58 research outputs found
A Software Suite for the Control and the Monitoring of Adaptive Robotic Ecologies
Adaptive robotic ecologies are networks of heterogeneous robotic devices (sensors, actuators, automated appliances) pervasively embedded in everyday environments, where they learn to cooperate towards the achievement of complex tasks. While their flexibility makes them an increasingly popular way to improve a system’s reliability, scalability, robustness and autonomy, their effective realisation demands integrated control and software solutions for the specification, integration and management of their highly heterogeneous and computational constrained components. In this extended abstract we briefly illustrate the characteristic requirements dictated by robotic ecologies, discuss our experience in developing adaptive robotic ecologies, and provide an overview of the specific solutions developed as part of the EU FP7 RUBICON Project
GRAPE-S: Near Real-Time Coalition Formation for Multiple Service Collectives
Robotic collectives for military and disaster response applications require
coalition formation algorithms to partition robots into appropriate task teams.
Collectives' missions will often incorporate tasks that require multiple
high-level robot behaviors or services, which coalition formation must
accommodate. The highly dynamic and unstructured application domains also
necessitate that coalition formation algorithms produce near optimal solutions
(i.e., >95% utility) in near real-time (i.e., <5 minutes) with very large
collectives (i.e., hundreds of robots). No previous coalition formation
algorithm satisfies these requirements. An initial evaluation found that
traditional auction-based algorithms' runtimes are too long, even though the
centralized simulator incorporated ideal conditions unlikely to occur in
real-world deployments (i.e., synchronization across robots and perfect,
instantaneous communication). The hedonic game-based GRAPE algorithm can
produce solutions in near real-time, but cannot be applied to multiple service
collectives. This manuscript integrates GRAPE and a services model, producing
GRAPE-S and Pair-GRAPE-S. These algorithms and two auction baselines were
evaluated using a centralized simulator with up to 1000 robots, and via the
largest distributed coalition formation simulated evaluation to date, with up
to 500 robots. The evaluations demonstrate that auctions transfer poorly to
distributed collectives, resulting in excessive runtimes and low utility
solutions. GRAPE-S satisfies the target domains' coalition formation
requirements, producing near optimal solutions in near real-time, and
Pair-GRAPE-S more than satisfies the domain requirements, producing optimal
solutions in near real-time. GRAPE-S and Pair-GRAPE-S are the first algorithms
demonstrated to support near real-time coalition formation for very large,
distributed collectives with multiple services
A Framework for Collaborative Multi-task, Multi-robot Missions
Robotics is a transformative technology that will empower our civilization for a new scale of human endeavors. Massive scale is only possible through the collaboration of individual or groups of robots. Collaboration allows specialization, meaning a multirobot system may accommodate heterogeneous platforms including human partners.
This work develops a unified control architecture for collaborative missions comprised of multiple, multi-robot tasks. Using kinematic equations and Jacobian matrices, the system states are transformed into alternative control spaces which are more useful for the designer or more convenient for the operator. The architecture allows multiple tasks to be combined, composing tightly coordinated missions. Using this approach, the designer is able to compensate for non-ideal behavior in the appropriate space using whatever control scheme they choose. This work presents a general design methodology, including analysis techniques for relevant control metrics like stability, responsiveness, and disturbance rejection, which were missing in prior work.
Multiple tasks may be combined into a collaborative mission. The unified motion control architecture merges the control space components for each task into a concise federated system to facilitate analysis and implementation. The task coordination function defines task commands as functions of mission commands and state values to create explicit closed-loop collaboration. This work presents analysis techniques to understand the effects of cross-coupling tasks. This work analyzes system stability for the particular control architecture and identifies an explicit condition to ensure stable switching when reallocating robots. We are unaware of any other automated control architectures that address large-scale collaborative systems composed of task-oriented multi-robot coalitions where relative spatial control is critical to mission performance.
This architecture and methodology have been validated in experiments and in simulations, repeating earlier work and exploring new scenarios and. It can perform large-scale, complex missions via a rigorous design methodology
The Viability of Domain Constrained Coalition Formation for Robotic Collectives
Applications, such as military and disaster response, can benefit from
robotic collectives' ability to perform multiple cooperative tasks (e.g.,
surveillance, damage assessments) efficiently across a large spatial area.
Coalition formation algorithms can potentially facilitate collective robots'
assignment to appropriate task teams; however, most coalition formation
algorithms were designed for smaller multiple robot systems (i.e., 2-50
robots). Collectives' scale and domain-relevant constraints (i.e.,
distribution, near real-time, minimal communication) make coalition formation
more challenging. This manuscript identifies the challenges inherent to
designing coalition formation algorithms for very large collectives (e.g., 1000
robots). A survey of multiple robot coalition formation algorithms finds that
most are unable to transfer directly to collectives, due to the identified
system differences; however, auctions and hedonic games may be the most
transferable. A simulation-based evaluation of three auction and hedonic game
algorithms, applied to homogeneous and heterogeneous collectives, demonstrates
that there are collective compositions for which no existing algorithm is
viable; however, the experimental results and literature survey suggest paths
forward.Comment: 46 pages, 9 figures, Swarm Intelligence (under review
Coalition Formation and Execution in Multi-robot Tasks
In this research, I explore several related problems in distributed robot systems that must be addressed in order to achieve multi-robot tasks, in which individual robots may not possess all the required capabilities. While most previous research work on multi-robot cooperation mainly concentrates on loosely-coupled multi-robot tasks, a more challenging problem is to also address tightly-coupled multi- robot tasks involving close robot interactions, which often require capability sharing. Three related topics towards addressing these tasks are discussed, as follows:
Forming coalitions, which determines how robots should form into subgroups (i.e., coalitions) to address individual tasks. To achieve system autonomy, the ability to identify the feasibility of potential solutions is critical for forming coalitions. A general IQ-ASyMTRe architecture, which is formally proven to be sound and complete in this research, is introduced to incorporate this capability based on the ASyMTRe architecture.
Executing coalitions, which coordinates different robots within the same coalition during physical execution to accomplish individual tasks. For executing coalitions, the IQ-ASyMTRe+ approach is presented. An information quality measure is introduced to control the robots to maintain the required constraints for task execution in dynamic environment. Redundancies at sensory and computational levels are utilized to enable execution that is robust to internal and external influences.
Task allocation, which optimizes the overall performance of the system when multiple tasks need to be addressed. In this research, this problem is analyzed and the formulation is extended. A new greedy heuristic is introduced, which considers inter-task resource constraints to approximate the influence between different assignments in task allocation.
Through combining the above approaches, a framework that achieves system autonomy can be created for addressing multi-robot tasks
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