2,761 research outputs found
FPR -- Fast Path Risk Algorithm to Evaluate Collision Probability
As mobile robots and autonomous vehicles become increasingly prevalent in
human-centred environments, there is a need to control the risk of collision.
Perceptual modules, for example machine vision, provide uncertain estimates of
object location. In that context, the frequently made assumption of an exactly
known free-space is invalid. Clearly, no paths can be guaranteed to be
collision free. Instead, it is necessary to compute the probabilistic risk of
collision on any proposed path. The FPR algorithm, proposed here, efficiently
calculates an upper bound on the risk of collision for a robot moving on the
plane. That computation orders candidate trajectories according to (the bound
on) their degree of risk. Then paths within a user-defined threshold of primary
risk could be selected according to secondary criteria such as comfort and
efficiency. The key contribution of this paper is the FPR algorithm and its
`convolution trick' to factor the integrals used to bound the risk of
collision. As a consequence of the convolution trick, given obstacles and
candidate paths, the computational load is reduced from the naive ,
to the qualitatively faster .Comment: To appear in IEEE Robotics and Automation Letters (RA-L
Fast and Bounded Probabilistic Collision Detection in Dynamic Environments for High-DOF Trajectory Planning
We present a novel approach to perform probabilistic collision detection
between a high-DOF robot and high-DOF obstacles in dynamic, uncertain
environments. In dynamic environments with a high-DOF robot and moving
obstacles, our approach efficiently computes accurate collision probability
between the robot and obstacles with upper error bounds. Furthermore, we
describe a prediction algorithm for future obstacle position and motion that
accounts for both spatial and temporal uncertainties. We present a trajectory
optimization algorithm for high-DOF robots in dynamic, uncertain environments
based on probabilistic collision detection. We highlight motion planning
performance in challenging scenarios with robot arms operating in environments
with dynamically moving human obstacles
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
SLAP: Simultaneous Localization and Planning Under Uncertainty for Physical Mobile Robots via Dynamic Replanning in Belief Space: Extended version
Simultaneous localization and Planning (SLAP) is a crucial ability for an
autonomous robot operating under uncertainty. In its most general form, SLAP
induces a continuous POMDP (partially-observable Markov decision process),
which needs to be repeatedly solved online. This paper addresses this problem
and proposes a dynamic replanning scheme in belief space. The underlying POMDP,
which is continuous in state, action, and observation space, is approximated
offline via sampling-based methods, but operates in a replanning loop online to
admit local improvements to the coarse offline policy. This construct enables
the proposed method to combat changing environments and large localization
errors, even when the change alters the homotopy class of the optimal
trajectory. It further outperforms the state-of-the-art FIRM (Feedback-based
Information RoadMap) method by eliminating unnecessary stabilization steps.
Applying belief space planning to physical systems brings with it a plethora of
challenges. A key focus of this paper is to implement the proposed planner on a
physical robot and show the SLAP solution performance under uncertainty, in
changing environments and in the presence of large disturbances, such as a
kidnapped robot situation.Comment: 20 pages, updated figures, extended theory and simulation result
Formal-language-theoretic Optimal Path Planning For Accommodation of Amortized Uncertainties and Dynamic Effects
We report a globally-optimal approach to robotic path planning under
uncertainty, based on the theory of quantitative measures of formal languages.
A significant generalization to the language-measure-theoretic path planning
algorithm \nustar is presented that explicitly accounts for average dynamic
uncertainties and estimation errors in plan execution. The notion of the
navigation automaton is generalized to include probabilistic uncontrollable
transitions, which account for uncertainties by modeling and planning for
probabilistic deviations from the computed policy in the course of execution.
The planning problem is solved by casting it in the form of a performance
maximization problem for probabilistic finite state automata. In essence we
solve the following optimization problem: Compute the navigation policy which
maximizes the probability of reaching the goal, while simultaneously minimizing
the probability of hitting an obstacle. Key novelties of the proposed approach
include the modeling of uncertainties using the concept of uncontrollable
transitions, and the solution of the ensuing optimization problem using a
highly efficient search-free combinatorial approach to maximize quantitative
measures of probabilistic regular languages. Applicability of the algorithm in
various models of robot navigation has been shown with experimental validation
on a two-wheeled mobile robotic platform (SEGWAY RMP 200) in a laboratory
environment.Comment: Submitted for review for possible publication elsewhere; journal
reference will be added when availabl
Generating Comfortable, Safe and Comprehensible Trajectories for Automated Vehicles in Mixed Traffic
While motion planning approaches for automated driving often focus on safety
and mathematical optimality with respect to technical parameters, they barely
consider convenience, perceived safety for the passenger and comprehensibility
for other traffic participants. For automated driving in mixed traffic,
however, this is key to reach public acceptance. In this paper, we revise the
problem statement of motion planning in mixed traffic: Instead of largely
simplifying the motion planning problem to a convex optimization problem, we
keep a more complex probabilistic multi agent model and strive for a near
optimal solution. We assume cooperation of other traffic participants, yet
being aware of violations of this assumption. This approach yields solutions
that are provably safe in all situations, and convenient and comprehensible in
situations that are also unambiguous for humans. Thus, it outperforms existing
approaches in mixed traffic scenarios, as we show in simulation
Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective Search on GPUs
In this paper we present the PUMP (Parallel Uncertainty-aware Multiobjective
Planning) algorithm for addressing the stochastic kinodynamic motion planning
problem, whereby one seeks a low-cost, dynamically-feasible motion plan subject
to a constraint on collision probability (CP). To ensure exhaustive evaluation
of candidate motion plans (as needed to tradeoff the competing objectives of
performance and safety), PUMP incrementally builds the Pareto front of the
problem, accounting for the optimization objective and an approximation of CP.
