本文介绍了我们的网络物理移动实验室（CPM实验室）。它是网络和自治车辆的开源开发环境，专注于网络决策，轨迹规划和控制。 CPM实验室主持20个物理模型规模车辆（{\ mu}汽车），我们可以通过无限制的模拟车辆无缝扩展。代码和施工计划是公开的，以实现重建CPM实验室。我们的四层架构使得能够在模拟中和实验中无缝使用相同的软件，而无需进一步的适应。基于数据分发服务（DDS）的中间件允许以无缝方式在实验期间调整车辆数量。中间件还负责在逻辑执行时间方法后同步所有实体，以实现实验的确定性和再现性。这种方法使CPM实验室成为网络决策算法快速功能原型的独特平台。 CPM实验室允许研究人员以及来自不同学科的学生，以了解他们发展成为现实的想法。我们使用两个示例实验展示其能力。我们正在通过WebInterface进行远程访问CPM实验室。

Reliable and efficient validation technologies are critical for the recent development of multi-vehicle cooperation and vehicle-road-cloud integration. In this paper, we introduce our miniature experimental platform, Mixed Cloud Control Testbed (MCCT), developed based on a new notion of Mixed Digital Twin (mixedDT). Combining Mixed Reality with Digital Twin, mixedDT integrates the virtual and physical spaces into a mixed one, where physical entities coexist and interact with virtual entities via their digital counterparts. Under the framework of mixedDT, MCCT contains three major experimental platforms in the physical, virtual and mixed spaces respectively, and provides a unified access for various human-machine interfaces and external devices such as driving simulators. A cloud unit, where the mixed experimental platform is deployed, is responsible for fusing multi-platform information and assigning control instructions, contributing to synchronous operation and real-time cross-platform interaction. Particularly, MCCT allows for multi-vehicle coordination composed of different multi-source vehicles (\eg, physical vehicles, virtual vehicles and human-driven vehicles). Validations on vehicle platooning demonstrate the flexibility and scalability of MCCT.

从教育和研究的角度来看，关于硬件的实验是机器人技术和控制的关键方面。在过去的十年中，已经介绍了许多用于车轮机器人的开源硬件和软件框架，主要采用独轮车和类似汽车的机器人的形式，目的是使更广泛的受众访问机器人并支持控制系统开发。独轮车通常很小且便宜，因此有助于在较大的机队中进行实验，但它们不适合高速运动。类似汽车的机器人更敏捷，但通常更大且更昂贵，因此需要更多的空间和金钱资源。为了弥合这一差距，我们介绍了Chronos，这是一种具有定制开源电子设备的新型汽车的1/28比例机器人，以及CRS是用于控制和机器人技术的开源软件框架。 CRS软件框架包括实施各种最新的算法，以进行控制，估计和多机构协调。通过这项工作，我们旨在更轻松地使用硬件，并减少启动新的教育和研究项目所需的工程时间。

在不久的将来，自动驾驶的开发将变得更加复杂，因为这些车辆不仅会依靠自己的传感器，而且还与其他车辆和基础设施进行交流以合作和改善驾驶体验。为此，需要进行一些研究领域，例如机器人技术，沟通和控制，以实施未来的方法。但是，每个领域首先关注其组件的开发，而组件可能对整个系统产生的影响仅在后期考虑。在这项工作中，我们集成了机器人技术，通信和控制的仿真工具，即ROS2，Omnet ++和MATLAB来评估合作驾驶场景。可以利用该框架使用指定工具来开发各个组件，而最终评估可以在完整的情况下进行，从而可以模拟高级多机器人应用程序以进行合作驾驶。此外，它可以用于集成其他工具，因为集成以模块化方式完成。我们通过在合作自适应巡航控制（CACC）和ETSI ITS-G5通信体系结构下展示排量场景来展示该框架。此外，我们比较了理论分析和实际案例研究之间控制器性能的差异。

