We present a novel distributed Kalman-like observer for cooperative state estimation in multi-agent systems. Our approach builds on a class of existing Kalmanlike observers that replace the process covariance matrix with a forgetting factor. We show that this replacement enables the propagation of the information matrix dynamics in a fully distributed manner, while preserving key stability properties. We compute the observers correction term by solving a linear equation dynamically in a distributed manner, circumventing the need for direct centralized matrix inversion. Unlike existing methods that partially discard cross-information to allow distributed computations, our approach preserves inter-agent coupling. Rigorous stability guarantees are provided, and numerical simulations in a cooperative localization scenario demonstrate the effectiveness of the approach in estimating agent states.

We address control synthesis of stochastic discrete-time linear multi-agent systems under jointly chance-constrained collaborative signal temporal logic specifications in a distribution-free manner using available disturbance samples, which are partitioned into training and calibration sets. Leveraging linearity, we decompose each agent’s system into deterministic nominal and stochastic error parts, and design disturbance feedback controllers to bound the stochastic errors by solving a tractable optimization problem over the training data. We then quantify prediction regions (PRs) for the aggregate error trajectories corresponding to agent \textit{cliques}, involved in collaborative tasks, using conformal prediction and calibration data. This enables us to address the specified joint chance constraint via Lipschitz tightening and the computed PRs, and relax the centralized stochastic optimal control problem to a deterministic one, whose solution represents feedforward inputs. To enhance scalability, we decompose the deterministic problem into agent-level subproblems solved in an MPC fashion, yielding a distributed control policy. Finally, we present an illustrative example and a comparison with [1].

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottomup approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.

Modern wireless systems can accurately estimate network latencies; this information could help design better controllers for systems prone to large delays. In this paper, we consider the problem of event-triggered control of linear networked systems with known input delays and address it via hybrid system approach. Specifically, we design a predictor-based state observer that incorporates input delay and propagated outputs to estimate the future state of the system. Subsequently, this estimated state is utilized to generate control inputs for transmissions over the network. In order to reduce the number of transmissions, we design a dynamic event-triggering mechanism (ETM) which makes decisions on whether or not to transmit the control inputs at pre-defined instants. The ETM, by its design, is devoid of Zeno behavior.

We propose a k-hop Distributed Prescribed Performance Observer (k-hop DPPO) for state estimation in multi-agent systems. The observer allows each agent to estimate the state of those agents that are 2-hop or more distant by communicating only with 1-hop neighbors, while guaranteeing that transient estimation errors satisfy prescribed performance defined a priori. Furthermore, we demonstrate that if the controller with perfect state knowledge drives the system towards the goal and the estimation based closed-loop system is set-Input to State Stable (set-ISS) with respect to the set describing the goal, then the state estimates can be adapted to achieve the teams objective. Simulation results are provided to demonstrate the effectiveness of the proposed results.

Motivated by applications in which agents need to perform collaborative tasks under limited communication, in this work we consider the asymptotic satisfaction of relative-position based spatial tasks by a leader-follower network. As opposed to existing approaches, the spatial tasks may involve non-communicating agents and/or a subset of the multi-agent team. In order to ensure the satisfaction of the constraints using local information, as a first contribution, we express the incidence matrix of the task graph in terms of the corresponding matrix of the communication graph. In addition, for undirected spanning tree communication graphs, we show that the relation of the incidence matrices of these graphs is unique. Building upon this relation, as a second contribution we propose a two-hop communication based distributed feedback control law that ensures asymptotic satisfaction of the constraints  with a predetermined robustness. The proposed control law employs the line graph of the communication graph and does not require knowledge of a complete row of the constraint matrix.

In this paper, we present a robust distributed model predictive control (DMPC) scheme for dynamically decoupled nonlinear systems which are subject to state constraints, coupled state constraints and input constraints. In the proposed control scheme, all subsystems solve their local optimization problem in parallel and neighbor-to-neighbor communication suffices. The approach relies on consistency constraints which define a neighborhood around each subsystem's reference trajectory where the state of the subsystem is guaranteed to stay in. Contrary to related approaches, the reference trajectories are improved consecutively. In order to ensure the controller's robustness against bounded uncertainties, we employ tubes. The presented approach can be considered as a time-efficient alternative to the well-established sequential DMPC. In the end, we briefly comment on an iterative extension. The effectiveness of the proposed DMPC scheme is demonstrated with simulations, and its performance is compared to other DMPC schemes.

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.

This paper studies the problem of 3-D relative-measurement-based leader–follower simultaneous distributed localization and formation tracking control. The position information is only available to the leaders, and the followers have inter-agent relative measurements and communication with their neighbors. The key contribution is the development of a weight-matrix-based position constraint, which can make use of relative measurements such as bearing, ratio-of-distance, angle, distance, relative position and their mixture to describe the position relationship among each follower and its neighbors in 3-D space. A bearing-based distributed protocol is proposed for each follower to estimate its position and track its target position, which can drive the followers from their unlocalizable positions to localizable positions. The proposed algorithm is then extended to the case that both bearing and ratio-of-distance measurements are available, where the followers are localizable at all times if the followers and their neighbors are not collocated. In addition, the proposed method is also applicable to homogeneous or heterogeneous angle, distance, and relative position measurements as the ratio-of-distances or bearings can be obtained indirectly by these relative measurements. A remarkable advantage is that the proposed method can be implemented without persistently exciting motions. Some illustrative simulations are presented to verify the theoretical results.

This paper addresses the synchronization problemin open multi-agent systems containing both cooperative andantagonistic interactions. In these systems, new agents can join and new interactions can be formed over time. Moreover, the types of interactions, cooperative or antagonistic, may change. To model these structural changes, we represent the system as a switched system interconnected over a dynamic signed graph. Using the signed edge-based agreement protocol and constructing strict Lyapunov functions for signed edge-Laplacian matrices with multiple zero eigenvalues, we establish global asymptotic stability of the synchronization errors. Numerical simulations validate our theoretical results.