Synchronization-Based Cooperative Distributed Model Predictive Control

Distributed control algorithms are known to reduce overall computation time compared to centralized control algorithms. However, they can result in inconsistent solutions leading to the violation of safety-critical constraints. Inconsistent solutions can arise when two or more agents compute concurrently while making predictions on each others control actions. To address this issue, we propose an iterative algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control, which we presented in [1]. The algorithm consists of two steps: 1. computing the optimal control inputs for each agent and 2. synchronizing the predicted states across all agents. We demonstrate the efficacy of our algorithm in the control of multiple small-scale vehicles in our Cyber-Physical Mobility Lab.

A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms

Connected and automated vehicles and robot swarms hold transformative potential for enhancing safety, efficiency, and sustainability in the transportation and manufacturing sectors. Extensive testing and validation of these technologies is crucial for their deployment in the real world. While simulations are essential for initial testing, they often have limitations in capturing the complex dynamics of real-world interactions. This limitation underscores the importance of small-scale testbeds. These testbeds provide a realistic, cost-effective, and controlled environment for testing and validating algorithms, acting as an essential intermediary between simulation and full-scale experiments. This work serves to facilitate researchers' efforts in identifying existing small-scale testbeds suitable for their experiments and provide insights for those who want to build their own. In addition, it delivers a comprehensive survey of the current landscape of these testbeds. We derive 62 characteristics of testbeds based on the well-known sense-plan-act paradigm and offer an online table comparing 22 small-scale testbeds based on these characteristics. The online table is hosted on our designated public webpage www.cpm-remote.de/testbeds, and we invite testbed creators and developers to contribute to it. We closely examine nine testbeds in this paper, demonstrating how the derived characteristics can be used to present testbeds. Furthermore, we discuss three ongoing challenges concerning small-scale testbeds that we identified, i.e., small-scale to full-scale transition, sustainability, and power and resource management.

Cyber-Physical Mobility Lab An Open-Source Platform for Networked and Autonomous Vehicles

We introduce our Cyber-Physical Mobility Lab (CPM Lab), a development environment for networked and autonomous vehicles. It consists of 20 model-scale vehicles for experiments and a simulation environment. We show our four-layered architecture that enables the seamless use of the same software in simulations and in experiments without any adaptions. A Data Distribution Service (DDS) based middleware allows to adapt the number of vehicles during experiments in a seamless manner. Experiments with the 20 vehicles can be extended by unlimited additional simulated vehicles. Another layer is responsible for synchronizing all entities following a logical execution time approach. We pursue an open policy in the CPM Lab and will publish the entire code as well as construction plans online. Additionally, we will offer a remote-access to the CPM Lab using a web interface. The remote-access will be publicly available. The CPM Lab allows researchers as well as students from different disciplines to see their ideas develop into reality.

Networked and Autonomous Model-scale Vehicles for Experiments in Research and Education

This paper presents the $\mathrm{\mu}$Car, a 1:18 model-scale vehicle with Ackermann steering geometry developed for experiments in networked and autonomous driving in research and education. The vehicle is open source, moderately costed and highly flexible, which allows for many applications. It is equipped with an inertial measurement unit and an odometer and obtains its pose via WLAN from an indoor positioning system. The two supported operating modes for controlling the vehicle are (1) computing control inputs on external hardware, transmitting them via WLAN and applying received inputs to the actuators and (2) transmitting a reference trajectory via WLAN, which is then followed by a controller running on the onboard Raspberry Pi Zero W. The design allows identical vehicles to be used at the same time in order to conduct experiments with a large amount of networked agents.