17 research outputs found
Barrier Functions for Multiagent-POMDPs with DTL Specifications
Multi-agent partially observable Markov decision processes (MPOMDPs) provide a framework to represent heterogeneous autonomous agents subject to uncertainty and partial observation. In this paper, given a nominal policy provided by a human operator or a conventional planning method, we propose a technique based on barrier functions to design a minimally interfering safety-shield ensuring satisfaction of high-level specifications in terms of linear distribution temporal logic (LDTL). To this end, we use sufficient and necessary conditions for the invariance of a given set based on discrete-time barrier functions (DTBFs) and formulate sufficient conditions for finite time DTBF to study finite time convergence to a set. We then show that different LDTL mission/safety specifications can be cast as a set of invariance or finite time reachability problems. We demonstrate that the proposed method for safety-shield synthesis can be implemented online by a sequence of one-step greedy algorithms. We demonstrate the efficacy of the proposed method using experiments involving a team of robots
Barrier Functions for Multiagent-POMDPs with DTL Specifications
Multi-agent partially observable Markov decision processes (MPOMDPs) provide a framework to represent heterogeneous autonomous agents subject to uncertainty and partial observation. In this paper, given a nominal policy provided by a human operator or a conventional planning method, we propose a technique based on barrier functions to design a minimally interfering safety-shield ensuring satisfaction of high-level specifications in terms of linear distribution temporal logic (LDTL). To this end, we use sufficient and necessary conditions for the invariance of a given set based on discrete-time barrier functions (DTBFs) and formulate sufficient conditions for finite time DTBF to study finite time convergence to a set. We then show that different LDTL mission/safety specifications can be cast as a set of invariance or finite time reachability problems. We demonstrate that the proposed method for safety-shield synthesis can be implemented online by a sequence of one-step greedy algorithms. We demonstrate the efficacy of the proposed method using experiments involving a team of robots
Energy-Constrained Active Exploration Under Incremental-Resolution Symbolic Perception
In this work, we consider the problem of autonomous exploration in search of
targets while respecting a fixed energy budget. The robot is equipped with an
incremental-resolution symbolic perception module wherein the perception of
targets in the environment improves as the robot's distance from targets
decreases. We assume no prior information about the total number of targets,
their locations as well as their possible distribution within the environment.
This work proposes a novel decision-making framework for the resulting
constrained sequential decision-making problem by first converting it into a
reward maximization problem on a product graph computed offline. It is then
solved online as a Mixed-Integer Linear Program (MILP) where the knowledge
about the environment is updated at each step, combining automata-based and
MILP-based techniques. We demonstrate the efficacy of our approach with the
help of a case study and present empirical evaluation in terms of expected
regret. Furthermore, the runtime performance shows that online planning can be
efficiently performed for moderately-sized grid environments
Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes
Autonomous systems are often required to operate in partially observable
environments. They must reliably execute a specified objective even with
incomplete information about the state of the environment. We propose a
methodology to synthesize policies that satisfy a linear temporal logic formula
in a partially observable Markov decision process (POMDP). By formulating a
planning problem, we show how to use point-based value iteration methods to
efficiently approximate the maximum probability of satisfying a desired logical
formula and compute the associated belief state policy. We demonstrate that our
method scales to large POMDP domains and provides strong bounds on the
performance of the resulting policy.Comment: 8 pages, 3 figures, AAAI 202
Unified Multi-Rate Control: from Low Level Actuation to High Level Planning
In this paper we present a hierarchical multi-rate control architecture for
nonlinear autonomous systems operating in partially observable environments.
Control objectives are expressed using syntactically co-safe Linear Temporal
Logic (LTL) specifications and the nonlinear system is subject to state and
input constraints. At the highest level of abstraction, we model the
system-environment interaction using a discrete Mixed Observable Markov
Decision Problem (MOMDP), where the environment states are partially observed.
The high level control policy is used to update the constraint sets and cost
function of a Model Predictive Controller (MPC) which plans a reference
trajectory. Afterwards, the MPC planned trajectory is fed to a low-level
high-frequency tracking controller, which leverages Control Barrier Functions
(CBFs) to guarantee bounded tracking errors. Our strategy is based on model
abstractions of increasing complexity and layers running at different
frequencies. We show that the proposed hierarchical multi-rate control
architecture maximizes the probability of satisfying the high-level
specifications while guaranteeing state and input constraint satisfaction.
Finally, we tested the proposed strategy in simulations and experiments on
examples inspired by the Mars exploration mission, where only partial
environment observations are available