29,506 research outputs found
Variations on the Stochastic Shortest Path Problem
In this invited contribution, we revisit the stochastic shortest path
problem, and show how recent results allow one to improve over the classical
solutions: we present algorithms to synthesize strategies with multiple
guarantees on the distribution of the length of paths reaching a given target,
rather than simply minimizing its expected value. The concepts and algorithms
that we propose here are applications of more general results that have been
obtained recently for Markov decision processes and that are described in a
series of recent papers.Comment: Invited paper for VMCAI 201
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Tractable Pathfinding for the Stochastic On-Time Arrival Problem
We present a new and more efficient technique for computing the route that
maximizes the probability of on-time arrival in stochastic networks, also known
as the path-based stochastic on-time arrival (SOTA) problem. Our primary
contribution is a pathfinding algorithm that uses the solution to the
policy-based SOTA problem---which is of pseudo-polynomial-time complexity in
the time budget of the journey---as a search heuristic for the optimal path. In
particular, we show that this heuristic can be exceptionally efficient in
practice, effectively making it possible to solve the path-based SOTA problem
as quickly as the policy-based SOTA problem. Our secondary contribution is the
extension of policy-based preprocessing to path-based preprocessing for the
SOTA problem. In the process, we also introduce Arc-Potentials, a more
efficient generalization of Stochastic Arc-Flags that can be used for both
policy- and path-based SOTA. After developing the pathfinding and preprocessing
algorithms, we evaluate their performance on two different real-world networks.
To the best of our knowledge, these techniques provide the most efficient
computation strategy for the path-based SOTA problem for general probability
distributions, both with and without preprocessing.Comment: Submission accepted by the International Symposium on Experimental
Algorithms 2016 and published by Springer in the Lecture Notes in Computer
Science series on June 1, 2016. Includes typographical corrections and
modifications to pre-processing made after the initial submission to SODA'15
(July 7, 2014
Particle Swarm Optimization Based Source Seeking
Signal source seeking using autonomous vehicles is a complex problem. The
complexity increases manifold when signal intensities captured by physical
sensors onboard are noisy and unreliable. Added to the fact that signal
strength decays with distance, noisy environments make it extremely difficult
to describe and model a decay function. This paper addresses our work with
seeking maximum signal strength in a continuous electromagnetic signal source
with mobile robots, using Particle Swarm Optimization (PSO). A one to one
correspondence with swarm members in a PSO and physical Mobile robots is
established and the positions of the robots are iteratively updated as the PSO
algorithm proceeds forward. Since physical robots are responsive to swarm
position updates, modifications were required to implement the interaction
between real robots and the PSO algorithm. The development of modifications
necessary to implement PSO on mobile robots, and strategies to adapt to real
life environments such as obstacles and collision objects are presented in this
paper. Our findings are also validated using experimental testbeds.Comment: 13 pages, 12 figure
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
Model-free reinforcement learning has recently been shown to be effective at
learning navigation policies from complex image input. However, these
algorithms tend to require large amounts of interaction with the environment,
which can be prohibitively costly to obtain on robots in the real world. We
present an approach for efficiently learning goal-directed navigation policies
on a mobile robot, from only a single coverage traversal of recorded data. The
navigation agent learns an effective policy over a diverse action space in a
large heterogeneous environment consisting of more than 2km of travel, through
buildings and outdoor regions that collectively exhibit large variations in
visual appearance, self-similarity, and connectivity. We compare pretrained
visual encoders that enable precomputation of visual embeddings to achieve a
throughput of tens of thousands of transitions per second at training time on a
commodity desktop computer, allowing agents to learn from millions of
trajectories of experience in a matter of hours. We propose multiple forms of
computationally efficient stochastic augmentation to enable the learned policy
to generalise beyond these precomputed embeddings, and demonstrate successful
deployment of the learned policy on the real robot without fine tuning, despite
environmental appearance differences at test time. The dataset and code
required to reproduce these results and apply the technique to other datasets
and robots is made publicly available at rl-navigation.github.io/deployable
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