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 multiagent and reciprocal collision avoidance scenarios. Furthermore, we demonstrate in simulation that our method avoids collisions where state-of-the-art methods fail.
Given probabilistic predictions of obstacle movement, we propose an uncertainty-aware multi-agent dynamic collision avoidance algorithm based on Nonlinear Probabilistic Velocity Obstacles (NPVO), a novel extension of existing velocity obstacle notions. These include Probabilistic Velocity Obstacles, which provide an uncertainty-aware policy in static environments @cite_1 , and Nonlinear Velocity Obstacles @cite_10 , which can guarantee collision avoidance for obstacles moving along known trajectories. Our NPVO, which considers the future behavior of an obstacle in a probabilistic sense, is a generalization of these notions.
@cite_10 @cite_1
2120794503 2157304242
This paper generalizes the concept of velocity obstacles given by (1998) to obstacles moving along arbitrary trajectories. We introduce the nonlinear velocity obstacle, which takes into account the shape, velocity and path curvature of the moving obstacle. The nonlinear v-obstacle allows selecting a single avoidance maneuver (if one exists) that avoids any number of obstacles moving on any known trajectories. For unknown trajectories, the nonlinear v-obstacles can be used to generate local avoidance maneuvers based on the current velocity and path curvature of the moving obstacle. This elevates the planning strategy to a second order method, compared to the first order avoidance using the linear v-obstacle, and zero order avoidance using only position information. Analytic expressions for the nonlinear v-obstacle are derived for general trajectories in the plane. The nonlinear v-obstacles are demonstrated in a complex traffic example. Most of present work for autonomous navigation in dynamic environment doesn't take into account the dynamics of the obstacles or the limits of the perception system. To face these problems we applied the probabilistic velocity obstacle (PVO) approach (Kluge and Prassler, 2004) to a dynamic occupancy grid. The paper presents a method to estimate the probability of collision where uncertainty in position, shape and velocity of the obstacles, occlusions and limited sensor range contribute directly to the computation. A simple navigation algorithm is then presented in order to apply the method to collision avoidance and goal driven control. Simulation results show that the robot is able to adapt its behaviour to the level of available knowledge and navigate safely among obstacles with a constant linear velocity. Extensions to non-linear, non-constant velocities are proposed.