Toward Verifiable Real-Time Obstacle Motion Prediction for Dynamic Collision Avoidance
This addresses the need for reliable collision avoidance in UAV applications where obstacle trajectories are non-linear or unknown, representing an incremental improvement over existing methods.
The paper tackles the problem of dynamic collision avoidance for UAVs by proposing an LSTM-based method for real-time obstacle motion prediction and a Nonlinear Probabilistic Velocity Obstacle to select collision-free velocities with a given probability, demonstrating in simulation that it avoids collisions where state-of-the-art methods fail.
Next generation Unmanned Aerial Vehicles (UAVs) must reliably avoid moving obstacles. Existing dynamic collision avoidance methods are effective where obstacle trajectories are linear or known, but such restrictions are not accurate to many real-world UAV applications. We propose an efficient method of predicting an obstacle's motion based only on recent observations, via online training of an LSTM neural network. Given such predictions, we define a Nonlinear Probabilistic Velocity Obstacle (NPVO), which can be used select a velocity that is collision free with a given probability. We take a step towards formal verification of our approach, using statistical model checking to approximate the probability that our system will mispredict an obstacle's motion. Given such a probability, we prove upper bounds on the probability of collision in multi-agent and reciprocal collision avoidance scenarios. Furthermore, we demonstrate in simulation that our method avoids collisions where state-of-the-art methods fail.