  Autonomous mobile manipulation robots that can collect trolleys are widely
used to liberate human resources and fight epidemics. Most prior robotic
trolley collection solutions only detect trolleys with 2D poses or are merely
based on specific marks and lack the formal design of planning algorithms. In
this paper, we present a novel mobile manipulation system with applications in
luggage trolley collection. The proposed system integrates a compact hardware
design and a progressive perception and planning framework, enabling the system
to efficiently and robustly collect trolleys in dynamic and complex
environments. For the perception, we first develop a 3D trolley detection
method that combines object detection and keypoint estimation. Then, a docking
process in a short distance is achieved with an accurate point cloud plane
detection method and a novel manipulator design. On the planning side, we
formulate the robot's motion planning under a nonlinear model predictive
control framework with control barrier functions to improve obstacle avoidance
capabilities while maintaining the target in the sensors' field of view at
close distances. We demonstrate our design and framework by deploying the
system on actual trolley collection tasks, and their effectiveness and
robustness are experimentally validated.
