21,530 research outputs found
Towards Fully Environment-Aware UAVs: Real-Time Path Planning with Online 3D Wind Field Prediction in Complex Terrain
Today, low-altitude fixed-wing Unmanned Aerial Vehicles (UAVs) are largely
limited to primitively follow user-defined waypoints. To allow fully-autonomous
remote missions in complex environments, real-time environment-aware navigation
is required both with respect to terrain and strong wind drafts. This paper
presents two relevant initial contributions: First, the literature's first-ever
3D wind field prediction method which can run in real time onboard a UAV is
presented. The approach retrieves low-resolution global weather data, and uses
potential flow theory to adjust the wind field such that terrain boundaries,
mass conservation, and the atmospheric stratification are observed. A
comparison with 1D LIDAR data shows an overall wind error reduction of 23% with
respect to the zero-wind assumption that is mostly used for UAV path planning
today. However, given that the vertical winds are not resolved accurately
enough further research is required and identified. Second, a sampling-based
path planner that considers the aircraft dynamics in non-uniform wind
iteratively via Dubins airplane paths is presented. Performance optimizations,
e.g. obstacle-aware sampling and fast 2.5D-map collision checks, render the
planner 50% faster than the Open Motion Planning Library (OMPL) implementation.
Test cases in Alpine terrain show that the wind-aware planning performs up to
50x less iterations than shortest-path planning and is thus slower in low
winds, but that it tends to deliver lower-cost paths in stronger winds. More
importantly, in contrast to the shortest-path planner, it always delivers
collision-free paths. Overall, our initial research demonstrates the
feasibility of 3D wind field prediction from a UAV and the advantages of
wind-aware planning. This paves the way for follow-up research on
fully-autonomous environment-aware navigation of UAVs in real-life missions and
complex terrain
Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments
Safe autonomous navigation is an essential and challenging problem for robots
operating in highly unstructured or completely unknown environments. Under
these conditions, not only robotic systems must deal with limited localisation
information, but also their manoeuvrability is constrained by their dynamics
and often suffer from uncertainty. In order to cope with these constraints,
this manuscript proposes an uncertainty-based framework for mapping and
planning feasible motions online with probabilistic safety-guarantees. The
proposed approach deals with the motion, probabilistic safety, and online
computation constraints by: (i) incrementally mapping the surroundings to build
an uncertainty-aware representation of the environment, and (ii) iteratively
(re)planning trajectories to goal that are kinodynamically feasible and
probabilistically safe through a multi-layered sampling-based planner in the
belief space. In-depth empirical analyses illustrate some important properties
of this approach, namely, (a) the multi-layered planning strategy enables rapid
exploration of the high-dimensional belief space while preserving asymptotic
optimality and completeness guarantees, and (b) the proposed routine for
probabilistic collision checking results in tighter probability bounds in
comparison to other uncertainty-aware planners in the literature. Furthermore,
real-world in-water experimental evaluation on a non-holonomic torpedo-shaped
autonomous underwater vehicle and simulated trials in the Stairwell scenario of
the DARPA Subterranean Challenge 2019 on a quadrotor unmanned aerial vehicle
demonstrate the efficacy of the method as well as its suitability for systems
with limited on-board computational power.Comment: The International Journal of Robotics Research (under review
RRT-CoLearn: towards kinodynamic planning without numerical trajectory optimization
Sampling-based kinodynamic planners, such as Rapidly-exploring Random Trees
(RRTs), pose two fundamental challenges: computing a reliable (pseudo-)metric
for the distance between two randomly sampled nodes, and computing a steering
input to connect the nodes. The core of these challenges is a Two Point
Boundary Value Problem, which is known to be NP-hard. Recently, the distance
metric has been approximated using supervised learning, reducing computation
time drastically. The previous work on such learning RRTs use direct optimal
control to generate the data for supervised learning. This paper proposes to
use indirect optimal control instead, because it provides two benefits: it
reduces the computational effort to generate the data, and it provides a low
dimensional parametrization of the action space. The latter allows us to learn
both the distance metric and the steering input to connect two nodes. This
eliminates the need for a local planner in learning RRTs. Experimental results
on a pendulum swing up show 10-fold speed-up in both the offline data
generation and the online planning time, leading to at least a 10-fold speed-up
in the overall planning time.Comment: This paper is currently under review at IEEE RA-
Motion Planning of Uncertain Ordinary Differential Equation Systems
This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems.
Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs.
The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space
Optimization-Based Autonomous Racing of 1:43 Scale RC Cars
This paper describes autonomous racing of RC race cars based on mathematical
optimization. Using a dynamical model of the vehicle, control inputs are
computed by receding horizon based controllers, where the objective is to
maximize progress on the track subject to the requirement of staying on the
track and avoiding opponents. Two different control formulations are presented.
