2,483 research outputs found
Probabilistic properties of near-optimal trajectories of an agent moving over a lattice
Probability distribution of the agent’s residence in a given cell at a given moment of time for random landscapes of different types is analyze
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
Future state maximisation as an intrinsic motivation for decision making
The concept of an “intrinsic motivation" is used in the psychology literature to distinguish between behaviour which is motivated by the expectation of an immediate, quantifiable reward (“extrinsic motivation") and behaviour which arises because it is inherently useful, interesting or enjoyable. Examples of the latter can include curiosity driven behaviour such as exploration and the accumulation of knowledge, as well as developing skills that might not be immediately useful but that have the potential to be re-used in a variety of different future situations. In this thesis, we examine a candidate for an intrinsic motivation with wide-ranging applicability which we refer to as “future state maximisation". Loosely speaking this is the idea that, taking everything else to be equal, decisions should be made so as to maximally keep one's options open, or to give the maximal amount of control over what one can potentially do in the future. Our goal is to study how this principle can be applied in a quantitative manner, as well as identifying examples of systems where doing so could be useful in either explaining or generating behaviour.
We consider a number of examples, however our primary application is to a model of collective motion in which we consider a group of agents equipped with simple visual sensors, moving around in two dimensions. In this model, agents aim to make decisions about how to move so as to maximise the amount of control they have over the potential visual states that they can access in the future. We find that with each agent following this simple, low-level motivational principle a swarm spontaneously emerges in which the agents exhibit rich collective behaviour, remaining cohesive and highly-aligned. Remarkably, the emergent swarm also shares a number of features which are observed in real flocks of starlings, including scale free correlations and marginal opacity. We go on to explore how the model can be developed to allow us to manipulate and control the swarm, as well as looking at heuristics which are able to mimic future state maximisation whilst requiring significantly less computation, and so which could plausibly operate under animal cognition
Self-organization of R&D search in complex technology spaces
R&D, technology, organization
Near-Optimal Multi-Robot Motion Planning with Finite Sampling
An underlying structure in several sampling-based methods for continuous
multi-robot motion planning (MRMP) is the tensor roadmap (TR), which emerges
from combining multiple PRM graphs constructed for the individual robots via a
tensor product. We study the conditions under which the TR encodes a
near-optimal solution for MRMP---satisfying these conditions implies near
optimality for a variety of popular planners, including dRRT*, and the discrete
methods M* and CBS when applied to the continuous domain. We develop the first
finite-sample analysis of this kind, which specifies the number of samples,
their deterministic distribution, and magnitude of the connection radii that
should be used by each individual PRM graph, to guarantee near-optimality using
the TR. This significantly improves upon a previous asymptotic analysis,
wherein the number of samples tends to infinity, and supports guaranteed
high-quality solutions in practice, within bounded running time. To achieve our
new result, we first develop a sampling scheme, which we call the staggered
grid, for finite-sample motion planning for individual robots, which requires
significantly less samples than previous work. We then extend it to the much
more involved MRMP setting which requires to account for interactions among
multiple robots. Finally, we report on a few experiments that serve as a
verification of our theoretical findings and raise interesting questions for
further investigation.Comment: Submitted to the International Conference on Robotics and Automation
(ICRA), 202
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