We design a distributed feedback optimization strategy, embedded into a modular ROS 2 control architecture, which allows a team of heterogeneous robots to cooperatively monitor and encircle a target while patrolling points of interest. Relying on the aggregative feedback optimization framework, we handle multi-robot dynamics while minimizing a global performance index depending on both microscopic (e.g., the location of single robots) and macroscopic variables (e.g., the spatial distribution of the team). The proposed distributed policy allows the robots to cooperatively address the global problem by employing only local measurements and neighboring data exchanges. These exchanges are performed through an asynchronous communication protocol ruled by locally-verifiable triggering conditions. We formally prove that our strategy steers the robots to a set of configurations representing stationary points of the considered optimization problem. The effectiveness and scalability of the overall strategy are tested via Monte Carlo campaigns of realistic Webots ROS 2 virtual experiments. Finally, the applicability of our solution is shown with real experiments on ground and aerial robots.

ABSTRACT Traffic simulators are used to generate data for learning in intelligent transportation systems (ITSs). A key question is to what extent their modelling assumptions affect the capabilities of ITSs to adapt to various scenarios when deployed in the real world. This work focuses on two simulators commonly used to train reinforcement learning (RL) agents for traffic applications, CityFlow and SUMO. A controlled virtual experiment varying driver behavior and simulation scale finds evidence against distributional equivalence in RL-relevant measures from these simulators, with the root mean squared error and KL divergence being significantly greater than 0 for all assessed measures. While granular real-world validation generally remains infeasible, these findings suggest that traffic simulators are not a deus ex machina for RL training: understanding the impacts of inter-simulator differences is necessary to train and deploy RL-based ITSs. 1 INTRODUCTION Transportation efficiency is becoming an increasingly critical challenge due to continual growth in the volume of people and objects that need to be transported. The 2021 Urban Mobility Report (Schrank et al. 2021) projected that, while the COVID-19 pandemic alleviated congestion, traffic levels in the US will quickly rebound in areas with expanding populations and job markets to produce the most rapid congestion growth since 1982. The increased traffic will stress existing infrastructure and result in social, economic, and environmental costs (Schrank et al. 2021), thus making the development and deployment of intelligent transportation systems (ITSs) a critical priority. At the same time, advances in computational algorithms and roadway infrastructure made in response to these challenges provide opportunities to enhance ITS learning. For example, novel traffic signal control technologies based on reinforcement learning (RL), which learn adaptive signaling policies from simulations generated using real-world traffic data, have already achieved performance on par with and even exceeding traditional control methods (Chen et al. 2020). However, collecting data for ITS learning remains a nontrivial task.