2,578 research outputs found
Multi-Agent Motion Planning and Object Transportation under High Level Goals
This paper presents a hybrid control framework for the motion planning of a
multi-agent system including N robotic agents and M objects, under high level
goals. In particular, we design control protocols that allow the transition of
the agents as well as the transportation of the objects by the agents, among
predefined regions of interest in the workspace. This allows us to abstract the
coupled behavior of the agents and the objects as a finite transition system
and to design a high-level multi-agent plan that satisfies the agents' and the
objects' specifications, given as temporal logic formulas. Simulation results
verify the proposed framework.Comment: To appear in the World Congress of the International Federation of
Automatic Control (IFAC), Toulouse, France, July 201
Decentralized Abstractions for Feedback Interconnected Multi-Agent Systems
The purpose of this report is to define abstractions for multi-agent systems
under coupled constraints. In the proposed decentralized framework, we specify
a finite or countable transition system for each agent which only takes into
account the discrete positions of its neighbors. The dynamics of the considered
systems consist of two components. An appropriate feedback law which guarantees
that certain performance requirements (eg. connectivity) are preserved and
induces the coupled constraints and additional free inputs which we exploit in
order to accomplish high level tasks. In this work we provide sufficient
conditions on the space and time discretization of the system which ensure that
we can extract a well posed and hence meaningful finite transition system.Comment: 15 page
Prescribed Performance Control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks
Signal temporal logic (STL) provides a user-friendly interface for defining
complex tasks for robotic systems. Recent efforts aim at designing control laws
or using reinforcement learning methods to find policies which guarantee
satisfaction of these tasks. While the former suffer from the trade-off between
task specification and computational complexity, the latter encounter
difficulties in exploration as the tasks become more complex and challenging to
satisfy. This paper proposes to combine the benefits of the two approaches and
use an efficient prescribed performance control (PPC) base law to guide
exploration within the reinforcement learning algorithm. The potential of the
method is demonstrated in a simulated environment through two sample
navigational tasks.Comment: This is the extended version of the paper accepted to the 2019
American Control Conference (ACC), Philadelphia (to be published
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