223 research outputs found
Nonsmooth Control Barrier Function design of continuous constraints for network connectivity maintenance
This paper considers the problem of maintaining global connectivity of a
multi-robot system while executing a desired coordination task. Our approach
builds on optimization-based feedback design formulations, where the nominal
cost function and constraints encode desirable control objectives for the
resulting input. We take advantage of the flexibility provided by control
barrier functions to produce additional constraints that guarantee that the
resulting optimization-based controller is continuous and maintains network
connectivity. Our solution uses the algebraic connectivity of the multi-robot
interconnection topology as a control barrier function and critically embraces
its nonsmooth nature. The technical treatment combines elements from set-valued
theory, nonsmooth analysis, and algebraic graph theory to imbue the proposed
constraints with regularity properties so that they can be smoothly combined
with other control constraints. We provide simulations and experimental results
illustrating the effectiveness and continuity of the proposed approach in a
resource gathering problem.Comment: submitted to Automatic
Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions
A multi-agent partially observable Markov decision process (MPOMDP) is a
modeling paradigm used for high-level planning of heterogeneous autonomous
agents subject to uncertainty and partial observation. Despite their modeling
efficiency, MPOMDPs have not received significant attention in safety-critical
settings. In this paper, we use barrier functions to design policies for
MPOMDPs that ensure safety. Notably, our method does not rely on discretization
of the belief space, or finite memory. To this end, we formulate sufficient and
necessary conditions for the safety of a given set based on discrete-time
barrier functions (DTBFs) and we demonstrate that our formulation also allows
for Boolean compositions of DTBFs for representing more complicated safe sets.
We show that the proposed method can be implemented online by a sequence of
one-step greedy algorithms as a standalone safe controller or as a
safety-filter given a nominal planning policy. We illustrate the efficiency of
the proposed methodology based on DTBFs using a high-fidelity simulation of
heterogeneous robots.Comment: 8 pages and 4 figure
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
Distributed Coverage Hole Prevention for Visual Environmental Monitoring with Quadcopters via Nonsmooth Control Barrier Functions
This paper proposes a distributed coverage control strategy for quadcopters
equipped with downward-facing cameras that prevents the appearance of
unmonitored areas in between the quadcopters' fields of view (FOVs). We derive
a necessary and sufficient condition for eliminating any unsurveilled area that
may arise in between the FOVs among a trio of quadcopters by utilizing a power
diagram, i.e. a weighted Voronoi diagram defined by radii of FOVs. Because this
condition can be described as logically combined constraints, we leverage
nonsmooth control barrier functions (NCBFs) to prevent the appearance of
unmonitored areas among a team's FOV. We then investigate the symmetric
properties of the proposed NCBFs to develop a distributed algorithm. The
proposed algorithm can support the switching of the NCBFs caused by changes of
the quadcopters composing trios. The existence of the control input satisfying
NCBF conditions is analyzed by employing the characteristics of the power
diagram. The proposed framework is synthesized with a coverage control law that
maximizes the monitoring quality while reducing overlaps of FOVs. The proposed
method is demonstrated in simulation and experiment.Comment: 17 pages, 18 figures, submitted to the IEEE Transactions on Robotic
Composing Control Barrier Functions for Complex Safety Specifications
The increasing complexity of control systems necessitates control laws that
guarantee safety w.r.t. complex combinations of constraints. In this letter, we
propose a framework to describe compositional safety specifications with
control barrier functions (CBFs). The specifications are formulated as Boolean
compositions of state constraints, and we propose an algorithmic way to create
a single continuously differentiable CBF that captures these constraints and
enables safety-critical control. We describe the properties of the proposed
CBF, and we demonstrate its efficacy by numerical simulations.Comment: Submitted to the IEEE Control System Letters (L-CSS) and the 2024
American Control Conference (ACC). 6 pages, 3 figure
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