504 research outputs found
Decentralized Abstractions and Timed Constrained Planning of a General Class of Coupled Multi-Agent Systems
This paper presents a fully automated procedure for controller synthesis for
a general class of multi-agent systems under coupling constraints. Each agent
is modeled with dynamics consisting of two terms: the first one models the
coupling constraints and the other one is an additional bounded control input.
We aim to design these inputs so that each agent meets an individual high-level
specification given as a Metric Interval Temporal Logic (MITL). Furthermore,
the connectivity of the initially connected agents, is required to be
maintained. First, assuming a polyhedral partition of the workspace, a novel
decentralized abstraction that provides controllers for each agent that
guarantee the transition between different regions is designed. The controllers
are the solution of a Robust Optimal Control Problem (ROCP) for each agent.
Second, by utilizing techniques from formal verification, an algorithm that
computes the individual runs which provably satisfy the high-level tasks is
provided. Finally, simulation results conducted in MATLAB environment verify
the performance of the proposed framework
A Nonlinear Model Predictive Control Scheme for Cooperative Manipulation with Singularity and Collision Avoidance
This paper addresses the problem of cooperative transportation of an object
rigidly grasped by robotic agents. In particular, we propose a Nonlinear
Model Predictive Control (NMPC) scheme that guarantees the navigation of the
object to a desired pose in a bounded workspace with obstacles, while complying
with certain input saturations of the agents. Moreover, the proposed
methodology ensures that the agents do not collide with each other or with the
workspace obstacles as well as that they do not pass through singular
configurations. The feasibility and convergence analysis of the NMPC are
explicitly provided. Finally, simulation results illustrate the validity and
efficiency of the proposed method.Comment: Simulation results with 3 agents adde
Communication-based Decentralized Cooperative Object Transportation Using Nonlinear Model Predictive Control
This paper addresses the problem of cooperative transportation of an object
rigidly grasped by N robotic agents. We propose a Nonlinear Model Predictive
Control (NMPC) scheme that guarantees the navigation of the object to a desired
pose in a bounded workspace with obstacles, while complying with certain input
saturations of the agents. The control scheme is based on inter-agent
communication and is decentralized in the sense that each agent calculates its
own control signal. Moreover, the proposed methodology ensures that the agents
do not collide with each other or with the workspace obstacles as well as that
they do not pass through singular configurations. The feasibility and
convergence analysis of the NMPC are explicitly provided. Finally, simulation
results illustrate the validity and efficiency of the proposed method.Comment: European Control Conference 2018. arXiv admin note: text overlap with
arXiv:1705.0142
Barrier-Based Test Synthesis for Safety-Critical Systems Subject to Timed Reach-Avoid Specifications
We propose an adversarial, time-varying test-synthesis procedure for
safety-critical systems without requiring specific knowledge of the underlying
controller steering the system. From a broader test and evaluation context,
determination of difficult tests of system behavior is important as these tests
would elucidate problematic system phenomena before these mistakes can engender
problematic outcomes, e.g. loss of human life in autonomous cars, costly
failures for airplane systems, etc. Our approach builds on existing,
simulation-based work in the test and evaluation literature by offering a
controller-agnostic test-synthesis procedure that provides a series of
benchmark tests with which to determine controller reliability. To achieve
this, our approach codifies the system objective as a timed reach-avoid
specification. Then, by coupling control barrier functions with this class of
specifications, we construct an instantaneous difficulty metric whose minimizer
corresponds to the most difficult test at that system state. We use this
instantaneous difficulty metric in a game-theoretic fashion, to produce an
adversarial, time-varying test-synthesis procedure that does not require
specific knowledge of the system's controller, but can still provably identify
realizable and maximally difficult tests of system behavior. Finally, we
develop this test-synthesis procedure for both continuous and discrete-time
systems and showcase our test-synthesis procedure on simulated and hardware
examples
Agents and Robots for Reliable Engineered Autonomy
This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems
Agent Based Modeling in Land-Use and Land-Cover Change Studies
Agent based models (ABM) for land use and cover change (LUCC) holds the promise to provide new insight into the processes and patterns of the human and biophysical interactions in ways that have never been explored. Advances in computer technology make it possible to run almost infinite numbers of simulations with multiple heterogeneously shaped actors that reciprocally interact via vertical and horizontal power lines on various levels. Based upon an extensive literature review the basic components for such exercises are explored and discussed. This resulted in a systematic representation of these components consisting of: (1) Spatial static input data, (2) Actor and Actor-group static input data, (3) Spatial dynamic input, (4) Actor and Actor-group dynamic input data, (5) the model with the rules describing the rules, (6) Spatial static output, (7) Actor and Actor-group static output, (8) Dynamic output of Actor behaviour changes, (9) Dynamic output of actor-group behavioural changes, (10) Dynamic output of spatial patterns, (11) Dynamic output of temporal patterns.
This representation proves to be epistemologically useful in the analysis of the relationships between the ABM LUCC components. In this paper, this representation is also used to enumerate the strengths and limitations of agent based modelling in LUCC
- …