped with a robotic arm. Several experiments were carried out demonstrating that the proposed approach speeds up the computation time and increases the success ratio for a martian sample retrieval mission, which can be considered as a representative use case of goal-constrained trajectory generation for an over-actuated mobile platform.

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“…With the continuous development of robot technology, robots gradually appear in a variety of tasks to improve the e ciency of task or reduce labor costs. As a part of robot application, coverage task has been widely used in area cleaning [1,2], disaster detection [3], postdisaster rescue, and other elds. In the rst place, researchers focus on coverage path planning (CPP) for individual robot and achieve good results [4,5].…”

Section: Introductionmentioning

confidence: 99%

Multirobot Coverage Path Planning Based on Deep Q-Network in Unknown Environment

Zhao

Dian

2022

Journal of Robotics

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Aiming at the problems of security, high repetition rate, and many restrictions of multirobot coverage path planning (MCPP) in an unknown environment, Deep Q-Network (DQN) is selected as a part of the method in this paper after considering its powerful approximation ability to the optimal action value function. Then, a deduction method and some environments handling methods are proposed to improve the performance of the decision-making stage. The deduction method assumes the movement direction of each robot and counts the reward value obtained by the robots in this way and then determines the actual movement directions combined with DQN. For these reasons, the whole algorithm is divided into two parts: offline training and online decision-making. Online decision-making relies on the sliding-view method and probability statistics to deal with the nonstandard size and unknown environments and the deduction method to improve the efficiency of coverage. Simulation results show that the performance of the proposed online method is close to that of the offline algorithm which needs long time optimization, and the proposed method is more stable as well. Some performance defects of current MCPP methods in an unknown environment are ameliorated in this study.

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Area-Coverage Planning for Spray-based Surface Disinfection with a Mobile Manipulator

Cited by 13 publications

References 91 publications

Recognizing object surface materials to adapt robotic disinfection in infrastructure facilities

Recognizing object surface materials to adapt robotic disinfection in infrastructure facilities

Multi-stage warm started optimal motion planning for over-actuated mobile platforms

Multirobot Coverage Path Planning Based on Deep Q-Network in Unknown Environment

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