在未来几十年中，自动驾驶将普遍存在。闲置在交叉点上提高自动驾驶的安全性，并通过改善交叉点的交通吞吐量来提高效率。在闲置时，路边基础设施通过卸载从车辆到路边基础设施的知觉和计划，在交叉路口远程驾驶自动驾驶汽车。为了实现这一目标，iDriving必须能够以全帧速率以较少100毫秒的尾声处理大量的传感器数据，而无需牺牲准确性。我们描述了算法和优化，使其能够使用准确且轻巧的感知组件实现此目标，该组件是从重叠传感器中得出的复合视图的原因，以及一个共同计划多个车辆的轨迹的计划者。在我们的评估中，闲置始终确保车辆的安全通过，而自动驾驶只能有27％的时间。与其他方法相比，闲置的等待时间还要低5倍，因为它可以实现无流量的交叉点。

我们描述了一个软件框架和用于串联的硬件平台，用于设计和分析模拟和现实中机器人自主算法。该软件是开源的，独立的容器和操作系统（OS）的软件，具有三个主要组件：COS ++车辆仿真框架（Chrono）的ROS 2接口（Chrono），该框架提供了高保真的轮毂/跟踪的车辆和传感器仿真；基于ROS 2的基本基于算法设计和测试的自治堆栈；以及一个开发生态系统，可在感知，状态估计，路径计划和控制中进行可视化和硬件实验。随附的硬件平台是1/6刻度的车辆，并具有可重新配置的用于计算，传感和跟踪的可重新配置的安装。其目的是允许对算法和传感器配置进行物理测试和改进。由于该车辆平台在模拟环境中具有数字双胞胎，因此可以测试和比较模拟和现实中相同的算法和自主堆栈。该平台的构建是为了表征和管理模拟到现实差距。在此，我们描述了如何建立，部署和用于改善移动应用程序的自主权。

尽管机器人学课程在高等教育方面已建立，但这些课程通常专注于理论，有时缺乏对开发，部署和将软件应用于真实硬件的技术的系统覆盖。此外，大多数用于机器人教学的硬件平台是针对中学水平的年轻学生的低级玩具。为了解决这一差距，开发了一个自动驾驶汽车硬件平台，称为第1 f1 f1tth，用于教授自动驾驶系统。本文介绍了以“赛车”和替换考试的竞赛为主题的各种教育水平教学模块和软件堆栈。第1辆车提供了一个模块化硬件平台及其相关软件，用于教授自动驾驶算法的基础知识。从基本的反应方法到高级计划算法，教学模块通过使用第1辆车的自动驾驶来增强学生的计算思维。第1辆汽车填补了研究平台和低端玩具车之间的空白，并提供了学习自主系统中主题的动手经验。多年的四所大学为他们的学期本科和研究生课程采用了教学模块。学生反馈用于分析第1个平台的有效性。超过80％的学生强烈同意，硬件平台和模块大大激发了他们的学习，而超过70％的学生强烈同意，硬件增强了他们对学科的理解。调查结果表明，超过80％的学生强烈同意竞争激励他们参加课程。

两栖地面汽车将飞行和驾驶模式融合在一起，以实现更灵活的空中行动能力，并且最近受到了越来越多的关注。通过分析现有的两栖车辆，我们强调了在复杂的三维城市运输系统中有效使用两栖车辆的自动驾驶功能。我们审查并总结了现有两栖车辆设计中智能飞行驾驶的关键促成技术，确定主要的技术障碍，并提出潜在的解决方案，以实现未来的研究和创新。本文旨在作为研究和开发智能两栖车辆的指南，以实现未来的城市运输。

Prototyping and validating hardware-software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt towards developing such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things.

