20,612 research outputs found
CARE: Cooperative Autonomy for Resilience and Efficiency of Robot Teams for Complete Coverage of Unknown Environments under Robot Failures
This paper addresses the problem of Multi-robot Coverage Path Planning (MCPP)
for unknown environments in the presence of robot failures. Unexpected robot
failures can seriously degrade the performance of a robot team and in extreme
cases jeopardize the overall operation. Therefore, this paper presents a
distributed algorithm, called Cooperative Autonomy for Resilience and
Efficiency (CARE), which not only provides resilience to the robot team against
failures of individual robots, but also improves the overall efficiency of
operation via event-driven replanning. The algorithm uses distributed Discrete
Event Supervisors (DESs), which trigger games between a set of feasible players
in the event of a robot failure or idling, to make collaborative decisions for
task reallocations. The game-theoretic structure is built using Potential
Games, where the utility of each player is aligned with a shared objective
function for all players. The algorithm has been validated in various complex
scenarios on a high-fidelity robotic simulator, and the results demonstrate
that the team achieves complete coverage under failures, reduced coverage time,
and faster target discovery as compared to three alternative methods
3D multi-robot patrolling with a two-level coordination strategy
Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks
Decentralized Informative Path Planning with Exploration-Exploitation Balance for Swarm Robotic Search
Swarm robotic search is concerned with searching targets in unknown
environments (e.g., for search and rescue or hazard localization), using a
large number of collaborating simple mobile robots. In such applications,
decentralized swarm systems are touted for their task/coverage scalability,
time efficiency, and fault tolerance. To guide the behavior of such swarm
systems, two broad classes of approaches are available, namely nature-inspired
swarm heuristics and multi-robotic search methods. However, simultaneously
offering computationally-efficient scalability and fundamental insights into
the exhibited behavior (instead of a black-box behavior model), remains
challenging under either of these two class of approaches. In this paper, we
develop an important extension of the batch Bayesian search method for
application to embodied swarm systems, searching in a physical 2D space. Key
contributions lie in: 1) designing an acquisition function that not only
balances exploration and exploitation across the swarm, but also allows
modeling knowledge extraction over trajectories; and 2) developing its
distributed implementation to allow asynchronous task inference and path
planning by the swarm robots. The resulting collective informative path
planning approach is tested on target search case studies of varying
complexity, where the target produces a spatially varying (measurable) signal.
Significantly superior performance, in terms of mission completion efficiency,
is observed compared to exhaustive search and random walk baselines, along with
favorable performance scalability with increasing swarm size.Comment: Accepted for presentation in (and publication in the proceedings of)
The ASME 2019 International Design Engineering Technical Conferences &
Computers and Information in Engineering Conference (IDETC/CIE 2019
Artificial Intelligence-Based Techniques for Emerging Robotics Communication: A Survey and Future Perspectives
This paper reviews the current development of artificial intelligence (AI)
techniques for the application area of robot communication. The study of the
control and operation of multiple robots collaboratively toward a common goal
is fast growing. Communication among members of a robot team and even including
humans is becoming essential in many real-world applications. The survey
focuses on the AI techniques for robot communication to enhance the
communication capability of the multi-robot team, making more complex
activities, taking an appreciated decision, taking coordinated action, and
performing their tasks efficiently.Comment: 11 pages, 6 figure
Multi-robot coverage to locate fixed targets using formation structures
This paper develops an algorithm that guides a multi-robot system in an
unknown environment in search of fixed targets. The area to be scanned contains
an unknown number of convex obstacles of unknown size and shape. The algorithm
covers the entire free space in a sweeping fashion and as such relies on the
use of robot formations. The geometry of the robot group is a lateral line
formation, which is allowed to split and rejoin when passing obstacles. It is
our main goal to exploit this formation structure in order to reduce robot
resources to a minimum. Each robot has a limited and finite amount of memory
available. No information of the topography is recorded. Communication between
two robots is only possible up to a maximum inter-robot distance, and if the
line-of-sight between both robots is not obstructed. Broadcasting capabilities
and indirect communication are not allowed. Supervisory control is prohibited.
The number of robots equipped with GPS is kept as small as possible.
