Collision Avoidance of Hexacopter UAV Based on LiDAR Data in Dynamic Environment

Abstract

A reactive three-dimensional maneuver strategy for a multirotor Unmanned Aerial Vehicle (UAV) is proposed based on the collision cone approach to avoid potential collision with a single moving obstacle detected by an onboard sensor. A Light Detection And Ranging (LiDAR) system is assumed to be mounted on a hexacopter to obtain the obstacle information from the collected point clouds. The collision cone approach is enhanced to appropriately deal with the moving obstacle with the help of a Kalman filter. The filter estimates the position, velocity, and acceleration of the obstacle by using the LiDAR data as the associated measurement. The obstacle state estimate is utilized to predict the future trajectories of the moving obstacle. The collision detection and obstacle avoidance maneuver decisions are made considering the predicted trajectory of the obstacle. Numerical simulations, including a Monte Carlo campaign, are conducted to verify the performance of the proposed collision avoidance algorithm.

Reactive Collision Avoidance Algorithm for UAV Using Bounding Tube Against Multiple Moving Obstacles

“…where t d and t p are time step parameters introduced in Table 1. The rest of the parameters of UAV, Kalman filter, and collision avoidance algorithm used in simulations are identical to those used in [24].…”

“…This allows the UAV to move to the desired position via attitude control based on time-scale separation between the translational and the rotational motion. The position loop developed in [24] produces the moment command based on a Proportional-Derivative (PD) control of the position tracking error. The guidance algorithm that will be developed in the present study to perform reactive collision avoidance produces the desired position for feeding into the position controller.…”

“…In Simulation I, the proposed collision avoidance algorithm is compared to the previous algorithm [24] in the presence of a single obstacle. The initial position of the UAV and the goal point are set to [0 0 0] T and [30 0 0] T in the NED coordinate system, respectively.…”

“…A new cluster is assigned or an existing cluster is removed, depending on the circumstances. Numerical simulations considering maneuvering obstacles show that the proposed algorithm based on the bounding tube successfully avoids collision by producing aiming point candidates that are not invaded by the movement of the obstacle, whereas the previous algorithm developed in [24] based only on the instantaneous bounding box results in failure. Simulation studies also show that the algorithm appropriately handles multiple dynamic obstacles with the proposed cluster tracking method.…”

“…Chakravarthy and Ghose [22] applied the collision cone approach which is appropriate for dynamic obstacle environment and also generalized the collision cone concept to three-dimensional environment [23]. Many other studies extended the collision cone concept to the vision-based collision avoidance [24]- [29], collision avoidance in dynamic environment [30], [31], and formation flight [32]. Despite the significant benefits, most of the studies on the collision cone approach consider only a single obstacle.…”