Abstract
With the various applications of unmanned aerial vehicles (UAVs), the number of UAVs will increase in limited airspace, leading to an increased risk collision. To reduce such potential risk, this work proposes a collision avoidance strategy for UAVs using an enhanced potential field (EPF) approach in cluttered three-dimensional urban environments. Using the EPF formulated in a two-dimensional environment, the avoidance maneuvers for both horizontal and vertical planes are generated by introducing rotation matrices, and these maneuvers are combined by applying a weighting factor. The numerical simulations with various meaningful scenarios are conducted to validate the performance of the proposed approach. To mimic practical situations, UAV dynamics and sensor limitations were considered. The simulation results show that the proposed approach provides an efficient, reliable, and collision-free path without local minima and unreachable goal issues.
Highlights
Unmanned aerial vehicles (UAVs) have recently been used in several fields to perform various missions, such as search and rescue [1], surveillance [2], monitoring [3], delivery [4], and inspection [5]
The parameters for the proposed collision avoidance (CA) algorithm listed in Table 2, and α and γ values selected in Section 3.1 were used
This paper proposes a novel collision avoidance (CA) approach that reformulates the repulsive potential function of the artificial potential field
Summary
Unmanned aerial vehicles (UAVs) have recently been used in several fields to perform various missions, such as search and rescue [1], surveillance [2], monitoring [3], delivery [4], and inspection [5]. Zhang et al [13] revised the repulsive potential field formulation by multiplying an additional term and combining it with the rolling window approach to resolve the APF’s two issues, and the intermediate target position was used to avoid obstacles. Several researchers have proposed various techniques for resolving the APF’s issues, the aforementioned approaches have been applied in a two-dimensional (2D) environment and are not suitable in a situation where the 3D motion of UAVs has to be considered. The quadcopter’s position and velocity vectors expressed in the body frame are defined as r ∈ R3 and v ∈ R3, respectively. The quadcopter’s angular velocity vector with respect to the inertial frame is defined by ω ∈ R3. Using Equation (1), the position and velocity vector expressed in the inertial frame are defined as.
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