Abstract
Robotic technology is acquiring more attention now. COVID-19 pandemic brought a large change within short span of time making social distancing among everyone. High safety considerations have to be established everywhere, and in case of hospitals, it is necessary. In order to control a robot, we have to go deep into its control strategies. Control strategy is the major section of robot that makes a robot self-stabilized and helps to control its position and thereby reducing the error. In this chapter, the control strategy and machine learning approach in robot are discussed. Control strategy discussed here helps to ensure the trajectory tracking by back stepping technique and by using sliding mode control (SMC). It helps to achieve the velocity convergence and balancing the robots. In SMC, there is presence of chattering, and other intelligent technique is also discussed to reduce this chattering phenomenon. Those intelligent techniques are adaptive neuro fuzzy interference system (ANFIS) and neuro-sliding mode control scheme. Also, machine learning (ML) which is a part of artificial intelligence (AI) is also discussed here. This chapter mainly focusing on the idea of two-wheeled balancing robot with SMC and back stepping controller along with information about machine learning technology
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Amritha Ashok, K., Savy, A., Shijoh, V., Shaw, R.N., Ghosh, A. (2021). Hospital Assistance Robots Control Strategy and Machine Learning Technology. In: Bianchini, M., Simic, M., Ghosh, A., Shaw, R.N. (eds) Machine Learning for Robotics Applications. Studies in Computational Intelligence, vol 960. Springer, Singapore. https://doi.org/10.1007/978-981-16-0598-7_3
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