60,437 research outputs found
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
Occupancy Map Building through Bayesian Exploration
We propose a novel holistic approach for safe autonomous exploration and map
building based on constrained Bayesian optimisation. This method finds optimal
continuous paths instead of discrete sensing locations that inherently satisfy
motion and safety constraints. Evaluating both the objective and constraints
functions requires forward simulation of expected observations. As such
evaluations are costly, the Bayesian optimiser proposes only paths which are
likely to yield optimal results and satisfy the constraints with high
confidence. By balancing the reward and risk associated with each path, the
optimiser minimises the number of expensive function evaluations. We
demonstrate the effectiveness of our approach in a series of experiments both
in simulation and with a real ground robot and provide comparisons to other
exploration techniques. Evidently, each method has its specific favourable
conditions, where it outperforms all other techniques. Yet, by reasoning on the
usefulness of the entire path instead of its end point, our method provides a
robust and consistent performance through all tests and performs better than or
as good as the other leading methods
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
Towards Search-based Motion Planning for Micro Aerial Vehicles
Search-based motion planning has been used for mobile robots in many
applications. However, it has not been fully developed and applied for planning
full state trajectories of Micro Aerial Vehicles (MAVs) due to their
complicated dynamics and the requirement of real-time computation. In this
paper, we explore a search-based motion planning framework that plans
dynamically feasible, collision-free, and resolution optimal and complete
trajectories. This paper extends the search-based planning approach to address
three important scenarios for MAVs navigation: (i) planning safe trajectories
in the presence of motion uncertainty; (ii) planning with constraints on
field-of-view and (iii) planning in dynamic environments. We show that these
problems can be solved effectively and efficiently using the proposed
search-based planning with motion primitives.Comment: 8 pages, 10 figures, submitted to ICRA 201
Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction
We introduce a new method that efficiently computes a set of viewpoints and
trajectories for high-quality 3D reconstructions in outdoor environments. Our
goal is to automatically explore an unknown area, and obtain a complete 3D scan
of a region of interest (e.g., a large building). Images from a commodity RGB
camera, mounted on an autonomously navigated quadcopter, are fed into a
multi-view stereo reconstruction pipeline that produces high-quality results
but is computationally expensive. In this setting, the scanning result is
constrained by the restricted flight time of quadcopters. To this end, we
introduce a novel optimization strategy that respects these constraints by
maximizing the information gain from sparsely-sampled view points while
limiting the total travel distance of the quadcopter. At the core of our method
lies a hierarchical volumetric representation that allows the algorithm to
distinguish between unknown, free, and occupied space. Furthermore, our
information gain based formulation leverages this representation to handle
occlusions in an efficient manner. In addition to the surface geometry, we
utilize the free-space information to avoid obstacles and determine
collision-free flight paths. Our tool can be used to specify the region of
interest and to plan trajectories. We demonstrate our method by obtaining a
number of compelling 3D reconstructions, and provide a thorough quantitative
evaluation showing improvement over previous state-of-the-art and regular
patterns.Comment: 31 pages, 12 figures, 9 table
Sparse 3D Topological Graphs for Micro-Aerial Vehicle Planning
Micro-Aerial Vehicles (MAVs) have the advantage of moving freely in 3D space.
However, creating compact and sparse map representations that can be
efficiently used for planning for such robots is still an open problem. In this
paper, we take maps built from noisy sensor data and construct a sparse graph
containing topological information that can be used for 3D planning. We use a
Euclidean Signed Distance Field, extract a 3D Generalized Voronoi Diagram
(GVD), and obtain a thin skeleton diagram representing the topological
structure of the environment. We then convert this skeleton diagram into a
sparse graph, which we show is resistant to noise and changes in resolution. We
demonstrate global planning over this graph, and the orders of magnitude
speed-up it offers over other common planning methods. We validate our planning
algorithm in real maps built onboard an MAV, using RGB-D sensing.Comment: Accepted for publication in IEEE IROS 201
Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments
The use of drones for aerial cinematography has revolutionized several
applications and industries that require live and dynamic camera viewpoints
such as entertainment, sports, and security. However, safely controlling a
drone while filming a moving target usually requires multiple expert human
operators; hence the need for an autonomous cinematographer. Current approaches
have severe real-life limitations such as requiring fully scripted scenes,
high-precision motion-capture systems or GPS tags to localize targets, and
prior maps of the environment to avoid obstacles and plan for occlusion.
In this work, we overcome such limitations and propose a complete system for
aerial cinematography that combines: (1) a vision-based algorithm for target
localization; (2) a real-time incremental 3D signed-distance map algorithm for
occlusion and safety computation; and (3) a real-time camera motion planner
that optimizes smoothness, collisions, occlusions and artistic guidelines. We
evaluate robustness and real-time performance in series of field experiments
and simulations by tracking dynamic targets moving through unknown,
unstructured environments. Finally, we verify that despite removing previous
limitations, our system achieves state-of-the-art performance. Videos of the
system in action can be seen at https://youtu.be/ZE9MnCVmum
FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning
Fast and safe navigation of dynamical systems through a priori unknown
cluttered environments is vital to many applications of autonomous systems.
However, trajectory planning for autonomous systems is computationally
intensive, often requiring simplified dynamics that sacrifice safety and
dynamic feasibility in order to plan efficiently. Conversely, safe trajectories
can be computed using more sophisticated dynamic models, but this is typically
too slow to be used for real-time planning. We propose a new algorithm
FaSTrack: Fast and Safe Tracking for High Dimensional systems. A path or
trajectory planner using simplified dynamics to plan quickly can be
incorporated into the FaSTrack framework, which provides a safety controller
for the vehicle along with a guaranteed tracking error bound. This bound
captures all possible deviations due to high dimensional dynamics and external
disturbances. Note that FaSTrack is modular and can be used with most current
path or trajectory planners. We demonstrate this framework using a 10D
nonlinear quadrotor model tracking a 3D path obtained from an RRT planner.Comment: Submitted to IEEE Conference on Decision and Control, 201
Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions
Using reinforcement learning to learn control policies is a challenge when
the task is complex with potentially long horizons. Ensuring adequate but safe
exploration is also crucial for controlling physical systems. In this paper, we
use temporal logic to facilitate specification and learning of complex tasks.
We combine temporal logic with control Lyapunov functions to improve
exploration. We incorporate control barrier functions to safeguard the
exploration and deployment process. We develop a flexible and learnable system
that allows users to specify task objectives and constraints in different forms
and at various levels. The framework is also able to take advantage of known
system dynamics and handle unknown environmental dynamics by integrating
model-free learning with model-based planning
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties
Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertainties
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