Collision Avoidance for Complex and Dynamic Obstacles : A study for warehouse safety

Today a group of automated guided vehicles at Toyota Material Handling Manufacturing Sweden detect and avoid objects primarily by using 2D-LiDAR, with shortcomings being the limitation of only scanning the area in a 2D plane and missing objects close to the ground. Several dynamic obstacles exist in the environment of the vehicles. Protruding forks are one such obstacle, impossible to detect and avoid with the current choice of sensor and its placement. This thesis investigates possible solutions and limitations of using a single RGB camera for obstacle detection, tracking, and avoidance. The obstacle detection uses the deep learning model YOLOv5s. A solution for semi-automatic data gathering and labeling is designed, and pre-trained weights are chosen to minimize the amount of labeled data needed. Two different approaches are implemented for the tracking of the object. The YOLOv5s detection is the foundation of the first, where 2D-bounding boxes are used as measurements in an Extended Kalman Filter (EKF). Fiducial markers build up the second approach, used as measurements in another EKF. A state lattice motion planner is designed to find a feasible path around the detected obstacle. The chosen graph search algorithm is ARA*, designed to initially find a suboptimal path and improve it if time allows. The detection works successfully with an average precision of 0.714. The filter using 2D-bounding boxes can not differentiate between a clockwise and counterclockwise rotation, but the performance is improved when a measurement of rotation is included. Using ARA* in the motion planner, the solution sufficiently avoids the obstacles