Abstract
This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner.
Highlights
Featured Application: This work provides a generalization of the velocity obstacle (VO) collision avoidance strategy to nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments
S is defined as a series of discrete planes that rotate around the x-axis of the s-frame at Similar to two-dimensional velocity obstacle (2D-VO) methods, the unmanned aerial vehicles (UAV) needs to certain angles
We rely on the concept of the VO cone and the avoidance plane introduced in the previous sections to solve the avoidance velocity, mainly referring to Ref. [17], taking into account the kinematic and dynamic constraints of the fixed-wing UAV
Summary
Substantial research on motion planning for UAVs has been conducted, including sampling-based methods [5] and search-based methods [6]. VO methods were further extended to the system with non-holonomic constraints by testing the optimality of sampling control [14], by mapping between the holonomic speed and the nonholonomic control input [15] The latter guarantees a lower computational cost. Our method builds on the concept proposed in [17] which was successfully verified by experiments for collision avoidance with static obstacles [18] and extended to the dynamic system with nonholonomic constraints. (2) The dynamics of a fixed-wing UAV is represented as a second-order nonlinear differential equation with variable coefficients, which make it extremely difficult to directly derive a feasible velocity set or acceleration set, as described in [12,19,20]. In line with the conventional behavior-based approaches and similar to the approach proposed in [2], we decompose the overall mission into several rotations, which are parameterized by quaternions
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