Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios

TL;DR

An adaptive dynamic tracking and perching scheme for autonomous quadrotors to achieve tight integration with moving platforms and an efficient spatiotemporal trajectory optimization framework considerin g full state dynamics is presented.

Abstract

Perching on the moving platforms is a promising solution to enhance the endurance and operational range of quadrotors, which could benefit the efficiency of a variety of air-ground cooperative tasks. To ensure robust perching, tracking with a steady relative state and reliable perception is a prerequisite. This paper presents an adaptive dynamic tracking and perching scheme for autonomous quadrotors to achieve tight integration with moving platforms. For reliable perception of dynamic targets, we introduce elastic visibility-aware planning to actively avoid occlusion and target loss. Additionally, we propose a flexible terminal adjustment method that adapts the changes in flight duration and the coupled terminal states, ensuring full-state synchronization with the time-varying perching surface at various angles. A relaxation strategy is developed by optimizing the tangential relative speed to address the dynamics and safety violations brought by hard boundary conditions. Moreover, we take SE(3) motion planning into account to ensure no collision between the quadrotor and the platform until the contact moment. Furthermore, we propose an efficient spatiotemporal trajectory optimization framework considering full state dynamics for tracking and perching. The proposed method is extensively tested through benchmark comparisons and ablation studies. To facilitate the application of academic research to industry and to validate the efficiency of our scheme under strictly limited computational resources, we deploy our system on a commercial drone (DJI-MAVIC3) with a full-size sport-utility vehicle (SUV). We conduct extensive real-world experiments, where the drone successfully tracks and perches at 30~km/h (8.3~m/s) on the top of the SUV, and at 3.5~m/s with 60° inclined into the trunk of the SUV.

Figures (21)

• Figure 1: Simulations and real-world experiments of our adaptive tracking and perching system. Please watch our attached videos for more information at: youtu.be/5XKm7qkp2Xs, youtu.be/fBwW93Zq9ss.
• Figure 2: An overview of our complete aerial system with dynamic tracking and perching scheme. The trajectory generation module takes all the requirements mentioned in Sec. \ref{['sec:intro']} into account and provides spatiotemporal optimal feasible trajectory for stable tracking and dynamic perching.
• Figure 3: Illustration of the geometry model. The quadrotor is modeled as a disc $\mathcal{C}$, and the perching surface is modeled as a half-space $\mathcal{P}$.
• Figure 4: A. Illustration of the OE metric. The drone at position $\mathbf{p}$ observes the target $\bm \varrho$. A sequence of ball-shaped areas in blue are used to approximate the confident FoV expected to be obstacle-free. B. An example of the two decomposition parts of the negative $F_{OE}$'s gradient, driving the drone to move towards obstacle-free areas and adjust the tracking distance elastically. C. The drone moves towards obstacle-free areas for less occlusion probability. D. The drone adjusts the tracking distance in obstacle-rich areas for less occlusion probability.
• Figure 5: Illustration of the perception distance range selection function with a front camera. The perception metric is only activated within the setting range $\left[\bar{d}_{min},\bar{d}_{max} \right]$. Overly constraining at a close relative distance can lead to violations of terminal attitude constraints.