25 research outputs found
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
MINVO Basis: Finding Simplexes with Minimum Volume Enclosing Polynomial Curves
This paper studies the problem of finding the smallest -simplex enclosing
a given -degree polynomial curve. Although the Bernstein and
B-Spline polynomial bases provide feasible solutions to this problem, the
simplexes obtained by these bases are not the smallest possible, which leads to
undesirably conservative results in many applications. We first prove that the
polynomial basis that solves this problem (MINVO basis) also solves for the
-degree polynomial curve with largest convex hull enclosed in a
given -simplex. Then, we present a formulation that is \emph{independent} of
the -simplex or -degree polynomial curve given. By using
Sum-Of-Squares (SOS) programming, branch and bound, and moment relaxations, we
obtain high-quality feasible solutions for any and prove
numerical global optimality for . The results obtained for show
that, for any given -degree polynomial curve, the MINVO basis is
able to obtain an enclosing simplex whose volume is and times
smaller than the ones obtained by the Bernstein and B-Spline bases,
respectively. When , these ratios increase to and
, respectively.Comment: 25 pages, 16 figure
Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environments
This paper presents Deep-PANTHER, a learning-based perception-aware
trajectory planner for unmanned aerial vehicles (UAVs) in dynamic environments.
Given the current state of the UAV, and the predicted trajectory and size of
the obstacle, Deep-PANTHER generates multiple trajectories to avoid a dynamic
obstacle while simultaneously maximizing its presence in the field of view
(FOV) of the onboard camera. To obtain a computationally tractable real-time
solution, imitation learning is leveraged to train a Deep-PANTHER policy using
demonstrations provided by a multimodal optimization-based expert. Extensive
simulations show replanning times that are two orders of magnitude faster than
the optimization-based expert, while achieving a similar cost. By ensuring that
each expert trajectory is assigned to one distinct student trajectory in the
loss function, Deep-PANTHER can also capture the multimodality of the problem
and achieve a mean squared error (MSE) loss with respect to the expert that is
up to 18 times smaller than state-of-the-art (Relaxed) Winner-Takes-All
approaches. Deep-PANTHER is also shown to generalize well to obstacle
trajectories that differ from the ones used in training
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
Robust MADER: Decentralized Multiagent Trajectory Planner Robust to Communication Delay in Dynamic Environments
Communication delays can be catastrophic for multiagent systems. However,
most existing state-of-the-art multiagent trajectory planners assume perfect
communication and therefore lack a strategy to rectify this issue in real-world
environments. To address this challenge, we propose Robust MADER (RMADER), a
decentralized, asynchronous multiagent trajectory planner robust to
communication delay. By always keeping a guaranteed collision-free trajectory
and performing a delay check step, RMADER is able to guarantee safety even
under communication delay. We perform an in-depth analysis of trajectory
deconfliction among agents, extensive benchmark studies, and hardware flight
experiments with multiple dynamic obstacles. We show that RMADER outperforms
existing approaches by achieving a 100% success rate of collision-free
trajectory generation, whereas the next best asynchronous decentralized method
only achieves 83% success.Comment: 8 pagers, 13 figures,. arXiv admin note: substantial text overlap
with arXiv:2209.1366
Modelo cinemático de un robot hexápodo con "C-LEGS"
En este artÃculo se profundiza en el modelado matemático de la odometrÃa para robots hexápodos con extremidades denominadas C-legs. El estudio no es trivial, y se analizan todas las posibilidades que puede tener el sistema según las variables que se definan. Todo el estudio se ve reforzado con una serie de simulaciones realizadas donde los resultados obtenidos coinciden con los esperados
Combined Point-of-Care Nucleic Acid and Antibody Testing for SARS-CoV-2 following Emergence of D614G Spike Variant
Rapid COVID-19 diagnosis in the hospital is essential, although this is complicated by 30%–50% of nose/throat swabs being negative by SARS-CoV-2 nucleic acid amplification testing (NAAT). Furthermore, the D614G spike mutant dominates the pandemic and it is unclear how serological tests designed to detect anti-spike antibodies perform against this variant. We assess the diagnostic accuracy of combined rapid antibody point of care (POC) and nucleic acid assays for suspected COVID-19 disease due to either wild-type or the D614G spike mutant SARS-CoV-2. The overall detection rate for COVID-19 is 79.2% (95% CI 57.8–92.9) by rapid NAAT alone. The combined point of care antibody test and rapid NAAT is not affected by D614G and results in very high sensitivity for COVID-19 diagnosis with very high specificity