15 research outputs found
Optimal multi-robot path planning with temporal logic constraints
In this paper we present a method for automatically planning optimal paths for a group of robots that satisfy a common high level mission specification. Each robot's motion in the environment is modeled as a weighted transition system. The mission is given as a Linear Temporal Logic formula. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize the maximum time between satisfying instances of the optimizing proposition. Our method is guaranteed to compute an optimal set of robot paths. We utilize a timed automaton representation in order to capture the relative position of the robots in the environment. We then obtain a bisimulation of this timed automaton as a finite transition system that captures the joint behavior of the robots and apply our earlier algorithm for the single robot case to optimize the group motion. We present a simulation of a persistent monitoring task in a road network environment.United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-09-1051)United States. Army Research Office (W911NF-09-1-0088)United States. Air Force Office of Scientific Research (YIP FA9550-09-1-020)National Science Foundation (U.S.). (CNS- 0834260
Masquerade attack detection through observation planning for multi-robot systems
The increasing adoption of autonomous mobile robots comes with
a rising concern over the security of these systems. In this work, we
examine the dangers that an adversary could pose in a multi-agent
robot system. We show that conventional multi-agent plans are
vulnerable to strong attackers masquerading as a properly functioning
agent. We propose a novel technique to incorporate attack
detection into the multi-agent path-finding problem through the
simultaneous synthesis of observation plans. We show that by
specially crafting the multi-agent plan, the induced inter-agent
observations can provide introspective monitoring guarantees; we
achieve guarantees that any adversarial agent that plans to break
the system-wide security specification must necessarily violate the
induced observation plan.Accepted manuscrip
Resilience of multi-robot systems to physical masquerade attacks
The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip
Optimality and robustness in multi-robot path planning with temporal logic constraints
In this paper we present a method for automatically generating optimal robot paths satisfying high-level mission specifications. The motion of the robot in the environment is modeled as a weighted transition system. The mission is specified by an arbitrary linear temporal-logic (LTL) formula over propositions satisfied at the regions of a partitioned environment. The mission specification contains an optimizing proposition, which must be repeatedly satisfied. The cost function that we seek to minimize is the maximum time between satisfying instances of the optimizing proposition. For every environment model, and for every formula, our method computes a robot path that minimizes the cost function. The problem is motivated by applications in robotic monitoring and data-gathering. In this setting, the optimizing proposition is satisfied at all locations where data can be uploaded, and the LTL formula specifies a complex data-collection mission. Our method utilizes BĂĽchi automata to produce an automaton (which can be thought of as a graph) whose runs satisfy the temporal-logic specification. We then present a graph algorithm that computes a run corresponding to the optimal robot path. We present an implementation for a robot performing data collection in a road-network platform.This work was supported in part by the Office of Naval Research (grant number MURI N00014-09-1051), Army Research Office (grant number W911NF-09-1-0088), Air Force Office of Scientific Research (grant number YIP FA9550-09-1-020), National Science Foundation (grant number CNS-0834260), Singapore-MIT Alliance for Research and Technology (SMART) Future of Urban Mobility Project and by Natural Sciences and Engineering Research Council of Canada. (MURI N00014-09-1051 - Office of Naval Research; W911NF-09-1-0088 - Army Research Office; YIP FA9550-09-1-020 - Air Force Office of Scientific Research; CNS-0834260 - National Science Foundation; Singapore-MIT Alliance for Research and Technology (SMART); Natural Sciences and Engineering Research Council of Canada
Measuring Global Similarity between Texts
We propose a new similarity measure between texts which, contrary to the
current state-of-the-art approaches, takes a global view of the texts to be
compared. We have implemented a tool to compute our textual distance and
conducted experiments on several corpuses of texts. The experiments show that
our methods can reliably identify different global types of texts.Comment: Submitted to SLSP 201
Cooperative Task Planning of Multi-Agent Systems Under Timed Temporal Specifications
In this paper the problem of cooperative task planning of multi-agent systems
when timed constraints are imposed to the system is investigated. We consider
timed constraints given by Metric Interval Temporal Logic (MITL). We propose a
method for automatic control synthesis in a two-stage systematic procedure.
With this method we guarantee that all the agents satisfy their own individual
task specifications as well as that the team satisfies a team global task
specification.Comment: Submitted to American Control Conference 201
Specification Patterns for Robotic Missions
Mobile and general-purpose robots increasingly support our everyday life,
requiring dependable robotics control software. Creating such software mainly
amounts to implementing their complex behaviors known as missions. Recognizing
the need, a large number of domain-specific specification languages has been
proposed. These, in addition to traditional logical languages, allow the use of
formally specified missions for synthesis, verification, simulation, or guiding
the implementation. For instance, the logical language LTL is commonly used by
experts to specify missions, as an input for planners, which synthesize the
behavior a robot should have. Unfortunately, domain-specific languages are
usually tied to specific robot models, while logical languages such as LTL are
difficult to use by non-experts. We present a catalog of 22 mission
specification patterns for mobile robots, together with tooling for
instantiating, composing, and compiling the patterns to create mission
specifications. The patterns provide solutions for recurrent specification
problems, each of which detailing the usage intent, known uses, relationships
to other patterns, and---most importantly---a template mission specification in
temporal logic. Our tooling produces specifications expressed in the LTL and
CTL temporal logics to be used by planners, simulators, or model checkers. The
patterns originate from 245 realistic textual mission requirements extracted
from the robotics literature, and they are evaluated upon a total of 441
real-world mission requirements and 1251 mission specifications. Five of these
reflect scenarios we defined with two well-known industrial partners developing
human-size robots. We validated our patterns' correctness with simulators and
two real robots
Control Strategies for COVID-19 Epidemic with Vaccination, Shield Immunity and Quarantine: A Metric Temporal Logic Approach
Ever since the outbreak of the COVID-19 epidemic, various public health
control strategies have been proposed and tested against the coronavirus
SARS-CoV-2. We study three specific COVID-19 epidemic control models: the
susceptible, exposed, infectious, recovered (SEIR) model with vaccination
control; the SEIR model with shield immunity control; and the susceptible,
un-quarantined infected, quarantined infected, confirmed infected (SUQC) model
with quarantine control. We express the control requirement in metric temporal
logic (MTL) formulas (a type of formal specification languages) which can
specify the expected control outcomes such as "the deaths from the infection
should never exceed one thousand per day within the next three months" or "the
population immune from the disease should eventually exceed 200 thousand within
the next 100 to 120 days". We then develop methods for synthesizing control
strategies with MTL specifications. To the best of our knowledge, this is the
first paper to systematically synthesize control strategies based on the
COVID-19 epidemic models with formal specifications. We provide simulation
results in three different case studies: vaccination control for the COVID-19
epidemic with model parameters estimated from data in Lombardy, Italy; shield
immunity control for the COVID-19 epidemic with model parameters estimated from
data in Lombardy, Italy; and quarantine control for the COVID-19 epidemic with
model parameters estimated from data in Wuhan, China. The results show that the
proposed synthesis approach can generate control inputs such that the
time-varying numbers of individuals in each category (e.g., infectious, immune)
satisfy the MTL specifications. The results also show that early intervention
is essential in mitigating the spread of COVID-19, and more control effort is
needed for more stringent MTL specifications
Optimal temporal logic control of autonomous vehicles
Thesis (Ph.D.)--Boston UniversityTemporal logics, such as Linear Temporal Logic (LTL) and Computation Tree Logic (CTL), are extensions of propositional logic that can capture temporal relations. Even though temporal logics have been used in model checking of finite systems for quite some time, they have gained popularity as a means for specifying complex mission requirements in path planning and control synthesis problems only recently. This dissertation proposes and evaluates methods and algorithms for optimal path planning and control synthesis for autonomous vehicles where a high-level mission specification expressed in LTL (or a fragment of LTL) must be satisfied. In summary, after obtaining a discrete representation of the overall system, ideas and tools from formal verification and graph theory are leveraged to synthesize provably correct and optimal control strategies.
The first part of this dissertation focuses on automatic planning of optimal paths for a group of robots that must satisfy a common high level mission specification. The effect of slight deviations in traveling times on the behavior of the team is analyzed and methods that are robust to bounded non-determinism in traveling times are proposed. The second part focuses on the case where a controllable agent is required to satisfy a high-level mission specification in the presence of other probabilistic agents that cannot be controlled. Efficient methods to synthesize control policies that maximize the probability of satisfaction of the mission specification are presented. The focus of the third part is the problem where an autonomous vehicle is required to satisfy a rich mission specification over service requests occurring at the regions of a partitioned environment. A receding horizon control strategy that makes use of the local information provided by the sensors on the vehicle in addition to the a priori information about the environment is presented. For all of the automatic planning and control synthesis problems that are considered, the proposed algorithms are implemented, evaluated, and validated through experiments and/or simulations
Mind the gap: Robotic Mission Planning Meets Software Engineering
In the context of robotic software, the selection of an appropriate planner is one of the most crucial software engineering decisions. Robot planners aim at computing plans (i.e., blueprint of actions) to accomplish a complex mission. While many planners have been proposed in the robotics literature, they are usually evaluated on showcase examples, making hard to understand whether they can be effectively (re)used for realising complex missions, with heterogeneous robots, and in real-world scenarios.
In this paper we propose ENFORCE, a framework which allows wrapping FM-based planners into comprehensive software engineering tools, and considers complex robotic missions. ENFORCE relies on (i) realistic maps (e.g, fire escape maps) that describe the environment in which the robots are deployed; (ii) temporal logic for mission specification; and (iii) Uppaal model checker to compute plans that satisfy mission specifications. We evaluated ENFORCE by analyzing how it supports computing plans in real case scenarios, and by evaluating the generated plans in simulated and real environments. The results show that while ENFORCE is adequate for handling single-robot applications, the state explosion still represents a major barrier for reusing existing planners in multi-robot applications