5 research outputs found
Temporal Logic Task Planning and Intermittent Connectivity Control of Mobile Robot Networks
In this paper, we develop a distributed intermittent communication and task
planning framework for mobile robot teams. The goal of the robots is to
accomplish complex tasks, captured by local Linear Temporal Logic formulas, and
share the collected information with all other robots and possibly also with a
user. Specifically, we consider situations where the robot communication
capabilities are not sufficient to form reliable and connected networks while
the robots move to accomplish their tasks. In this case, intermittent
communication protocols are necessary that allow the robots to temporarily
disconnect from the network in order to accomplish their tasks free of
communication constraints. We assume that the robots can only communicate with
each other when they meet at common locations in space. Our distributed control
framework jointly determines local plans that allow all robots fulfill their
assigned temporal tasks, sequences of communication events that guarantee
information exchange infinitely often, and optimal communication locations that
minimize a desired distance metric. Simulation results verify the efficacy of
the proposed controllers
Sampling-Based Optimal Control Synthesis for Multi-Robot Systems under Global Temporal Tasks
This paper proposes a new optimal control synthesis algorithm for multi-robot
systems under global temporal logic tasks. Existing planning approaches under
global temporal goals rely on graph search techniques applied to a product
automaton constructed among the robots. In this paper, we propose a new
sampling-based algorithm that builds incrementally trees that approximate the
state-space and transitions of the synchronous product automaton. By
approximating the product automaton by a tree rather than representing it
explicitly, we require much fewer memory resources to store it and motion plans
can be found by tracing sequences of parent nodes without the need for
sophisticated graph search methods. This significantly increases the
scalability of our algorithm compared to existing optimal control synthesis
methods. We also show that the proposed algorithm is probabilistically complete
and asymptotically optimal. Finally, we present numerical experiments showing
that our approach can synthesize optimal plans from product automata with
billions of states, which is not possible using standard optimal control
synthesis algorithms or off-the-shelf model checkers
STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems
This paper proposes a new highly scalable and asymptotically optimal control
synthesis algorithm from linear temporal logic specifications, called
for large-Scale optimal Temporal Logic Synthesis, that is
designed to solve complex temporal planning problems in large-scale multi-robot
systems. Existing planning approaches with temporal logic specifications rely
on graph search techniques applied to a product automaton constructed among the
robots. In our previous work, we have proposed a more tractable sampling-based
algorithm that builds incrementally trees that approximate the state-space and
transitions of the synchronous product automaton and does not require
sophisticated graph search techniques. Here, we extend our previous work by
introducing bias in the sampling process which is guided by transitions in the
Bchi automaton that belong to the shortest path to the
accepting states. This allows us to synthesize optimal motion plans from
product automata with hundreds of orders of magnitude more states than those
that existing optimal control synthesis methods or off-the-shelf model checkers
can manipulate. We show that is probabilistically complete
and asymptotically optimal and has exponential convergence rate. This is the
first time that convergence rate results are provided for sampling-based
optimal control synthesis methods. We provide simulation results that show that
can synthesize optimal motion plans for very large
multi-robot systems which is impossible using state-of-the-art methods
Control of Magnetic Microrobot Teams for Temporal Micromanipulation Tasks
In this paper, we present a control framework that allows magnetic microrobot
teams to accomplish complex micromanipulation tasks captured by global Linear
Temporal Logic (LTL) formulas. To address this problem, we propose an optimal
control synthesis method that constructs discrete plans for the robots that
satisfy both the assigned tasks as well as proximity constraints between the
robots due to the physics of the problem. Our proposed algorithm relies on an
existing optimal control synthesis approach combined with a novel
sampling-based technique to reduce the state-space of the product automaton
that is associated with the LTL specifications. The synthesized discrete plans
are executed by the microrobots independently using local magnetic fields.
Simulation studies show that the proposed algorithm can address large-scale
planning problems that cannot be solved using existing optimal control
synthesis approaches. Moreover, we present experimental results that also
illustrate the potential of our method in practice. To the best of our
knowledge, this is the first control framework that allows independent control
of teams of magnetic microrobots for temporal micromanipulation tasks
An Abstraction-Free Method for Multi-Robot Temporal Logic Optimal Control Synthesis
The majority of existing Linear Temporal Logic (LTL) planning methods rely on
the construction of a discrete product automaton, that combines a discrete
abstraction of robot mobility and a Bchi automaton that
captures the LTL specification. Representing this product automaton as a graph
and using graph search techniques, optimal plans that satisfy the LTL task can
be synthesized. However, constructing expressive discrete abstractions makes
the synthesis problem computationally intractable. In this paper, we propose a
new sampling-based LTL planning algorithm that does not require any discrete
abstraction of robot mobility. Instead, it incrementally builds trees that
explore the product state-space, until a maximum number of iterations is
reached or a feasible plan is found. The use of trees makes data storage and
graph search tractable, which significantly increases the scalability of our
algorithm. To accelerate the construction of feasible plans, we introduce bias
in the sampling process which is guided by transitions in the
Bchi automaton that belong to the shortest path to the
accepting states. We show that our planning algorithm, with and without bias,
is probabilistically complete and asymptotically optimal. Finally, we present
numerical experiments showing that our method outperforms relevant temporal
logic planning methods.Comment: 21 pages, 10 figure