Applications of the algorithm are mine field clearance, search-and-rescue
missions, and intercept missions. Simulations are included and made available
on the internet, demonstrating the flexibility of the algorithm
A Coordinated Search Strategy for Multiple Solitary Robots: An Extension
The problem of coordination without a priori information about the
environment is important in robotics. Applications vary from formation control
to search and rescue. This paper considers the problem of search by a group of
solitary robots: self-interested robots without a priori knowledge about each
other, and with restricted communication capacity. When the capacity of robots
to communicate is limited, they may obliviously search in overlapping regions
(i.e. be subject to interference). Interference hinders robot progress, and
strategies have been proposed in the literature to mitigate interference [1],
[2]. Interaction of solitary robots has attracted much interest in robotics,
but the problem of mitigating interference when time for search is limited
remains an important area of research. We propose a coordination strategy based
on the method of cellular decomposition [3] where we employ the concept of soft
obstacles: a robot considers cells assigned to other robots as obstacles. The
performance of the proposed strategy is demonstrated by means of simulation
experiments. Simulations indicate the utility of the strategy in situations
where a known upper bound on the search time precludes search of the entire
environment.Comment: This paper has 9 two-columns pages with 33 figures. It is an
extension (in terms of theoretical and practical investigation) of a
conference paper published on 2019 Third IEEE International Conference on
Robotic Computing (IRC). Revised title (contents are unchanged) because
Google Scholar is failing to distinguish it from an earlier versio
Voluntary Retreat for Decentralized Interference Reduction in Robot Swarms
In densely-packed robot swarms operating in confined regions, spatial
interference -- which manifests itself as a competition for physical space --
forces robots to spend more time navigating around each other rather than
performing the primary task. This paper develops a decentralized algorithm that
enables individual robots to decide whether to stay in the region and
contribute to the overall mission, or vacate the region so as to reduce the
negative effects that interference has on the overall efficiency of the swarm.
We develop this algorithm in the context of a distributed collection task,
where a team of robots collect and deposit objects from one set of locations to
another in a given region. Robots do not communicate and use only binary
information regarding the presence of other robots around them to make the
decision to stay or retreat. We illustrate the efficacy of the algorithm with
experiments on a team of real robots.Comment: 7 pages, 6 figures. Accepted for publication, and will be presented
at the International Conference on Robotics and Automation (ICRA) 2019,
Montreal, Canad
Exploiting Heterogeneous Robotic Systems in Cooperative Missions
In this paper we consider the problem of coordinating robotic systems with
different kinematics, sensing and vision capabilities to achieve certain
mission goals. An approach that makes use of a heterogeneous team of agents has
several advantages when cost, integration of capabilities, or large search
areas need to be considered. A heterogeneous team allows for the robots to
become "specialized", accomplish sub-goals more effectively, and thus increase
the overall mission efficiency. Two main scenarios are considered in this work.
In the first case study we exploit mobility to implement a power control
algorithm that increases the Signal to Interference plus Noise Ratio (SINR)
among certain members of the network. We create realistic sensing fields and
manipulation by using the geometric properties of the sensor field-of-view and
the manipulability metric, respectively. The control strategy for each agent of
the heterogeneous system is governed by an artificial physics law that
considers the different kinematics of the agents and the environment, in a
decentralized fashion. Through simulation results we show that the network is
able to stay connected at all times and covers the environment well. The second
scenario studied in this paper is the biologically-inspired coordination of
heterogeneous physical robotic systems. A team of ground rovers, designed to
emulate desert seed-harvester ants, explore an experimental area using
behaviors fine-tuned in simulation by a genetic algorithm. Our robots
coordinate with a base station and collect clusters of resources scattered
within the experimental space. We demonstrate experimentally that through
coordination with an aerial vehicle, our ant-like ground robots are able to
collect resources two times faster than without the use of heterogeneous
coordination
Connectivity maintenance by robotic Mobile Ad-hoc NETwork
The problem of maintaining a wireless communication link between a fixed base
station and an autonomous agent by means of a team of mobile robots is
addressed in this work. Such problem can be of interest for search and rescue
missions in post disaster scenario where the autonomous agent can be used for
remote monitoring and first hand knowledge of the aftermath, while the mobile
robots can be used to provide the agent the possibility to dynamically send its
collected information to an external base station. To study the problem, a
distributed multi-robot system with wifi communication capabilities has been
developed and used to implement a Mobile Ad-hoc NETwork (MANET) to guarantee
the required multi-hop communication. None of the robots of the team possess
the knowledge of agent's movement, neither they hold a pre-assigned position in
the ad-hoc network but they adapt with respect to the dynamic environmental
situations. This adaptation only requires the robots to have the knowledge of
their position and the possibility to exchange such information with their
one-hop neighbours. Robots' motion is achieved by implementing a behavioural
control, namely the Null-Space based Behavioural control, embedding the
collective mission to achieve the required self-configuration. Validation of
the approach is performed by means of demanding experimental tests involving
five ground mobile robots capable of self localization and dynamic obstacle
avoidance
Aerial Drop of Robots and Sensors for Optimal Area Coverage
The problem of rapid optimal coverage through the distribution a team of
robots or static sensors via means of aerial drop is the topic of this work.
Considering a nonholonomic (fixed-wing) aerial robot that corresponds to the
carrier of a set of small holonomic (rotorcraft) aerial robots as well as
static modules that are all equipped with a camera sensor, we address the
problem of selecting optimal aerial drop times and configurations while the
motion capabilities of the small aerial robots are also exploited to further
survey their area of responsibility until they hit the ground. The overall
solution framework consists of lightweight path-planning algorithms that can
run on virtually any processing unit that might be available on-board.
Evaluation studies in simulation as well as a set of elementary experiments
that prove the validity of important assumptions illustrate the potential of
the approach.Comment: 6 pages, 9 figures, technical repor
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