39 research outputs found
MAPmAKER: performing multi-robot LTL planning under uncertainty
Robot applications are being increasingly used in real life to help humans performing dangerous, heavy, and/or monotonous tasks. They usually rely on planners that given a robot or a team of robots compute plans that specify how the robot(s) can fulfill their missions. Current robot applications ask for planners that make automated planning possible even when only partial knowledge about the environment in which the robots are deployed is available. To tackle such challenges we developed MAPmAKER, which provides a decentralized planning solution and is able to work in partially known environments. Decentralization is realized by decomposing the robotic team into subteams based on their missions, and then by running a classical planning algorithm. Partial knowledge is handled by calling several times a classical planning algorithm. Demo video available at: https://youtu.be/TJzC_u2yfzQ
Robust Motion Planning employing Signal Temporal Logic
Motion planning classically concerns the problem of accomplishing a goal
configuration while avoiding obstacles. However, the need for more
sophisticated motion planning methodologies, taking temporal aspects into
account, has emerged. To address this issue, temporal logics have recently been
used to formulate such advanced specifications. This paper will consider Signal
Temporal Logic in combination with Model Predictive Control. A robustness
metric, called Discrete Average Space Robustness, is introduced and used to
maximize the satisfaction of specifications which results in a natural
robustness against noise. The comprised optimization problem is convex and
formulated as a Linear Program.Comment: 6 page
Control Synthesis for Multi-Agent Systems under Metric Interval Temporal Logic Specifications
This paper presents a framework for automatic synthesis of a control sequence
for multi-agent systems governed by continuous linear dynamics under timed
constraints. First, the motion of the agents in the workspace is abstracted
into individual Transition Systems (TS). Second, each agent is assigned with an
individual formula given in Metric Interval Temporal Logic (MITL) and in
parallel, the team of agents is assigned with a collaborative team formula. The
proposed method is based on a correct-by-construction control synthesis method,
and hence guarantees that the resulting closed-loop system will satisfy the
specifications. The specifications considers boolean-valued properties under
real-time. Extended simulations has been performed in order to demonstrate the
efficiency of the proposed controllers.Comment: 8 pages version of the accepted paper to IFAC World Congres
Intelligent agent for formal modelling of temporal multi-agent systems
Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system
Robust Decentralized Abstractions for Multiple Mobile Manipulators
This paper addresses the problem of decentralized abstractions for multiple
mobile manipulators with 2nd order dynamics. In particular, we propose
decentralized controllers for the navigation of each agent among predefined
regions of interest in the workspace, while guaranteeing at the same time
inter-agent collision avoidance and connectivity maintenance for a subset of
initially connected agents. In that way, the motion of the coupled multi-agent
system is abstracted into multiple finite transition systems for each agent,
which are then suitable for the application of temporal logic-based high level
plans. The proposed methodology is decentralized, since each agent uses local
information based on limited sensing capabilities. Finally, simulation studies
verify the validity of the approach.Comment: Accepted for publication in the IEEE Conference on Decision and
Control, Melbourne, Australia, 201
Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems
This paper presents a human-robot trust integrated task allocation and motion
planning framework for multi-robot systems (MRS) in performing a set of tasks
concurrently. A set of task specifications in parallel are conjuncted with MRS
to synthesize a task allocation automaton. Each transition of the task
allocation automaton is associated with the total trust value of human in
corresponding robots. Here, the human-robot trust model is constructed with a
dynamic Bayesian network (DBN) by considering individual robot performance,
safety coefficient, human cognitive workload and overall evaluation of task
allocation. Hence, a task allocation path with maximum encoded human-robot
trust can be searched based on the current trust value of each robot in the
task allocation automaton. Symbolic motion planning (SMP) is implemented for
each robot after they obtain the sequence of actions. The task allocation path
can be intermittently updated with this DBN based trust model. The overall
strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask
automata
Probabilistic Plan Synthesis for Coupled Multi-Agent Systems
This paper presents a fully automated procedure for controller synthesis for
multi-agent systems under the presence of uncertainties. We model the motion of
each of the agents in the environment as a Markov Decision Process (MDP)
and we assign to each agent one individual high-level formula given in
Probabilistic Computational Tree Logic (PCTL). Each agent may need to
collaborate with other agents in order to achieve a task. The collaboration is
imposed by sharing actions between the agents. We aim to design local control
policies such that each agent satisfies its individual PCTL formula. The
proposed algorithm builds on clustering the agents, MDP products construction
and controller policies design. We show that our approach has better
computational complexity than the centralized case, which traditionally suffers
from very high computational demands.Comment: IFAC WC 2017, Toulouse, Franc
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