Lyapunov vector-based formation tracking control for unmanned aerial vehicles with obstacle/collision avoidance

Abstract

This paper presents a formation control method to solve the moving target tracking problem for a swarm of unmanned aerial vehicles (UAVs). The formation is achieved by the artificial potential field with both attractive and repulsive forces, and each UAV in the swarm will be driven into a leader-centered spherical surface. The leader is controlled by the attractive force by the moving target, while the Lyapunov vectors drive the leader UAV to a fly-around circle of the target. Furthermore, the rotational vector-based potential field is applied to achieve the obstacle avoidance of UAVs with smooth trajectories and avoid the local optima problem. The efficiency of the developed control scheme is verified by numerical simulations in four scenarios.

Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

“…Currently, with the continuous advancement of communication technology, the trend of completing tasks of civilian and military via unmanned aerial vehicle (UAV) formation is growing year by year (Ali and Zhangang, 2021; Chang et al, 2020; Hu et al, 2020), where the obstacle avoidance ability of formation is a key to flying. Studying the obstacle avoidance control of formation can improve the coordination, intelligence, and autonomy of UAV, which can improve the operational efficiency of UAV formation in military fields such as coordinated search, electronic countermeasures, and cluster attacks.…”

UAV Formation Obstacle Avoidance Control Algorithm Based on Improved Artificial Potential Field and Consensus

“…Hu et al proposed a distributed collision avoidance and obstacle avoidance control method based on IAPF and Voronoi constraints to enable UAVs to avoid collisions while avoiding obstacles smoothly [20]. Chang et al proposed an IAPF based on the rotation vector, which can plan a collision-free and efficient obstacle avoidance trajectory for the UAV [21]. Although the potential field functions designed in the above references can achieve the effect of avoiding obstacles, the gains in the potential field function are all in the form of fixed coefficients, and multiple debugging is required to determine more reasonable parameters, which increases the difficulty of debugging.…”

“…According to the above discussion and analysis, it can be seen that the research of multi-UAV formation obstacle avoidance and consensus control has made certain progress, and the method of combining APF and consensus theory has been widely used in the field of formation avoidance control, but existing research still exists several questions. For example, the UAV is not well able to adapt to the complex environment with static and dynamic obstacles because the gain in the designed potential field function in [18][19][20][21] is still in the form of a fixed coefficient. UAVs cannot avoid dynamic obstacles since the movement velocity of dynamic obstacles is not considered in the improved repulsive potential field functions of these references.…”

Over-flight and standoff tracking of a ground target with a fixed-wing unmanned aerial vehicle based on a unified sliding mode guidance law

“…During the past few decades, many research groups have studied the issues associated with target tracking. The Lyapunov vector field guidance (LVFG) (Frew et al, 2008; Lawrence et al, 2008; Oh and Kim, 2019) and improved LVFG (Chang et al, 2020; Oh et al, 2015; Pothen and Ratnoo, 2017; Ye et al, 2020) allowed UAV to fly around a ground target at a specified distance. LVFG method is simple to realize, but its convergence speed is slow.…”