MoMo: Mouse-Based Motion Planning for Optimized Grasping to Declutter Objects Using a Mobile Robotic Manipulator

Senthil Kumar Jagatheesaperumal, Varun Prakash Rajamohan, Abdul Khader Jilani Saudagar, Abdullah AlTameem, Muhammad Sajjad and Khan Muhammad ()
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Senthil Kumar Jagatheesaperumal: Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, India
Varun Prakash Rajamohan: Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, India
Abdul Khader Jilani Saudagar: Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Abdullah AlTameem: Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Muhammad Sajjad: Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjovik, Norway
Khan Muhammad: Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Republic of Korea

Mathematics, 2023, vol. 11, issue 20, 1-25

Abstract: The aim of this study is to develop a cost-effective and efficient mobile robotic manipulator designed for decluttering objects in both domestic and industrial settings. To accomplish this objective, we implemented a deep learning approach utilizing YOLO for accurate object detection. In addition, we incorporated inverse kinematics to facilitate the precise positioning, placing, and movement of the robotic arms toward the desired object location. To enhance the robot’s navigational capabilities within the environment, we devised an innovative algorithm named “MoMo”, which effectively utilizes odometry data. Through careful integration of these algorithms, our goal is to optimize grasp planning for object decluttering while simultaneously reducing the computational burden and associated costs of such systems. During the experimentation phase, the developed mobile robotic manipulator, following the MoMo path planning strategy, exhibited an impressive average path length coverage of 421.04 cm after completing 10 navigation trials. This performance surpassed that of other state-of-the-art path planning algorithms in reaching the target. Additionally, the MoMo strategy demonstrated superior efficiency, achieving an average coverage time of just 16.84 s, outperforming alternative methods.

Keywords: robot manipulator; AI autonomous robot; YOLO; MoMo; object localization; inverse kinematics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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