160 research outputs found
Multi-resolution mapping and planning for UAV navigation in attitude constrained environments
In this thesis we aim to bridge the gap between high quality map reconstruction and Unmanned Aerial Vehicles (UAVs) SE(3) motion planning in challenging environments with narrow openings, such as disaster areas, which requires attitude to be considered. We propose an efficient system that leverages the concept of adaptive-resolution volumetric mapping, which naturally integrates with the hierarchical decomposition of space in an octree data structure. Instead of a Truncated Signed Distance Function (TSDF), we adopt mapping of occupancy probabilities in log-odds representation, which allows representation of both surfaces, as well as the entire free, i.e.\ observed space, as opposed to unobserved space. We introduce a method for choosing resolution -on the fly- in real-time by means of a multi-scale max-min pooling of the input depth image. The notion of explicit free space mapping paired with the spatial hierarchy in the data structure, as well as map resolution, allows for collision queries, as needed for robot motion planning, at unprecedented speed. Our mapping strategy supports pinhole cameras as well as spherical sensor models. Additionally, we introduce a first-of-a-kind global minimum cost path search method based on A* that considers attitude along the path. State-of-the-art methods incorporate attitude only in the refinement stage. To make the problem tractable, our method exploits an adaptive and coarse-to-fine approach using global and local A* runs, plus an efficient method to introduce the UAV attitude in the process. We integrate our method with an SE(3) trajectory optimisation method based on a safe-flight-corridor, yielding a complete path planning pipeline.
We quantitatively evaluate our mapping strategy in terms of mapping accuracy, memory, runtime performance, and planning performance showing improvements over the state-of-the-art, particularly in cases requiring high resolution maps. Furthermore, extensive evaluation is undertaken using the AirSim flight simulator under closed loop control in a set of randomised maps, allowing us to quantitatively assess our path initialisation method. We show that it achieves significantly higher success rates than the baselines, at a reduced computational burden.Open Acces
Minimum-Time Quadrotor Waypoint Flight in Cluttered Environments
We tackle the problem of planning a minimum-time trajectory for a quadrotor
over a sequence of specified waypoints in the presence of obstacles while
exploiting the full quadrotor dynamics. This problem is crucial for autonomous
search and rescue and drone racing scenarios but was, so far, unaddressed by
the robotics community \emph{in its entirety} due to the challenges of
minimizing time in the presence of the non-convex constraints posed by
collision avoidance. Early works relied on simplified dynamics or polynomial
trajectory representations that did not exploit the full actuator potential of
a quadrotor and, thus, did not aim at minimizing time. We address this
challenging problem by using a hierarchical, sampling-based method with an
incrementally more complex quadrotor model. Our method first finds paths in
different topologies to guide subsequent trajectory search for a kinodynamic
point-mass model. Then, it uses an asymptotically-optimal, kinodynamic
sampling-based method based on a full quadrotor model on top of the point-mass
solution to find a feasible trajectory with a time-optimal objective. The
proposed method is shown to outperform all related baselines in cluttered
environments and is further validated in real-world flights at over 60km/h in
one of the world's largest motion capture systems. We release the code open
source.Comment: Accepted in IEEE Robotics and Automation Letter
Search-based Motion Planning for Aggressive Flight in SE(3)
Quadrotors with large thrust-to-weight ratios are able to track aggressive
trajectories with sharp turns and high accelerations. In this work, we develop
a search-based trajectory planning approach that exploits the quadrotor
maneuverability to generate sequences of motion primitives in cluttered
environments. We model the quadrotor body as an ellipsoid and compute its
flight attitude along trajectories in order to check for collisions against
obstacles. The ellipsoid model allows the quadrotor to pass through gaps that
are smaller than its diameter with non-zero pitch or roll angles. Without any
prior information about the location of gaps and associated attitude
constraints, our algorithm is able to find a safe and optimal trajectory that
guides the robot to its goal as fast as possible. To accelerate planning, we
first perform a lower dimensional search and use it as a heuristic to guide the
generation of a final dynamically feasible trajectory. We analyze critical
discretization parameters of motion primitive planning and demonstrate the
feasibility of the generated trajectories in various simulations and real-world
experiments.Comment: 8 pages, submitted to RAL and ICRA 201
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