Yongyao Li

In response to the issue of virus contamination in the cold-chain warehouse or hospital environment under the influence of the COVID-19, we propose the design work of a disinfection robot based on the UVC radiation mechanism using the low-computational path optimization at-the-edge. To build a surface disinfection robot with less computing power to generate a collision-free path with shorter total distance in studies, a 2D map is used as a graph-based approach to automatically generate a closed-loop disinfection path to cover all the accessible surfaces. The discrete disinfection points from the map are extracted with effective disinfection distances and sorted by a nearest-neighbor (NN) search over historical trajectory data and improved A * algorithm to obtain an efficient coverage path to all accessible boundaries of the entire area. The purpose of improved A * algorithm with NN is not to find the optimal path solution but to optimize one with reasonable computing power. The proposed algorithm enhances the path-finding efficiency by a dynamically weighted heuristic function and reduces the path turning angles, which improves the path smoothness significantly requiring less computing power. The Gazebo simulation is conducted, and the prototype disinfection robot has been built and tested in a real lab environment. Compared with the classic A * algorithm, the improved A * algorithm with NN has improved the path-finding efficiency and reduced the path length while covering the same area. Both the simulation and experimental results show that this approach can provide the design to balance the tradeoffs among the path-finding efficiency, smoothness, disinfection coverage, and computation resources.

This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm’s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m/s, and 9.87 mm at 4.2 m/s, and still works well with the angular velocity of 1.4 rad/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm’s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM.