23,114 research outputs found
Control with Probabilistic Signal Temporal Logic
Autonomous agents often operate in uncertain environments where their
decisions are made based on beliefs over states of targets. We are interested
in controller synthesis for complex tasks defined over belief spaces. Designing
such controllers is challenging due to computational complexity and the lack of
expressivity of existing specification languages. In this paper, we propose a
probabilistic extension to signal temporal logic (STL) that expresses tasks
over continuous belief spaces. We present an efficient synthesis algorithm to
find a control input that maximises the probability of satisfying a given task.
We validate our algorithm through simulations of an unmanned aerial vehicle
deployed for surveillance and search missions.Comment: 7 pages, submitted to the 2016 American Control Conference (ACC 2016)
on September, 30, 2015 (under review
Control with probabilistic signal temporal logic
Autonomous agents often operate in uncertain environments where their decisions are made based on beliefs over states of targets. We are interested in controller synthesis for complex tasks defined over belief spaces. Designing such controllers is challenging due to computational complexity and the lack of expressivity of existing specification languages. In this paper, we propose a probabilistic extension to signal temporal logic (STL) that expresses tasks over continuous belief spaces. We present an efficient synthesis algorithm to find a control input that maximises the probability of satisfying a given task. We validate our algorithm through simulations of an unmanned aerial vehicle deployed for surveillance and search missions
The Planning Spectrum - One, Two, Three, Infinity
Linear Temporal Logic (LTL) is widely used for defining conditions on the
execution paths of dynamic systems. In the case of dynamic systems that allow
for nondeterministic evolutions, one has to specify, along with an LTL formula
f, which are the paths that are required to satisfy the formula. Two extreme
cases are the universal interpretation A.f, which requires that the formula be
satisfied for all execution paths, and the existential interpretation E.f,
which requires that the formula be satisfied for some execution path.
When LTL is applied to the definition of goals in planning problems on
nondeterministic domains, these two extreme cases are too restrictive. It is
often impossible to develop plans that achieve the goal in all the
nondeterministic evolutions of a system, and it is too weak to require that the
goal is satisfied by some execution.
In this paper we explore alternative interpretations of an LTL formula that
are between these extreme cases. We define a new language that permits an
arbitrary combination of the A and E quantifiers, thus allowing, for instance,
to require that each finite execution can be extended to an execution
satisfying an LTL formula (AE.f), or that there is some finite execution whose
extensions all satisfy an LTL formula (EA.f). We show that only eight of these
combinations of path quantifiers are relevant, corresponding to an alternation
of the quantifiers of length one (A and E), two (AE and EA), three (AEA and
EAE), and infinity ((AE)* and (EA)*). We also present a planning algorithm for
the new language that is based on an automata-theoretic approach, and study its
complexity
TALplanner in IPC-2002: Extensions and Control Rules
TALplanner is a forward-chaining planner that relies on domain knowledge in
the shape of temporal logic formulas in order to prune irrelevant parts of the
search space. TALplanner recently participated in the third International
Planning Competition, which had a clear emphasis on increasing the complexity
of the problem domains being used as benchmark tests and the expressivity
required to represent these domains in a planning system. Like many other
planners, TALplanner had support for some but not all aspects of this increase
in expressivity, and a number of changes to the planner were required. After a
short introduction to TALplanner, this article describes some of the changes
that were made before and during the competition. We also describe the process
of introducing suitable domain knowledge for several of the competition
domains
Timed Automata Approach for Motion Planning Using Metric Interval Temporal Logic
In this paper, we consider the robot motion (or task) planning problem under
some given time bounded high level specifications. We use metric interval
temporal logic (MITL), a member of the temporal logic family, to represent the
task specification and then we provide a constructive way to generate a timed
automaton and methods to look for accepting runs on the automaton to find a
feasible motion (or path) sequence for the robot to complete the task.Comment: Full Version for ECC 201
- …