Dimos V. Dimarogonas

Multi-agent planning under Signal Temporal Logic (STL) is often hindered by collaborative tasks that lead to computational challenges due to the inherent high dimensionality of the problem, preventing scalable synthesis with satisfaction guarantees. To address this, we formulate STL planning as an optimization program under multi-agent STL constraints and introduce a penalty-based unconstrained relaxation that can be efficiently solved via a Block-Coordinate Gradient Descent (BCGD) method, where each block corresponds to a single agent’s decision variables, thereby mitigating complexity. By utilizing a quadratic penalty function defined via smooth STL semantics, we show that BCGD iterations converge to a stationary point of the penalized problem under standard regularity assumptions. To enforce feasibility, the BCGD solver is embedded within a two-layer optimization scheme: inner BCGD updates are performed for a fixed penalty parameter, which is then increased in an outer loop to progressively improve multi-agent STL robustness. The proposed framework enables scalable computations and is validated through various complex multi-robot planning scenarios.

This work presents an event-triggered switching control framework for a class of nonlinear underactuated multichannel systems with input constraints. These systems are inspired by cooperative manipulation tasks involving underactuation, where multiple underactuated agents collaboratively push or pull an object to a target pose. Unlike existing approaches for multichannel systems, our method addresses underactuation and the potential loss of controllability by additionally addressing channel assignment of agents. To simultaneously account for channel assignment, input constraints, and stabilization, we formulate the control problem as a mixed integer linear programming (MILP) and derive sufficient conditions for its feasibility. To improve real-time computation efficiency, we introduce an event-triggered control scheme that maintains stability even between switching events through a quadratic programming (QP)-based stabilizing controller. We theoretically establish the semi-global exponential stability of the proposed method and the asymptotic stability of its extension to nonprehensile cooperative manipulation under noninstantaneous switching. The proposed framework is further validated through numerical simulations on 2-D and 3-D free-flyer systems and multirobot nonprehensile pushing tasks.

Motivated by the growing importance of flexible automation in industrial environments, this article investigates the impact of wireless solutions in collaborative robotics, toward which we provide a quality of control (QoC)-based abstraction and methodology that comprehensively captures the interplay between network-induced delays, reliability, and robotic workload parameters for wireless collaborative robotics (WCR). For such a setting, we formulate a joint control-communication co-design based optimization framework to maximize the QoC across all robots, for 5G resource dimensioning. This is crucial for identifying optimal co-design parameters maximizing the QoC for limited 5G bandwidth across different topologies of robotic connectivity, prior to the deployment of these WCRs, or when selecting appropriate connectivity priority levels. We compare the performance of our proposed algorithm to different state of the art schemes in the literature. Our simulation results highlight the latency-reliability tradeoff and its implications on the control performance. We also demonstrate that our abstraction can be utilized for control-communication co-design, identifying optimal latency-reliability points in conjunction with the maximum velocity the robots operate with, while highlighting the energy gains due to co-design as well.

Rapid advancements in digitalization and artificial intelligence (AI) have catalyzed the adoption of digital twin technologies in the construction sector, enabling real-time synchronization between virtual models and physical systems. Simultaneously, on-site robotic automation has shown promise for reducing physical workloads, enhancing productivity, and contributing to sustainability goals that are key values of Industry 5.0. However, current digital twin implementations rarely incorporate multirobot construction systems, often relying on single-robot approaches or purely offline simulations. This gap hinders the realization of truly integrated construction environments that combine sensing, data analytics, wireless communications, and multirobot coordination. In response, this article proposes ConstrucTwin, a digital twin-driven multirobot construction framework designed to support complex construction tasks in real-world settings. By combining a 5G communication estimation-involved architecture and a cross-level planning strategy, ConstrucTwin streamlines interactions between physical robots and their digital counterparts. Essential tasks such as motion and task-level planning, as well as remote human-in-the-loop (HIL) oversight, are orchestrated within a single unified architecture. Through case studies involving rebar cage and brick wall construction, we demonstrate how an integrated approach to vision-based servoing and multirobot coordination enhances execution speed, precision, and scalability. The results underscore the system’s potential to advance human-centric, resilient, and sustainable construction, thereby aligning with the broader vision of Industry 5.0.

In this work, we develop a new distributed output- feedback stochastic model predictive control (SMPC) proposal for a plant that is cooperatively regulated by a set of actuator nodes. Contrary to most approaches, we consider hard constraints on the actuators, and we appropriately tighten the constraints to ensure recursive feasibility with a given probability, despite the stochastic noise present in the system. To lighten the communication load, the constraint design is performed offline and an event-triggering mechanism is included, so that the nodes only need to transmit their local state estimates to neighbors at event instants during online execution. We prove constraint satisfaction and stability of our proposal, and we include simulation results showing that similar control performance to the centralized case can be achieved by our distributed SMPC with reduced communication.

We consider the problem of cooperative manipulation by a mobile multi-manipulator system operating in obstacle-cluttered and highly constrained environments under spatio-temporal task specifications. The task requires transporting a grasped object while respecting both continuous robot dynamics and discrete geometric constraints arising from obstacles and narrow passages. To address this hybrid structure, we propose a multi-rate planning and control framework that combines offline generation of an STL-satisfying object trajectory and collision-free base footprints with online constrained inverse kinematics and continuous-time feedback control. The resulting closed-loop system enables coordinated reconfiguration of multiple manipulators while tracking the desired object motion. The approach is evaluated in high-fidelity physics simulations using three Franka Emika Panda mobile manipulators rigidly grasping an object.

This work presents an event-triggered switching control framework for a class of nonlinear underactuated multi-channel systems with input constraints. These systems are inspired by cooperative manipulation tasks involving underactuation, where multiple underactuated agents collaboratively push or pull an object to a target pose. Unlike existing approaches for multi-channel systems, our method addresses underactuation and the potential loss of controllability by additionally addressing channel assignment of agents. To simultaneously account for channel assignment, input constraints, and stabilization, we formulate the control problem as a Mixed Integer Linear Programming and derive sufficient conditions for its feasibility. To improve real-time computation efficiency, we introduce an event-triggered control scheme that maintains stability even between switching events through a quadratic programming-based stabilizing controller. We theoretically establish the semi-global exponential stability of the proposed method and the asymptotic stability of its extension to nonprehensile cooperative manipulation under noninstantaneous switching. The proposed framework is further validated through numerical simulations on 2D and 3D free-flyer systems and multi-robot nonprehensile pushing tasks.