Autonomous Medical Robot Trajectory Planning with Local Planner Time Elastic Band Algorithm

Robots have made significant contributions across various industries due to their efficiency and effectiveness. However, indoor navigation remains challenging due to complex environments and sensor signal interference. Changes in indoor conditions and the limited range of GPS signals necessitate the development of an accurate and efficient indoor robot navigation system. This study aims to create an autonomous indoor navigation system for medical robots using sensors such as Marvelmind, LiDAR, IMU, and an odometer, along with the Time Elastic Band (TEB) local planning algorithm to detect dynamic obstacles. The algorithm’s performance is evaluated using metrics like path length, duration, speed smoothness, path smoothness, Mean Squared Error (MSE), and positional error. In the test arena, TEB demonstrated superior efficiency with a path length of 155.55 m, 9.83 m shorter than the Dynamic Window Approach (DWA), which covered 165.38 m, and had a lower yaw error of 0.012 radians. TEB outperformed DWA in terms of speed smoothness, path smoothness, and MSE. In the Sterile Room Arena, TEB had an average path length of 14.84 m, slightly longer than DWA’s 14.32 m, but TEB navigated 2.82 s faster. Additionally, TEB showed better speed and path smoothness. In the Obstacle Room Arena, TEB recorded an average path length of 21.96 m in 57.3 s, outperforming DWA, which covered 23.44 m in 61 s, with better results in MSE, speed smoothness, and path smoothness, highlighting superior path consistency. These findings indicate that the TEB algorithm is an effective choice as a local planner in dynamic hospital environments.