1,789 research outputs found
Trajectory optimization and motion planning for quadrotors in unstructured environments
Trajectory optimization and motion planning for quadrotors in
unstructured environments
Coming out from university labs robots perform tasks usually navigating through
unstructured environment. The realization of autonomous motion in such type of environments
poses a number of challenges compared to highly controlled laboratory
spaces. In unstructured environments robots cannot rely on complete knowledge
of their sorroundings and they have to continously acquire information for decision
making. The challenges presented are a consequence of the high-dimensionality
of the state-space and of the uncertainty introduced by modeling and perception.
This is even more true for aerial-robots that has a complex nonlinear dynamics a can
move freely in 3D-space. To avoid this complexity a robot have to select a small set of
relevant features, reason on a reduced state space and plan trajectories on short-time
horizon. This thesis is a contribution towards the autonomous navigation of aerial
robots (quadrotors) in real-world unstructured scenarios. The first three chapters
present a contribution towards an implementation of Receding Time Horizon Optimal
Control. The optimization problem for a model based trajectory generation in
environments with obstacles is set, using an approach based on variational calculus
and modeling the robots in the SE(3) Lie Group of 3D space transformations. The
fourth chapter explores the problem of using minimal information and sensing to
generate motion towards a goal in an indoor bulding-like scenario. The fifth chapter
investigate the problem of extracting visual features from the environment to
control the motion in an indoor corridor-like scenario. The last chapter deals with
the problem of spatial reasoning and motion planning using atomic proposition in a
multi-robot environments with obstacles
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
Stochastic Motion Planning as Gaussian Variational Inference: Theory and Algorithms
We consider the motion planning problem under uncertainty and address it
using probabilistic inference. A collision-free motion plan with linear
stochastic dynamics is modeled by a posterior distribution. Gaussian
variational inference is an optimization over the path distributions to infer
this posterior within the scope of Gaussian distributions. We propose Gaussian
Variational Inference Motion Planner (GVI-MP) algorithm to solve this Gaussian
inference, where a natural gradient paradigm is used to iteratively update the
Gaussian distribution, and the factorized structure of the joint distribution
is leveraged. We show that the direct optimization over the state distributions
in GVI-MP is equivalent to solving a stochastic control that has a closed-form
solution. Starting from this observation, we propose our second algorithm,
Proximal Gradient Covariance Steering Motion Planner (PGCS-MP), to solve the
same inference problem in its stochastic control form with terminal
constraints. We use a proximal gradient paradigm to solve the linear stochastic
control with nonlinear collision cost, where the nonlinear cost is iteratively
approximated using quadratic functions and a closed-form solution can be
obtained by solving a linear covariance steering at each iteration. We evaluate
the effectiveness and the performance of the proposed approaches through
extensive experiments on various robot models. The code for this paper can be
found in https://github.com/hzyu17/VIMP.Comment: 19 page
Aspects of the Rover Problem
The basic task of a rover is to move about automonously in an unknown environment. A working rover must have the following three subsystems which interact in various ways: 1) locomotion--the ability to move, 2) perception--the ability to determine the three-dimensional structure of the environment, and 3) navigation--the ability to negotiate the environment. This paper will elucidate the nature of the problem in these areas and survey approaches to solving them while paying attention to real-world issues.MIT Artificial Intelligence Laborator
From visuomotor control to latent space planning for robot manipulation
Deep visuomotor control is emerging as an active research area for robot manipulation. Recent advances in learning sensory and motor systems in an end-to-end manner have achieved remarkable performance across a range of complex tasks. Nevertheless, a few limitations restrict visuomotor control from being more widely adopted as the de facto choice when facing a manipulation task on a real robotic platform. First, imitation learning-based visuomotor control approaches tend to suffer from the inability to recover from an out-of-distribution state caused by compounding errors. Second, the lack of versatility in task definition limits skill generalisability. Finally, the training data acquisition process and domain transfer are often impractical. In this thesis, individual solutions are proposed to address each of these issues.
In the first part, we find policy uncertainty to be an effective indicator of potential failure cases, in which the robot is stuck in out-of-distribution states. On this basis, we introduce a novel uncertainty-based approach to detect potential failure cases and a recovery strategy based on action-conditioned uncertainty predictions. Then, we propose to employ visual dynamics approximation to our model architecture to capture the motion of the robot arm instead of the static scene background, making it possible to learn versatile skill primitives. In the second part, taking inspiration from the recent progress in latent space planning, we propose a gradient-based optimisation method operating within the latent space of a deep generative model for motion planning. Our approach bypasses the traditional computational challenges encountered by established planning algorithms, and has the capability to specify novel constraints easily and handle multiple constraints simultaneously. Moreover, the training data comes from simple random motor-babbling of kinematically feasible robot states. Our real-world experiments further illustrate that our latent space planning approach can handle both open and closed-loop planning in challenging environments such as heavily cluttered or dynamic scenes. This leads to the first, to our knowledge, closed-loop motion planning algorithm that can incorporate novel custom constraints, and lays the foundation for more complex manipulation tasks
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