462 research outputs found
Improving Robotic Decision-Making in Unmodeled Situations
Existing methods of autonomous robotic decision-making are often fragile when faced with inaccurate or incompletely modeled distributions of uncertainty, also known as ambiguity. While decision-making under ambiguity is a field of study that has been gaining interest, many existing methods tend to be computationally challenging, require many assumptions about the nature of the problem, and often require much prior knowledge. Therefore, they do not scale well to complex real-world problems where fulfilling all of these requirements is often impractical if not impossible. The research described in this dissertation investigates novel approaches to robotic decision-making strategies which are resilient to ambiguity that are not subject to as many of these requirements as most existing methods. The novel frameworks described in this research incorporate physical feedback, diversity, and swarm local interactions, three factors that are hypothesized to be key in creating resilience to ambiguity. These three factors are inspired by examples of robots which demonstrate resilience to ambiguity, ranging from simple vibrobots to decentralized robotic swarms. The proposed decision-making methods, based around a proposed framework known as Ambiguity Trial and Error (AT&E), are tested for both single robots and robotic swarms in several simulated robotic foraging case studies, and a real-world robotic foraging experiment. A novel method for transferring swarm resilience properties back to single agent decision-making is also explored. The results from the case studies show that the proposed methods demonstrate resilience to varying types of ambiguities, both stationary and non-stationary, while not requiring accurate modeling and assumptions, large amounts of prior training data, or computationally expensive decision-making policy solvers. Conclusions about these novel methods are then drawn from the simulation and experiment results and the future research directions leveraging the lessons learned from this research are discussed
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Analysis of Dynamic Task Allocation in Multi-Robot Systems
Dynamic task allocation is an essential requirement for multi-robot systems
operating in unknown dynamic environments. It allows robots to change their
behavior in response to environmental changes or actions of other robots in
order to improve overall system performance. Emergent coordination algorithms
for task allocation that use only local sensing and no direct communication
between robots are attractive because they are robust and scalable. However, a
lack of formal analysis tools makes emergent coordination algorithms difficult
to design. In this paper we present a mathematical model of a general dynamic
task allocation mechanism. Robots using this mechanism have to choose between
two types of task, and the goal is to achieve a desired task division in the
absence of explicit communication and global knowledge. Robots estimate the
state of the environment from repeated local observations and decide which task
to choose based on these observations. We model the robots and observations as
stochastic processes and study the dynamics of the collective behavior.
Specifically, we analyze the effect that the number of observations and the
choice of the decision function have on the performance of the system. The
mathematical models are validated in a multi-robot multi-foraging scenario. The
model's predictions agree very closely with experimental results from
sensor-based simulations.Comment: Preprint version of the paper published in International Journal of
Robotics, March 2006, Volume 25, pp. 225-24
Information Exchange Design Patterns for Robot Swarm Foraging and Their Application in Robot Control Algorithms
In swarm robotics, a design pattern provides high-level guidelines for the implementation of a particular robot behaviour and describes its impact on swarm performance. In this paper, we explore information exchange design patterns for robot swarm foraging. First, a method for the specification of design patterns for robot swarms is proposed that builds on previous work in this field and emphasises modular behaviour design, as well as information-centric micro-macro link analysis. Next, design pattern application rules that can facilitate the pattern usage in robot control algorithms are given. A catalogue of six design patterns is then presented. The patterns are derived from an extensive list of experiments reported in the swarm robotics literature, demonstrating the capability of the proposed method to identify distinguishing features of robot behaviour and their impact on swarm performance in a wide range of swarm implementations and experimental scenarios. Each pattern features a detailed description of robot behaviour and its associated parameters, facilitated by the usage of a multi-agent modeling language, BDRML, and an account of feedback loops and forces that affect the pattern's applicability. Scenarios in which the pattern has been used are described. The consequences of each design pattern on overall swarm performance are characterised within the Information-Cost-Reward framework, that makes it possible to formally relate the way in which robots acquire, share and utilise information. Finally, the patterns are validated by demonstrating how they improved the performance of foraging e-puck swarms and how they could guide algorithm design in other scenarios
Sophisticated collective foraging with minimalist agents: a swarm robotics test
How groups of cooperative foragers can achieve efficient and robust
collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality
trade-offs and swarm-size-dependent foraging strategies. Here we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly
simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments
conducted with more capable real ants that sense pheromone concentration and
follow its gradient. One key feature of our controllers is a control parameter which
balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for
distance and quality of resources, as well as overcrowding, and predicts a swarmsize-dependent strategy. We test swarms implementing our controllers against our
optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates
the sufficiency of simple individual agent rules to generate sophisticated collective
foraging behaviour
Task partitioning for foraging robot swarms based on penalty and reward
This thesis is concerned with foraging robots that are retrieving items to a destination using odometry for navigation in enclosed environments, and their susceptibility to dead-reckoning noise. Such noise causes the location of targets recorded by the robots to appear to change over time, thus reducing the ability of the robots to return to the same location. Previous work on task partitioning was attempted in an effort to decrease this error and increase the rate of item collection by making the robots travel shorter distances. \par
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Dynamic Partitioning Strategy (DPS) is introduced and explored in this thesis which adjusts the travelling distance from the items location to a collection point as the robots locate the items, through the use of a penalty and reward mechanism. Robots adapt according to their dead-reckoning error rates, where the probability of finding items is related to the ratio between the penalty and the reward parameters. \par
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In addition, the diversity in the degrees of error within the members of a robot swarm and the performance repercussion in task partitioning foraging tasks is explored. This is achieved by following an experimental framework composed of three stages: emulation, simulation and hardware. An emulation is generated from an ensemble of machine learning techniques. The emulator allows to perform enriched analyses of simulations of the swarm from a global perspective in a relatively low time compared with experiments in simulations and hardware. Experiments with simulation and hardware provide the contribution of each robot in the swarm to the task
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