Observability-Aware Control for Cooperatively Localizing Quadrotor UAVs

Cooperatively Localizing robots should seek optimal control strategies to maximize precision of position estimation and ensure safety in flight. Observability-Aware Trajectory Optimization has strong potential to address this issue, but no concrete link between observability and precision has been proven yet. In this paper, we prove that improvement in positioning precision inherently follows from optimizing observability. Based on this finding, we develop an Observability-Aware Control principle to generate observability-optimal control strategies. We implement this principle in a Model Predictive Control framework, and we verify it on a team of quadrotor Unmanned Aerial Vehicles comprising a follower vehicle localizing itself by tracking a leader vehicle in both simulations and real-world flight tests. Our results demonstrate that maximizing observability contributed to improving global positioning precision for the quadrotor team.

Multi-UAV Uniform Sweep Coverage in Unknown Environments: A Mergeable Nervous System (MNS)-Based Random Exploration

This paper investigates the problem of multi-UAV uniform sweep coverage, where a homogeneous swarm of UAVs must collectively and evenly visit every portion of an unknown environment for a sampling task without having access to their own location and orientation. Random walk-based exploration strategies are practical for such a coverage scenario as they do not rely on localization and are easily implementable in robot swarms. We demonstrate that the Mergeable Nervous System (MNS) framework, which enables a robot swarm to self-organize into a hierarchical ad-hoc communication network using local communication, is a promising control approach for random exploration in unknown environments by UAV swarms. To this end, we propose an MNS-based random walk approach where UAVs self-organize into a line formation using the MNS framework and then follow a random walk strategy to cover the environment while maintaining the formation. Through simulations, we test the efficiency of our approach against several decentralized random walk-based strategies as benchmarks. Our results show that the MNS-based random walk outperforms the benchmarks in terms of the time required to achieve full coverage and the coverage uniformity at that time, assessed across both the entire environment and within local regions.

Time-Optimal Planning for Long-Range Quadrotor Flights: An Automatic Optimal Synthesis Approach

Time-critical tasks such as drone racing typically cover large operation areas. However, it is difficult and computationally intensive for current time-optimal motion planners to accommodate long flight distances since a large yet unknown number of knot points is required to represent the trajectory. We present a polynomial-based automatic optimal synthesis (AOS) approach that can address this challenge. Our method not only achieves superior time optimality but also maintains a consistently low computational cost across different ranges while considering the full quadrotor dynamics. First, we analyze the properties of time-optimal quadrotor maneuvers to determine the minimal number of polynomial pieces required to capture the dominant structure of time-optimal trajectories. This enables us to represent substantially long minimum-time trajectories with a minimal set of variables. Then, a robust optimization scheme is developed to handle arbitrary start and end conditions as well as intermediate waypoints. Extensive comparisons show that our approach is faster than the state-of-the-art approach by orders of magnitude with comparable time optimality. Real-world experiments further validate the quality of the resulting trajectories, demonstrating aggressive time-optimal maneuvers with a peak velocity of 8.86 m/s.

Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing

In drone racing, the time-minimum trajectory is affected by the drone's capabilities, the layout of the race track, and the configurations of the gates (e.g., their shapes and sizes). However, previous studies neglect the configuration of the gates, simply rendering drone racing a waypoint-passing task. This formulation often leads to a conservative choice of paths through the gates, as the spatial potential of the gates is not fully utilized. To address this issue, we present a time-optimal planner that can faithfully model gate constraints with various configurations and thereby generate a more time-efficient trajectory while considering the single-rotor-thrust limits. Our approach excels in computational efficiency which only takes a few seconds to compute the full state and control trajectories of the drone through tracks with dozens of different gates. Extensive simulations and experiments confirm the effectiveness of the proposed methodology, showing that the lap time can be further reduced by taking into account the gate's configuration. We validate our planner in real-world flights and demonstrate super-extreme flight trajectory through race tracks.