A Technique Based on Supervised Machine Learning for the Generation of Regulatory Barrier Components in Robotics

Authors

  • Trapty Agarwal Maharishi University of Information Technology, Lucknow, India -226036
  • Sanjeev Kumar Mandal Jain (Deemed to be University), Bangalore, Karnataka, India
  • Vijay Kumar Pandey Vivekananda Global University, Jaipur
  • Jaimine Vaishnav Department of ISME, ATLAS SkillTech University, Mumbai, Maharashtra, India
  • Abhinav Mishra Chitkara University, Rajpura- 140417, Punjab, India

Keywords:

Regulatory Barrier Components, Sensor Data, Deep- Antneuro Colonynet (D-ANCN), Robot Operating System (ROS), Machine learning (ML)

Abstract

Regulatory barrier components are mathematical compositions to ensure the security of robotic systems. Real-time performance with instantaneous control synthesis requirements may be accomplished for robots by integrating them in quadratic programming as restrictions optimization trouble. The safe zones occasionally need to be approximated online from sensor data, even though prevalent use has assumed a complete understanding of the safety barrier operations. The relevant barrier function in these circumstances has to be created online. The learning approach for predicting control barrier functions from sensor data is described in this research. This allows the system to operate safely in regions of unknown state space. The barrier function definition, in this case, is given by a Deep-AntNeuro ColonyNet (D-ANCN) classifier based on sets of safe and dangerous states gleaned from sensor readings. There are theoretical safety assurances offered. Results from experimental simulations using the Robot Operating System (ROS) to demonstrate the safe operation of a unidirectional robot using LiDAR.

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Published

24.03.2024

How to Cite

Agarwal, T. ., Mandal, S. K. ., Pandey, V. K. ., Vaishnav, J. ., & Mishra, A. . (2024). A Technique Based on Supervised Machine Learning for the Generation of Regulatory Barrier Components in Robotics. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 822–828. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5215

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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