在过去的十年中，自动驾驶航空运输车辆引起了重大兴趣。这是通过空中操纵器和新颖的握手的技术进步来实现这一目标的。此外，改进的控制方案和车辆动力学能够更好地对有效载荷进行建模和改进的感知算法，以检测无人机（UAV）环境中的关键特征。在这项调查中，对自动空中递送车辆的技术进步和开放研究问题进行了系统的审查。首先，详细讨论了各种类型的操纵器和握手，以及动态建模和控制方法。然后，讨论了降落在静态和动态平台上的。随后，诸如天气状况，州估计和避免碰撞之类的风险以确保安全过境。最后，调查了交付的UAV路由，该路由将主题分为两个领域：无人机操作和无人机合作操作。

We designed and constructed an A-sized base autonomous underwater vehicle (AUV), augmented with a stack of modular and extendable hardware and software, including autonomy, navigation, control and high fidelity simulation capabilities (A-size stands for the standard sonobuoy form factor, with a maximum diameter of 124 mm). Subsequently, we extended this base vehicle with a novel tuna-inspired morphing fin payload module (referred to as the Morpheus AUV), to achieve good directional stability and exceptional maneuverability; properties that are highly desirable for rigid hull AUVs, but are presently difficult to achieve because they impose contradictory requirements. The morphing fin payload allows the base AUV to dynamically change its stability-maneuverability qualities by using morphing fins, which can be deployed, deflected and retracted, as needed. The base vehicle and Morpheus AUV were both extensively field tested in-water in the Charles river, Massachusetts, USA; by conducting hundreds of hours of operations over a period of two years. The maneuvering capability of the Morpheus AUV was evaluated with and without the use of morphing fins to quantify the performance improvement. The Morpheus AUV was able to showcase an exceptional turning rate of around 25-35 deg/s. A maximum turn rate improvement of around 35% - 50% was gained through the use of morphing fins.

Connected Autonomous Vehicles (CAVs) are key components of the Intelligent Transportation System (ITS), and all-terrain Autonomous Ground Vehicles (AGVs) are indispensable tools for a wide range of applications such as disaster response, automated mining, agriculture, military operations, search and rescue missions, and planetary exploration. Experimental validation is a requisite for CAV and AGV research, but requires a large, safe experimental environment when using full-size vehicles which is time-consuming and expensive. To address these challenges, we developed XTENTH-CAR (eXperimental one-TENTH scaled vehicle platform for Connected autonomy and All-terrain Research), an open-source, cost-effective proportionally one-tenth scaled experimental vehicle platform governed by the same physics as a full-size on-road vehicle. XTENTH-CAR is equipped with the best-in-class NVIDIA Jetson AGX Orin System on Module (SOM), stereo camera, 2D LiDAR and open-source Electronic Speed Controller (ESC) with drivers written in the new Robot Operating System (ROS 2) to facilitate experimental CAV and AGV perception, motion planning and control research, that incorporate state-of-the-art computationally expensive algorithms such as Deep Reinforcement Learning (DRL). XTENTH-CAR is designed for compact experimental environments, and aims to increase the accessibility of experimental CAV and AGV research with low upfront costs, and complete Autonomous Vehicle (AV) hardware and software architectures similar to the full-sized X-CAR experimental vehicle platform, enabling efficient cross-platform development between small-scale and full-scale vehicles.

Experiments using large numbers of miniature swarm robots are desirable to teach, study, and test multi-robot and swarm intelligence algorithms and their applications. To realize the full potential of a swarm robot, it should be capable of not only motion but also sensing, computing, communication, and power management modules with multiple options. Current swarm robot platforms developed for commercial and academic research purposes lack several of these critical attributes by focusing only on a few of these aspects. Therefore, in this paper, we propose the HeRoSwarm, a fully-capable swarm robot platform with open-source hardware and software support. The proposed robot hardware is a low-cost design with commercial off-the-shelf components that uniquely integrates multiple sensing, communication, and computing modalities with various power management capabilities into a tiny footprint. Moreover, our swarm robot with odometry capability with Robot Operating Systems (ROS) support is unique in its kind. This simple yet powerful swarm robot design has been extensively verified with different prototyping variants and multi-robot experimental demonstrations.

