388,701 research outputs found
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is
interacting with an initially unknown environment in real time. In such
simultaneous planning and learning problems, the agent has to select its
actions in a limited amount of time, while sensing only a local part of the
environment centered at the agents current location. Real-time heuristic search
agents select actions using a limited lookahead search and evaluating the
frontier states with a heuristic function. Over repeated experiences, they
refine heuristic values of states to avoid infinite loops and to converge to
better solutions. The wide spread of such settings in autonomous software and
hardware agents has led to an explosion of real-time search algorithms over the
last two decades. Not only is a potential user confronted with a hodgepodge of
algorithms, but he also faces the choice of control parameters they use. In
this paper we address both problems. The first contribution is an introduction
of a simple three-parameter framework (named LRTS) which extracts the core
ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*,
SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they
are unified and extended with additional features. Second, we prove
completeness and convergence of any algorithm covered by the LRTS framework.
Third, we prove several upper-bounds relating the control parameters and
solution quality. Finally, we analyze the influence of the three control
parameters empirically in the realistic scalable domains of real-time
navigation on initially unknown maps from a commercial role-playing game as
well as routing in ad hoc sensor networks
Parallel processing for scientific computations
The main contribution of the effort in the last two years is the introduction of the MOPPS system. After doing extensive literature search, we introduced the system which is described next. MOPPS employs a new solution to the problem of managing programs which solve scientific and engineering applications on a distributed processing environment. Autonomous computers cooperate efficiently in solving large scientific problems with this solution. MOPPS has the advantage of not assuming the presence of any particular network topology or configuration, computer architecture, or operating system. It imposes little overhead on network and processor resources while efficiently managing programs concurrently. The core of MOPPS is an intelligent program manager that builds a knowledge base of the execution performance of the parallel programs it is managing under various conditions. The manager applies this knowledge to improve the performance of future runs. The program manager learns from experience
Asymptotically Optimal Sampling-Based Motion Planning Methods
Motion planning is a fundamental problem in autonomous robotics that requires
finding a path to a specified goal that avoids obstacles and takes into account
a robot's limitations and constraints. It is often desirable for this path to
also optimize a cost function, such as path length.
Formal path-quality guarantees for continuously valued search spaces are an
active area of research interest. Recent results have proven that some
sampling-based planning methods probabilistically converge toward the optimal
solution as computational effort approaches infinity. This survey summarizes
the assumptions behind these popular asymptotically optimal techniques and
provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics,
and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews,
https://www.annualreviews.org/. 25 pages. 2 figure
Exploring the deployment of autonomous medical emergency vessels in island and coastal regions : An overview of the opportunities and challenges
Introduction: Applications of vessels in emergency medical systems: Method: Discussion: Conclusion: Emergency medical systems in island and coastal regions face challenges
such as supply and staffing shortages and a dispersion of resources and people, which
negatively affect the timely and efficient delivery of emergency medical services. This thesis
explores the opportunities and challenges of using autonomous vessels in these systems to
start a discussion, as current research in this area is very limited.
Currently, emergency vessels are
primarily used to transfer patients to hospitals, doctors to emergency sites as well as
equipment between islands. Floating hospitals ships generally combine these functions by
enable comprehensive consultation, diagnosis, and treatment at the emergency sites.
Additionally, rescue and search operations can also be counted among the tasks of
emergency medical systems if one considers an extended range of tasks for these systems.
The location of hub facilities, where autonomous vessels are stationed when not in
operation, is one of the first decisions to be made when integrating those vessels into current
emergency systems. Therefore, the model for solving the maximal covering location
problem is applied and adjusted to cover a wider range of application of vessels in
emergency medical systems. Simulations are conducted to identify opportunities to improve
system performance when setting hub facilities for autonomous vessels.
Hub facilities for autonomous vessels can be located at a greater number of
locations, leading to better population coverage in some cases. Furthermore, the complexity
of response routes can be decreased by the ability of autonomous vessels to transform
current applications of vessels in emergency medical systems. Despite several other
opportunities to reduce response times and use resources more efficiently, there are also
challenges associated with the use of autonomous vessels. Some main challenges are to
successfully integrate the new vessels into the existing system and to ensure their use by the
population. Additionally, the costs of autonomous vessels are likely to exceed those of
conventional vessels requiring in-depth cost-benefit considerations.
Autonomous vessels have a great potential to enhance the performance of
emergency medical systems in island and coastal regions. Most of the challenges can be
mitigated by carefully planning their operations and introduction of the vessels into the
existing system. However, in the context of scarce funding, higher costs compared to
conventional vessels are likely to be the most significant challenge for the introduction of
autonomous vessels.nhhma
Metamorphic testing: testing the untestable
What if we could know that a program is buggy, even if we could not tell whether or not its observed output is correct? This is one of the key strengths of metamorphic testing, a technique where failures are not revealed by checking an individual concrete output, but by checking the relations among the inputs and outputs of multiple executions of the program under test. Two decades after its introduction, metamorphic testing has become a fully-fledged testing technique with successful applications in multiple domains, including online search engines, autonomous machinery, compilers, Web APIs, and deep learning programs, among others. This article serves as a hands-on entry point for newcomers to metamorphic testing, describing examples, possible applications, and current limitations, providing readers with the basics for the application of the technique in their own projects. IEE
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
Tactical decision making for autonomous driving is challenging due to the
diversity of environments, the uncertainty in the sensor information, and the
complex interaction with other road users. This paper introduces a general
framework for tactical decision making, which combines the concepts of planning
and learning, in the form of Monte Carlo tree search and deep reinforcement
learning. The method is based on the AlphaGo Zero algorithm, which is extended
to a domain with a continuous state space where self-play cannot be used. The
framework is applied to two different highway driving cases in a simulated
environment and it is shown to perform better than a commonly used baseline
method. The strength of combining planning and learning is also illustrated by
a comparison to using the Monte Carlo tree search or the neural network policy
separately
Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software
It is important to be able to establish formal performance bounds for
autonomous systems. However, formal verification techniques require a model of
the environment in which the system operates; a challenge for autonomous
systems, especially those expected to operate over longer timescales. This
paper describes work in progress to automate the monitor and repair of
ROS-based autonomous robot software written for an a-priori partially known and
possibly incorrect environment model. A taint analysis method is used to
automatically extract the data-flow sequence from input topic to publish topic,
and instrument that code. A unique reinforcement learning approximation of MDP
utility is calculated, an empirical and non-invasive characterization of the
inherent objectives of the software designers. By comparing off-line (a-priori)
utility with on-line (deployed system) utility, we show, using a small but real
ROS example, that it's possible to monitor a performance criterion and relate
violations of the criterion to parts of the software. The software is then
patched using automated software repair techniques and evaluated against the
original off-line utility.Comment: IEEE Workshop on Assured IEEE Workshop on Assured Autonomous Systems,
May, 202
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