港城大等团队突破连续体机器人控制难题，让柔性臂实现毫米级精准定位！

Core Viewpoint - Continuous robots exhibit great potential in fields such as robotic surgery and narrow space detection, but precise control remains a significant challenge. Recent breakthroughs by research teams from City University of Hong Kong and Hefei University of Technology have applied Kalman filtering technology to enhance the online control precision of these robots [1][2]. Group 1: Continuous Robots and Control Challenges - Continuous robots possess infinite degrees of freedom and adaptability, making them difficult to control accurately due to their deformable nature, akin to controlling a cooked noodle [4]. - Traditional rigid-link robots have simpler control mechanisms, while continuous robots face challenges from large deformations, friction effects, and inherent non-linear characteristics [4][5]. - The research team designed a lightweight robot with a complex internal structure, consisting of three flexible segments, each with five spacer disks and one drive disk, weighing only 8.4 grams [5]. Group 2: Control Methodology - The team utilized a piecewise constant curvature (PCC) model for initial control, which, while computationally efficient, resulted in position errors exceeding 1.6 mm and angle errors over 1.4 degrees, unacceptable for high-precision applications [7]. - Instead of developing a more complex model, the team innovatively employed the Kalman filter to allow the robot to self-correct during motion, estimating and compensating for errors in real-time [8][9]. - The control system operates at a frequency of 20 Hz, integrating steps such as obtaining end pose, calculating model Jacobians, estimating and compensating for Jacobian errors, and generating control commands [11]. Group 3: Experimental Validation - The research team conducted three trajectory tracking experiments and two disturbance resistance tests, demonstrating the effectiveness of the new method [12]. - In the first experiment, the root mean square error (RMSE) in the x-direction improved from 1.6 mm to 1.1 mm, and in the y-direction from 2.3 mm to 2.1 mm, showcasing significant enhancements in tracking precision [12][14]. - The second experiment focused on attitude control, achieving a reduction in RMSE from 2.1 degrees to 1.5 degrees, while maintaining position accuracy [14]. - The robustness of the method was further validated through disturbance tests, where the robot maintained performance even under significant load changes [15]. Group 4: Innovation and Future Prospects - The research combines model-driven and data-driven approaches, leveraging the strengths of both to enhance control precision while maintaining computational efficiency [17]. - The method's advantages include no need for offline data collection, high computational efficiency, and robustness against external disturbances, indicating strong potential for practical applications [17]. - Future research directions include incorporating dynamic effects and expanding to three-dimensional motion to improve estimation accuracy and applicability [17].