Tracking Multiple Autonomous Ground Vehicles Using Motion Capture System Operating in a Wireless Network.

The method examines the challenging issues in tracking multiple autonomous ground vehicles (AGVs) using a motion capture system (MoCap) accessible by a wireless network such as standard IEEE 802.11a WIFI protocol. A state-of-art technology such as global positioning system may have limitations in their accuracy and restricted lines of sight. Tracking in these environments entail various complexities such as target (AGV) occlusion, clutter, and the electromagnetic interference that can interrupt communication between AGV and ground-based control station. We present a novel idea that exploits IR signals emitted by the MoCap to fetch position data reflected from AGVs. MoCap uses the Motive software to manipulate the position data necessary for the tracking filter. This method adopted the fixed-interval smoothing based on the joint integrated track splitting filter (FIsJITS) for detecting and tracking the vehicles. FIsJITS obtains track state estimation in both forward and backward directions within fixed time measurement intervals. The multi-track backward predictions are fused in the forward-path track to obtain a-priori smoothing predictions, followed by a smoothing state estimation. This approach also calculates the smoothing target existence probability (TEP) to reinforce target detection, allowing a tracking system to simultaneously track multiple vehicles efficiently within cluttered environments. For a seamless data processing, we utilize the WIFI through a wireless access point (WAP) to transfer the data from MoCap to the computer system where Motive and tracking system softwares are running simultaneously. We conducted the real-time experiments to demonstrate the tracking performance of the proposed AGV system outperforming existing algorithms.