7,967 research outputs found
Rendezvous of Two Robots with Constant Memory
We study the impact that persistent memory has on the classical rendezvous
problem of two mobile computational entities, called robots, in the plane. It
is well known that, without additional assumptions, rendezvous is impossible if
the entities are oblivious (i.e., have no persistent memory) even if the system
is semi-synchronous (SSynch). It has been recently shown that rendezvous is
possible even if the system is asynchronous (ASynch) if each robot is endowed
with O(1) bits of persistent memory, can transmit O(1) bits in each cycle, and
can remember (i.e., can persistently store) the last received transmission.
This setting is overly powerful.
In this paper we weaken that setting in two different ways: (1) by
maintaining the O(1) bits of persistent memory but removing the communication
capabilities; and (2) by maintaining the O(1) transmission capability and the
ability to remember the last received transmission, but removing the ability of
an agent to remember its previous activities. We call the former setting
finite-state (FState) and the latter finite-communication (FComm). Note that,
even though its use is very different, in both settings, the amount of
persistent memory of a robot is constant.
We investigate the rendezvous problem in these two weaker settings. We model
both settings as a system of robots endowed with visible lights: in FState, a
robot can only see its own light, while in FComm a robot can only see the other
robot's light. We prove, among other things, that finite-state robots can
rendezvous in SSynch, and that finite-communication robots are able to
rendezvous even in ASynch. All proofs are constructive: in each setting, we
present a protocol that allows the two robots to rendezvous in finite time.Comment: 18 pages, 3 figure
An Unsupervised Neural Network for Real-Time Low-Level Control of a Mobile Robot: Noise Resistance, Stability, and Hardware Implementation
We have recently introduced a neural network mobile robot controller (NETMORC). The controller is based on earlier neural network models of biological sensory-motor control. We have shown that NETMORC is able to guide a differential drive mobile robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. Furthermore, NETMORC is able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we first review the NETMORC architecture, and then we prove that NETMORC is asymptotically stable. After presenting a series of simulations results showing robustness to disturbances, we compare NETMORC performance on a trajectory-following task with the performance of an alternative controller. Finally, we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499
On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages
We extend our study of Motion Planning via Manifold Samples (MMS), a general
algorithmic framework that combines geometric methods for the exact and
complete analysis of low-dimensional configuration spaces with sampling-based
approaches that are appropriate for higher dimensions. The framework explores
the configuration space by taking samples that are entire low-dimensional
manifolds of the configuration space capturing its connectivity much better
than isolated point samples. The contributions of this paper are as follows:
(i) We present a recursive application of MMS in a six-dimensional
configuration space, enabling the coordination of two polygonal robots
translating and rotating amidst polygonal obstacles. In the adduced experiments
for the more demanding test cases MMS clearly outperforms PRM, with over
20-fold speedup in a coordination-tight setting. (ii) A probabilistic
completeness proof for the most prevalent case, namely MMS with samples that
are affine subspaces. (iii) A closer examination of the test cases reveals that
MMS has, in comparison to standard sampling-based algorithms, a significant
advantage in scenarios containing high-dimensional narrow passages. This
provokes a novel characterization of narrow passages which attempts to capture
their dimensionality, an attribute that had been (to a large extent) unattended
in previous definitions.Comment: 20 page
Getting Close Without Touching: Near-Gathering for Autonomous Mobile Robots
In this paper we study the Near-Gathering problem for a finite set of
dimensionless, deterministic, asynchronous, anonymous, oblivious and autonomous
mobile robots with limited visibility moving in the Euclidean plane in
Look-Compute-Move (LCM) cycles. In this problem, the robots have to get close
enough to each other, so that every robot can see all the others, without
touching (i.e., colliding with) any other robot. The importance of solving the
Near-Gathering problem is that it makes it possible to overcome the restriction
of having robots with limited visibility. Hence it allows to exploit all the
studies (the majority, actually) done on this topic in the unlimited visibility
setting. Indeed, after the robots get close enough to each other, they are able
to see all the robots in the system, a scenario that is similar to the one
where the robots have unlimited visibility.
We present the first (deterministic) algorithm for the Near-Gathering
problem, to the best of our knowledge, which allows a set of autonomous mobile
robots to nearly gather within finite time without ever colliding. Our
algorithm assumes some reasonable conditions on the input configuration (the
Near-Gathering problem is easily seen to be unsolvable in general). Further,
all the robots are assumed to have a compass (hence they agree on the "North"
direction), but they do not necessarily have the same handedness (hence they
may disagree on the clockwise direction).
We also show how the robots can detect termination, i.e., detect when the
Near-Gathering problem has been solved. This is crucial when the robots have to
perform a generic task after having nearly gathered. We show that termination
detection can be obtained even if the total number of robots is unknown to the
robots themselves (i.e., it is not a parameter of the algorithm), and robots
have no way to explicitly communicate.Comment: 25 pages, 8 fiugre
Learning to Understand by Evolving Theories
In this paper, we describe an approach that enables an autonomous system to
infer the semantics of a command (i.e. a symbol sequence representing an
action) in terms of the relations between changes in the observations and the
action instances. We present a method of how to induce a theory (i.e. a
semantic description) of the meaning of a command in terms of a minimal set of
background knowledge. The only thing we have is a sequence of observations from
which we extract what kinds of effects were caused by performing the command.
This way, we yield a description of the semantics of the action and, hence, a
definition.Comment: KRR Workshop at ICLP 201
Metric Learning for Generalizing Spatial Relations to New Objects
Human-centered environments are rich with a wide variety of spatial relations
between everyday objects. For autonomous robots to operate effectively in such
environments, they should be able to reason about these relations and
generalize them to objects with different shapes and sizes. For example, having
learned to place a toy inside a basket, a robot should be able to generalize
this concept using a spoon and a cup. This requires a robot to have the
flexibility to learn arbitrary relations in a lifelong manner, making it
challenging for an expert to pre-program it with sufficient knowledge to do so
beforehand. In this paper, we address the problem of learning spatial relations
by introducing a novel method from the perspective of distance metric learning.
Our approach enables a robot to reason about the similarity between pairwise
spatial relations, thereby enabling it to use its previous knowledge when
presented with a new relation to imitate. We show how this makes it possible to
learn arbitrary spatial relations from non-expert users using a small number of
examples and in an interactive manner. Our extensive evaluation with real-world
data demonstrates the effectiveness of our method in reasoning about a
continuous spectrum of spatial relations and generalizing them to new objects.Comment: Accepted at the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems. The new Freiburg Spatial Relations Dataset and a demo
video of our approach running on the PR-2 robot are available at our project
website: http://spatialrelations.cs.uni-freiburg.d
- …