230 research outputs found
Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments
Autonomous navigation through unknown environments is a challenging task that
entails real-time localization, perception, planning, and control. UAVs with
this capability have begun to emerge in the literature with advances in
lightweight sensing and computing. Although the planning methodologies vary
from platform to platform, many algorithms adopt a hierarchical planning
architecture where a slow, low-fidelity global planner guides a fast,
high-fidelity local planner. However, in unknown environments, this approach
can lead to erratic or unstable behavior due to the interaction between the
global planner, whose solution is changing constantly, and the local planner; a
consequence of not capturing higher-order dynamics in the global plan. This
work proposes a planning framework in which multi-fidelity models are used to
reduce the discrepancy between the local and global planner. Our approach uses
high-, medium-, and low-fidelity models to compose a path that captures
higher-order dynamics while remaining computationally tractable. In addition,
we address the interaction between a fast planner and a slower mapper by
considering the sensor data not yet fused into the map during the collision
check. This novel mapping and planning framework for agile flights is validated
in simulation and hardware experiments, showing replanning times of 5-40 ms in
cluttered environments.Comment: ICRA 201
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
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously
navigate between target locations quickly and reliably while avoiding obstacles
in its path, and with little to no a-priori knowledge of the operating
environment. This challenge is addressed in the present paper. We describe the
system design and software architecture of our proposed solution, and showcase
how all the distinct components can be integrated to enable smooth robot
operation. We provide critical insight on hardware and software component
selection and development, and present results from extensive experimental
testing in real-world warehouse environments. Experimental testing reveals that
our proposed solution can deliver fast and robust aerial robot autonomous
navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field
Robotic
Accelerating Trajectory Generation for Quadrotors Using Transformers
In this work, we address the problem of computation time for trajectory
generation in quadrotors. Most trajectory generation methods for waypoint
navigation of quadrotors, for example minimum snap/jerk and minimum-time, are
structured as bi-level optimizations. The first level involves allocating time
across all input waypoints and the second step is to minimize the snap/jerk of
the trajectory under that time allocation. Such an optimization can be
computationally expensive to solve. In our approach we treat trajectory
generation as a supervised learning problem between a sequential set of inputs
and outputs. We adapt a transformer model to learn the optimal time allocations
for a given set of input waypoints, thus making it into a single step
optimization. We demonstrate the performance of the transformer model by
training it to predict the time allocations for a minimum snap trajectory
generator. The trained transformer model is able to predict accurate time
allocations with fewer data samples and smaller model size, compared to a
feedforward network (FFN), demonstrating that it is able to model the
sequential nature of the waypoint navigation problem.Comment: Accepted at L4DC 202
- …