1,783 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
Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles
In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex,
unknown, unstructured environments, they must be able to navigate with
guaranteed safety, even when faced with a cluttered environment they have no
prior knowledge of. While trajectory optimization-based local planners have
been shown to perform well in these cases, prior work either does not address
how to deal with local minima in the optimization problem, or solves it by
using an optimistic global planner.
We present a conservative trajectory optimization-based local planner,
coupled with a local exploration strategy that selects intermediate goals. We
perform extensive simulations to show that this system performs better than the
standard approach of using an optimistic global planner, and also outperforms
doing a single exploration step when the local planner is stuck. The method is
validated through experiments in a variety of highly cluttered environments
including a dense forest. These experiments show the complete system running in
real time fully onboard an MAV, mapping and replanning at 4 Hz.Comment: Accepted to ICRA 2018 and RA-L 201
Motion primitives and 3D path planning for fast flight through a forest
This paper presents two families of motion primitives for enabling fast, agile flight through a dense obstacle field. The first family of primitives consists of a time-delay dependent 3D circular path between two points in space and the control inputs required to fly the path. In particular, the control inputs are calculated using algebraic equations which depend on the flight parameters and the location of the waypoint. Moreover, the transition between successive maneuver states, where each state is defined by a unique combination of constant control inputs, is modeled rigorously as an instantaneous switch between the two maneuver states following a time delay which is directly related to the agility of the robotic aircraft. The second family consists of aggressive turn-around (ATA) maneuvers which the robot uses to retreat from impenetrable pockets of obstacles. The ATA maneuver consists of an orchestrated sequence of three sets of constant control inputs. The duration of the first segment is used to optimize the ATA for the spatial constraints imposed by the turning volume. The motion primitives are validated experimentally and implemented in a simulated receding horizon control (RHC)-based motion planner. The paper concludes with inverse-design pointers derived from the primitives
FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments
High-speed trajectory planning through unknown environments requires
algorithmic techniques that enable fast reaction times while maintaining safety
as new information about the operating environment is obtained. The requirement
of computational tractability typically leads to optimization problems that do
not include the obstacle constraints (collision checks are done on the
solutions) or use a convex decomposition of the free space and then impose an
ad-hoc time allocation scheme for each interval of the trajectory. Moreover,
safety guarantees are usually obtained by having a local planner that plans a
trajectory with a final "stop" condition in the free-known space. However,
these two decisions typically lead to slow and conservative trajectories. We
propose FASTER (Fast and Safe Trajectory Planner) to overcome these issues.
FASTER obtains high-speed trajectories by enabling the local planner to
optimize in both the free-known and unknown spaces. Safety guarantees are
ensured by always having a feasible, safe back-up trajectory in the free-known
space at the start of each replanning step. Furthermore, we present a Mixed
Integer Quadratic Program formulation in which the solver can choose the
trajectory interval allocation, and where a time allocation heuristic is
computed efficiently using the result of the previous replanning iteration.
This proposed algorithm is tested extensively both in simulation and in real
hardware, showing agile flights in unknown cluttered environments with
velocities up to 3.6 m/s.Comment: IROS 201
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
Path Planning in 3D with Motion Primitives for Wind Energy-Harvesting Fixed-Wing Aircraft
In this work, a set of motion primitives is defined for use in an
energy-aware motion planning problem. The motion primitives are defined as
sequences of control inputs to a simplified four-DOF dynamics model and are
used to replace the traditional continuous control space used in many
sampling-based motion planners. The primitives are implemented in a Stable
Sparse Rapidly Exploring Random Tree (SST) motion planner and compared to an
identical planner using a continuous control space. The planner using
primitives was found to run 11.0\% faster but yielded solution paths that were
on average worse with higher variance. Also, the solution path travel time is
improved by about 50\%. Using motion primitives for sampling spaces in SST can
effectively reduce the run time of the algorithm, although at the cost of
solution quality.Comment: 4 page
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