VACNA: Visibility-Aware Cooperative Navigation With Application in Inventory Management

This letter presents an online trajectory planning algorithm for an Unmanned Aerial Vehicle (UAV) to autonomously scan warehouse racks for inventory management. Our main motivation is to make small-sized UAVs with limited computing and sensing hardware capable of reliably performing the scanning task in cluttered environments. To this end, we propose a cooperative system where an Unmanned Ground Vehicle (UGV) guides the UAV using the novel template of visibility-aware cooperative navigation (VACNA). We propose a Cross-Entropy Method (CEM) based approach for solving the trajectory optimization underpinning VACNA. In particular, our CEM projects sampled vehicle trajectories onto the constraint sets before evaluating the cost functions. We further learn a deep generative model in the form of a Conditional Variational Autoencoder (CVAE) from expert demonstrations to warm-start our optimizer. We improve the state-of-the-art in the following respects. First, we present a detailed analysis of the role of our proposed cost and constraint functions for cooperative occlusion-free navigation. Second, we compare our custom CEM optimizer with conventional variants and show significantly reduced collision and occlusion rates. Finally, our CVAE initialization allows our optimizer to operate with smaller batch sizes and achieve real-time performance even on embedded hardware devices like NVIDIA Jetson Xavier.