The first controller employs a two-level structure, consisting of a path
planner and a nonlinear model predictive controller (NMPC) for tracking. The
second controller combines both tasks in one nonlinear optimization problem
(NLP) following the ideas of contouring control. Linear time varying models
obtained by linearization are used to build local approximations of the control
NLPs in the form of convex quadratic programs (QPs) at each sampling time. The
resulting QPs have a typical MPC structure and can be solved in the range of
milliseconds by recent structure exploiting solvers, which is key to the
real-time feasibility of the overall control scheme. Obstacle avoidance is
incorporated by means of a high-level corridor planner based on dynamic
programming, which generates convex constraints for the controllers according
to the current position of opponents and the track layout. The control
performance is investigated experimentally using 1:43 scale RC race cars,
driven at speeds of more than 3 m/s and in operating regions with saturated
rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on
embedded computing platforms, demonstrating the real-time feasibility and high
performance of optimization-based approaches for autonomous racing
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
Self-driving vehicles are a maturing technology with the potential to reshape
mobility by enhancing the safety, accessibility, efficiency, and convenience of
automotive transportation. Safety-critical tasks that must be executed by a
self-driving vehicle include planning of motions through a dynamic environment
shared with other vehicles and pedestrians, and their robust executions via
feedback control. The objective of this paper is to survey the current state of
the art on planning and control algorithms with particular regard to the urban
setting. A selection of proposed techniques is reviewed along with a discussion
of their effectiveness. The surveyed approaches differ in the vehicle mobility
model used, in assumptions on the structure of the environment, and in
computational requirements. The side-by-side comparison presented in this
survey helps to gain insight into the strengths and limitations of the reviewed
approaches and assists with system level design choices
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
We consider the problems of learning forward models that map state to
high-dimensional images and inverse models that map high-dimensional images to
state in robotics. Specifically, we present a perceptual model for generating
video frames from state with deep networks, and provide a framework for its use
in tracking and prediction tasks. We show that our proposed model greatly
outperforms standard deconvolutional methods and GANs for image generation,
producing clear, photo-realistic images. We also develop a convolutional neural
network model for state estimation and compare the result to an Extended Kalman
Filter to estimate robot trajectories. We validate all models on a real robotic
system.Comment: 8 pages, International Conference on Robotics and Automation (ICRA)
201
Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories
Predicting the motion of a mobile agent from a third-person perspective is an
important component for many robotics applications, such as autonomous
navigation and tracking. With accurate motion prediction of other agents,
robots can plan for more intelligent behaviors to achieve specified objectives,
instead of acting in a purely reactive way. Previous work addresses motion
prediction by either only filtering kinematics, or using hand-designed and
learned representations of the environment. Instead of separating kinematic and
environmental context, we propose a novel approach to integrate both into an
inverse reinforcement learning (IRL) framework for trajectory prediction.
Instead of exponentially increasing the state-space complexity with kinematics,
we propose a two-stage neural network architecture that considers motion and
environment together to recover the reward function. The first-stage network
learns feature representations of the environment using low-level LiDAR
statistics and the second-stage network combines those learned features with
kinematics data. We collected over 30 km of off-road driving data and validated
experimentally that our method can effectively extract useful environmental and
kinematic features. We generate accurate predictions of the distribution of
future trajectories of the vehicle, encoding complex behaviors such as
multi-modal distributions at road intersections, and even show different
predictions at the same intersection depending on the vehicle's speed.Comment: CoRL 201
Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation
Fast and efficient path generation is critical for robots operating in
complex environments. This motion planning problem is often performed in a
robot's actuation or configuration space, where popular pathfinding methods
such as A*, RRT*, get exponentially more computationally expensive to execute
as the dimensionality increases or the spaces become more cluttered and
complex. On the other hand, if one were to save the entire set of paths
connecting all pair of locations in the configuration space a priori, one would
run out of memory very quickly. In this work, we introduce a novel way of
producing fast and optimal motion plans for static environments by using a
stepping neural network approach, called OracleNet. OracleNet uses Recurrent
Neural Networks to determine end-to-end trajectories in an iterative manner
that implicitly generates optimal motion plans with minimal loss in performance
in a compact form. The algorithm is straightforward in implementation while
consistently generating near-optimal paths in a single, iterative, end-to-end
roll-out. In practice, OracleNet generally has fixed-time execution regardless
of the configuration space complexity while outperforming popular pathfinding
algorithms in complex environments and higher dimension
Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks
Natural disasters can have catastrophic impacts on the functionality of
infrastructure systems and cause severe physical and socio-economic losses.
Given budget constraints, it is crucial to optimize decisions regarding
mitigation, preparedness, response, and recovery practices for these systems.
This requires accurate and efficient means to evaluate the infrastructure
system reliability. While numerous research efforts have addressed and
quantified the impact of natural disasters on infrastructure systems, typically
using the Monte Carlo approach, they still suffer from high computational cost
and, thus, are of limited applicability to large systems. This paper presents a
deep learning framework for accelerating infrastructure system reliability
analysis. In particular, two distinct deep neural network surrogates are
constructed and studied: (1) A classifier surrogate which speeds up the
connectivity determination of networks, and (2) An end-to-end surrogate that
replaces a number of components such as roadway status realization,
connectivity determination, and connectivity averaging. The proposed approach
is applied to a simulation-based study of the two-terminal connectivity of a
California transportation network subject to extreme probabilistic earthquake
events. Numerical results highlight the effectiveness of the proposed approach
in accelerating the transportation system two-terminal reliability analysis
with extremely high prediction accuracy
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