192 research outputs found
A Robust Distributed Model Predictive Control Framework for Consensus of Multi-Agent Systems with Input Constraints and Varying Delays
This paper studies the consensus problem of general linear discrete-time
multi-agent systems (MAS) with input constraints and bounded time-varying
communication delays. We propose a robust distributed model predictive control
(DMPC) consensus protocol that integrates the offline consensus design with
online DMPC optimization to exploit their respective advantages. More
precisely, each agent is equipped with an offline consensus protocol, which is
a priori designed, depending on its immediate neighbors' estimated states.
Further, the estimation errors propagated over time due to inexact neighboring
information are proved bounded under mild technical assumptions, based on which
a robust DMPC strategy is deliberately designed to achieve robust consensus
while satisfying input constraints. Moreover, it is shown that, with the
suitably designed cost function and constraints, the feasibility of the
associated optimization problem can be recursively ensured. We further provide
the consensus convergence result of the constrained MAS in the presence of
bounded varying delays. Finally, two numerical examples are given to verify the
effectiveness of the proposed distributed consensus algorithm
Beyond Reynolds: A Constraint-Driven Approach to Cluster Flocking
In this paper, we present an original set of flocking rules using an
ecologically-inspired paradigm for control of multi-robot systems. We translate
these rules into a constraint-driven optimal control problem where the agents
minimize energy consumption subject to safety and task constraints. We prove
several properties about the feasible space of the optimal control problem and
show that velocity consensus is an optimal solution. We also motivate the
inclusion of slack variables in constraint-driven problems when the global
state is only partially observable by each agent. Finally, we analyze the case
where the communication topology is fixed and connected, and prove that our
proposed flocking rules achieve velocity consensus.Comment: 6 page
Decentralized Multi-Robot Social Navigation in Constrained Environments via Game-Theoretic Control Barrier Functions
We present an approach to ensure safe and deadlock-free navigation for
decentralized multi-robot systems operating in constrained environments,
including doorways and intersections. Although many solutions have been
proposed to ensure safety, preventing deadlocks in a decentralized fashion with
global consensus remains an open problem. We first formalize the objective as a
non-cooperative, non-communicative, partially observable multi-robot navigation
problem in constrained spaces with multiple conflicting agents, which we term
as social mini-games. Our approach to ensuring safety and liveness rests on two
novel insights: (i) deadlock resolution is equivalent to deriving a mixed-Nash
equilibrium solution to a social mini-game and (ii) this mixed-Nash strategy
can be interpreted as an analogue to control barrier functions (CBFs), that can
then be integrated with standard CBFs, inheriting their safety guarantees.
Together, the standard CBF along with the mixed-Nash CBF analogue preserves
both safety and liveness. We evaluate our proposed game-theoretic navigation
algorithm in simulation as well on physical robots using F1/10 robots, a
Clearpath Jackal, as well as a Boston Dynamics Spot in a doorway, corridor
intersection, roundabout, and hallway scenario. We show that (i) our approach
results in safer and more efficient navigation compared to local planners based
on geometrical constraints, optimization, multi-agent reinforcement learning,
and auctions, (ii) our deadlock resolution strategy is the smoothest in terms
of smallest average change in velocity and path deviation, and most efficient
in terms of makespan (iii) our approach yields a flow rate of 2.8 - 3.3
(ms)^{-1 which is comparable to flow rate in human navigation at 4 (ms)^{-1}.Comment: arXiv admin note: text overlap with arXiv:2306.0881
A Survey on Passing-through Control of Multi-Robot Systems in Cluttered Environments
This survey presents a comprehensive review of various methods and algorithms
related to passing-through control of multi-robot systems in cluttered
environments. Numerous studies have investigated this area, and we identify
several avenues for enhancing existing methods. This survey describes some
models of robots and commonly considered control objectives, followed by an
in-depth analysis of four types of algorithms that can be employed for
passing-through control: leader-follower formation control, multi-robot
trajectory planning, control-based methods, and virtual tube planning and
control. Furthermore, we conduct a comparative analysis of these techniques and
provide some subjective and general evaluations.Comment: 18 pages, 19 figure
Engineering Emergence: A Survey on Control in the World of Complex Networks
Complex networks make an enticing research topic that has been increasingly attracting researchers from control systems and various other domains over the last two decades. The aim of this paper was to survey the interest in control related to complex networks research over time since 2000 and to identify recent trends that may generate new research directions. The survey was performed for Web of Science, Scopus, and IEEEXplore publications related to complex networks. Based on our findings, we raised several questions and highlighted ongoing interests in the control of complex networks.publishedVersio
Resilient Delayed Impulsive Control for Consensus of Multiagent Networks Subject to Malicious Agents
Impulsive control is widely applied to achieve the consensus of multiagent networks (MANs). It is noticed that malicious agents may have adverse effects on the global behaviors, which, however, are not taken into account in the literature. In this study, a novel delayed impulsive control strategy based on sampled data is proposed to achieve the resilient consensus of MANs subject to malicious agents. It is worth pointing out that the proposed control strategy does not require any information on the number of malicious agents, which is usually required in the existing works on resilient consensus. Under appropriate control gains and sampling period, a necessary and sufficient graphic condition is derived to achieve the resilient consensus of the considered MAN. Finally, the effectiveness of the resilient delayed impulsive control is well demonstrated via simulation studies
Formation Flight in Dense Environments
Formation flight has a vast potential for aerial robot swarms in various
applications. However, existing methods lack the capability to achieve fully
autonomous large-scale formation flight in dense environments. To bridge the
gap, we present a complete formation flight system that effectively integrates
real-world constraints into aerial formation navigation. This paper proposes a
differentiable graph-based metric to quantify the overall similarity error
between formations. This metric is invariant to rotation, translation, and
scaling, providing more freedom for formation coordination. We design a
distributed trajectory optimization framework that considers formation
similarity, obstacle avoidance, and dynamic feasibility. The optimization is
decoupled to make large-scale formation flights computationally feasible. To
improve the elasticity of formation navigation in highly constrained scenes, we
present a swarm reorganization method which adaptively adjusts the formation
parameters and task assignments by generating local navigation goals. A novel
swarm agreement strategy called global-remap-local-replan and a formation-level
path planner is proposed in this work to coordinate the swarm global planning
and local trajectory optimizations efficiently. To validate the proposed
method, we design comprehensive benchmarks and simulations with other
cutting-edge works in terms of adaptability, predictability, elasticity,
resilience, and efficiency. Finally, integrated with palm-sized swarm platforms
with onboard computers and sensors, the proposed method demonstrates its
efficiency and robustness by achieving the largest scale formation flight in
dense outdoor environments.Comment: Submitted for IEEE Transactions on Robotic
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