4,328 research outputs found
Safe, Remote-Access Swarm Robotics Research on the Robotarium
This paper describes the development of the Robotarium -- a remotely
accessible, multi-robot research facility. The impetus behind the Robotarium is
that multi-robot testbeds constitute an integral and essential part of the
multi-agent research cycle, yet they are expensive, complex, and time-consuming
to develop, operate, and maintain. These resource constraints, in turn, limit
access for large groups of researchers and students, which is what the
Robotarium is remedying by providing users with remote access to a
state-of-the-art multi-robot test facility. This paper details the design and
operation of the Robotarium as well as connects these to the particular
considerations one must take when making complex hardware remotely accessible.
In particular, safety must be built in already at the design phase without
overly constraining which coordinated control programs the users can upload and
execute, which calls for minimally invasive safety routines with provable
performance guarantees.Comment: 13 pages, 7 figures, 3 code samples, 72 reference
Collision Free Navigation of a Multi-Robot Team for Intruder Interception
In this report, we propose a decentralised motion control algorithm for the
mobile robots to intercept an intruder entering (k-intercepting) or escaping
(e-intercepting) a protected region. In continuation, we propose a
decentralized navigation strategy (dynamic-intercepting) for a multi-robot team
known as predators to intercept the intruders or in the other words, preys,
from escaping a siege ring which is created by the predators. A necessary and
sufficient condition for the existence of a solution of this problem is
obtained. Furthermore, we propose an intelligent game-based decision-making
algorithm (IGD) for a fleet of mobile robots to maximize the probability of
detection in a bounded region. We prove that the proposed decentralised
cooperative and non-cooperative game-based decision-making algorithm enables
each robot to make the best decision to choose the shortest path with minimum
local information. Then we propose a leader-follower based collision-free
navigation control method for a fleet of mobile robots to traverse an unknown
cluttered environment where is occupied by multiple obstacles to trap a target.
We prove that each individual team member is able to traverse safely in the
region, which is cluttered by many obstacles with any shapes to trap the target
while using the sensors in some indefinite switching points and not
continuously, which leads to saving energy consumption and increasing the
battery life of the robots consequently. And finally, we propose a novel
navigation strategy for a unicycle mobile robot in a cluttered area with moving
obstacles based on virtual field force algorithm. The mathematical proof of the
navigation laws and the computer simulations are provided to confirm the
validity, robustness, and reliability of the proposed methods
Distributed navigation of multi-robot systems for sensing coverage
A team of coordinating mobile robots equipped with operation specific sensors can
perform different coverage tasks. If the required number of robots in the team is
very large then a centralized control system becomes a complex strategy. There
are also some areas where centralized communication turns into an issue. So, a
team of mobile robots for coverage tasks should have the ability of decentralized or
distributed decision making. This thesis investigates decentralized control of mobile
robots specifically for coverage problems. A decentralized control strategy is ideally
based on local information and it can offer flexibility in case there is an increment
or decrement in the number of mobile robots. We perform a broad survey of the
existing literature for coverage control problems. There are different approaches
associated with decentralized control strategy for coverage control problems. We
perform a comparative review of these approaches and use the approach based on
simple local coordination rules. These locally computed nearest neighbour rules are
used to develop decentralized control algorithms for coverage control problems.
We investigate this extensively used nearest neighbour rule-based approach for
developing coverage control algorithms. In this approach, a mobile robot gives an
equal importance to every neighbour robot coming under its communication range.
We develop our control approach by making some of the mobile robots playing
a more influential role than other members of the team. We develop the control
algorithm based on nearest neighbour rules with weighted average functions. The
approach based on this control strategy becomes efficient in terms of achieving a
consensus on control inputs, say heading angle, velocity, etc.
The decentralized control of mobile robots can also exhibit a cyclic behaviour
under some physical constraints like a quantized orientation of the mobile robot.
We further investigate the cyclic behaviour appearing due to the quantized control
of mobile robots under some conditions. Our nearest neighbour rule-based approach
offers a biased strategy in case of cyclic behaviour appearing in the team of mobile
robots.
We consider a clustering technique inside the team of mobile robots. Our decentralized
control strategy calculates the similarity measure among the neighbours
of a mobile robot. The team of mobile robots with the similarity measure based
approach becomes efficient in achieving a fast consensus like on heading angle or
velocity. We perform a rigorous mathematical analysis of our developed approach.
We also develop a condition based on relaxed criteria for achieving consensus on
velocity or heading angle of the mobile robots. Our validation approach is based on
mathematical arguments and extensive computer simulations
A Survey on Aerial Swarm Robotics
The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas
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