Abstract
To improve the accuracy of common intelligent algorithms when identifying the parameters of geometric error in medical robots, this paper proposes an improved beetle antennae search algorithm (RWSAVSBAS). We first establish a model for the kinematic error in medical robots, and then add the random wandering behavior of the wolf colony algorithm to the search process of the beetle antennae search algorithm to strengthen its capability for local search. Following this, we improve the global convergence ability of the beetle antennae search algorithm by using the simulated annealing algorithm. We compare the accuracy of end positioning of the proposed algorithm with the frog-jumping algorithm and the beetle antennae search algorithm with variable step length through simulations. The results show that the proposed algorithm has a higher accuracy of convergence, and can significantly improve the accuracy of end positioning of the medical robot.
Highlights
Introduction of Medical Robot Based onImprovedAdvances in Artificial Intelligence have penetrated into all aspects of healthcare in recent years, such as the combination of deep learning with smartphones for the remote monitoring of human activities to provide medical assistance in telemedicine, as reported by Wen et al, and the use of long-short term memory-Recurrent Neural Networks models in surgical robots to enhance their remote manipulation capabilities
The use of medical robots can substantially improve accuracy and controllability during surgical operations [3]. It eases the burden on medical personnel; in specific cases, medical robots can help ensure the safety of medical personnel, because they are resistant to radiation
We first establish the operational science model of the BH-7 robot, analyze the principle of RWSAVSBAS and its steps in geometric error calibration of medical robots, and through simulation experiments, the RWSAVSBAS and the Variable-Step Beetle Antennae Search Algorithm (VSBAS) proposed in this paper and Shuffled Frog Leading Algorithm (SFLA), and the experimental results show that the proposed algorithm has higher accuracy in recognition results and can significantly improve the end positioning accuracy of medical robots [15,16,17,18,19]
Summary
Advances in Artificial Intelligence have penetrated into all aspects of healthcare in recent years, such as the combination of deep learning with smartphones for the remote monitoring of human activities to provide medical assistance in telemedicine, as reported by Wen et al, and the use of long-short term memory-Recurrent Neural Networks models in surgical robots to enhance their remote manipulation capabilities. We combine the wandering behavior of the wolf pack algorithm (WPA) with the simulated annealing algorithm and BAS to propose a randomized wandering simulated annealingbased variable-step-length beetle antennae search algorithm (RWSAVSBAS) We use it to identify geometric errors in medical robots to improve their accuracy of localization. We first establish the operational science model of the BH-7 robot, analyze the principle of RWSAVSBAS and its steps in geometric error calibration of medical robots, and through simulation experiments, the RWSAVSBAS and the Variable-Step Beetle Antennae Search Algorithm (VSBAS) proposed in this paper and Shuffled Frog Leading Algorithm (SFLA), and the experimental results show that the proposed algorithm has higher accuracy in recognition results and can significantly improve the end positioning accuracy of medical robots [15,16,17,18,19]
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