This is performed by a massively parallel multiobjective search, here
implemented with a focus on GPUs. Upon termination of the exploration phase,
PUMP searches the Pareto set of motion plans to identify the lowest cost
solution that is certified to satisfy the CP constraint (according to an
asymptotically exact estimator). We introduce a novel particle-based CP
approximation scheme, designed for efficient GPU implementation, which accounts
for dependencies over the history of a trajectory execution. We present
numerical experiments for quadrotor planning wherein PUMP identifies solutions
in ~100 ms, evaluating over one hundred thousand partial plans through the
course of its exploration phase. The results show that this multiobjective
search achieves a lower motion plan cost, for the same CP constraint, compared
to a safety buffer-based search heuristic and repeated RRT trials
Provably Safe Robot Navigation with Obstacle Uncertainty
As drones and autonomous cars become more widespread it is becoming
increasingly important that robots can operate safely under realistic
conditions. The noisy information fed into real systems means that robots must
use estimates of the environment to plan navigation. Efficiently guaranteeing
that the resulting motion plans are safe under these circumstances has proved
difficult. We examine how to guarantee that a trajectory or policy is safe with
only imperfect observations of the environment. We examine the implications of
various mathematical formalisms of safety and arrive at a mathematical notion
of safety of a long-term execution, even when conditioned on observational
information. We present efficient algorithms that can prove that trajectories
or policies are safe with much tighter bounds than in previous work. Notably,
the complexity of the environment does not affect our methods ability to
evaluate if a trajectory or policy is safe. We then use these safety checking
methods to design a safe variant of the RRT planning algorithm.Comment: RSS 201
Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty
This article presents a novel approach, named MCMP (Monte Carlo Motion
Planning), to the problem of motion planning under uncertainty, i.e., to the
problem of computing a low-cost path that fulfills probabilistic collision
avoidance constraints. MCMP estimates the collision probability (CP) of a given
path by sampling via Monte Carlo the execution of a reference tracking
controller (in this paper we consider LQG). The key algorithmic contribution of
this paper is the design of statistical variance-reduction techniques, namely
control variates and importance sampling, to make such a sampling procedure
amenable to real-time implementation. MCMP applies this CP estimation procedure
to motion planning by iteratively (i) computing an (approximately) optimal path
for the deterministic version of the problem (here, using the FMT* algorithm),
(ii) computing the CP of this path, and (iii) inflating or deflating the
obstacles by a common factor depending on whether the CP is higher or lower
than a target value. The advantages of MCMP are threefold: (i) asymptotic
correctness of CP estimation, as opposed to most current approximations, which,
as shown in this paper, can be off by large multiples and hinder the
computation of feasible plans; (ii) speed and parallelizability, and (iii)
generality, i.e., the approach is applicable to virtually any planning problem
provided that a path tracking controller and a notion of distance to obstacles
in the configuration space are available. Numerical results illustrate the
correctness (in terms of feasibility), efficiency (in terms of path cost), and
computational speed of MCMP
Funnel Libraries for Real-Time Robust Feedback Motion Planning
We consider the problem of generating motion plans for a robot that are
guaranteed to succeed despite uncertainty in the environment, parametric model
uncertainty, and disturbances. Furthermore, we consider scenarios where these
plans must be generated in real-time, because constraints such as obstacles in
the environment may not be known until they are perceived (with a noisy sensor)
at runtime. Our approach is to pre-compute a library of "funnels" along
different maneuvers of the system that the state is guaranteed to remain within
(despite bounded disturbances) when the feedback controller corresponding to
the maneuver is executed. We leverage powerful computational machinery from
convex optimization (sums-of-squares programming in particular) to compute
these funnels. The resulting funnel library is then used to sequentially
compose motion plans at runtime while ensuring the safety of the robot. A major
advantage of the work presented here is that by explicitly taking into account
the effect of uncertainty, the robot can evaluate motion plans based on how
vulnerable they are to disturbances.
We demonstrate and validate our method using extensive hardware experiments
on a small fixed-wing airplane avoiding obstacles at high speed (~12 mph),
along with thorough simulation experiments of ground vehicle and quadrotor
models navigating through cluttered environments. To our knowledge, these
demonstrations constitute one of the first examples of provably safe and robust
control for robotic systems with complex nonlinear dynamics that need to plan
in real-time in environments with complex geometric constraints.Comment: International Journal of Robotics Research (To Appear
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