Designing a local planner to control tractor-trailer vehicles in forward and backward maneuvering is a challenging control problem in the research community of autonomous driving systems. Considering a critical situation in the stability of tractor-trailer systems, a practical and novel approach is presented to design a non-linear MPC(NMPC) local planner for tractor-trailer autonomous vehicles in both forward and backward maneuvering. The tractor velocity and steering angle are considered to be control variables. The proposed NMPC local planner is designed to handle jackknife situations, avoiding multiple static obstacles, and path following in both forward and backward maneuvering. The challenges mentioned above are converted into a constrained problem that can be handled simultaneously by the proposed NMPC local planner. The direct multiple shooting approach is used to convert the optimal control problem(OCP) into a non-linear programming problem(NLP) that IPOPT solvers can solve in CasADi. The controller performance is evaluated through different backup and forward maneuvering scenarios in the Gazebo simulation environment in real-time. It achieves asymptotic stability in avoiding static obstacles and accurate tracking performance while respecting path constraints. Finally, the proposed NMPC local planner is integrated with an open-source autonomous driving software stack called AutowareAi.

最近，自主驾驶社会上有许多进展，吸引了学术界和工业的很多关注。然而，现有的作品主要专注于汽车，自动驾驶卡车算法和模型仍然需要额外的开发。在本文中，我们介绍了智能自动驾驶卡车系统。我们所呈现的系统由三个主要组成部分组成，1）一个现实的交通仿真模块，用于在测试场景中产生现实的交通流量，2）设计和评估了在现实世界部署中模仿实际卡车响应的高保真卡车模型，3 ）具有基于学习的决策算法和多模轨迹策划仪的智能计划模块，考虑到卡车的约束，道路斜率变化和周围的交通流量。我们为每个组分单独提供定量评估，以证明每个部件的保真度和性能。我们还将我们的建议系统部署在真正的卡车上，并进行真实的世界实验，表明我们的系统能力缓解了SIM-TO-REAL差距。我们的代码可以在https://github.com/inceptioresearch/iits提供

We discuss a platform that has both software and hardware components, and whose purpose is to support research into characterizing and mitigating the sim-to-real gap in robotics and vehicle autonomy engineering. The software is operating-system independent and has three main components: a simulation engine called Chrono, which supports high-fidelity vehicle and sensor simulation; an autonomy stack for algorithm design and testing; and a development environment that supports visualization and hardware-in-the-loop experimentation. The accompanying hardware platform is a 1/6th scale vehicle augmented with reconfigurable mountings for computing, sensing, and tracking. Since this vehicle platform has a digital twin within the simulation environment, one can test the same autonomy perception, state estimation, or controls algorithms, as well as the processors they run on, in both simulation and reality. A demonstration is provided to show the utilization of this platform for autonomy research. Future work will concentrate on augmenting ART/ATK with support for a full-sized Chevy Bolt EUV, which will be made available to this group in the immediate future.

本文在移动平台上介绍了四摩托车的自动起飞和着陆系统。设计的系统解决了三个具有挑战性的问题：快速姿势估计，受限的外部定位和有效避免障碍物。具体而言，首先，我们基于Aruco标记设计了着陆识别和定位系统，以帮助四极管快速计算相对姿势。其次，我们利用基于梯度的本地运动计划者快速生成无冲突的参考轨迹；第三，我们构建了一台自主状态机器，使四极管能够完全自治完成其起飞，跟踪和着陆任务；最后，我们在模拟，现实世界和室外环境中进行实验，以验证系统的有效性并证明其潜力。

自动驾驶汽车是一项不断发展的技术，旨在通过自动操作从车道变更到超车来提高安全性，可访问性，效率和便利性。超车是自动驾驶汽车最具挑战性的操作之一，当前的自动超车技术仅限于简单情况。本文研究了如何通过允许动作流产来提高自主超车的安全性。我们提出了一个基于深层Q网络的决策过程，以确定是否以及何时需要中止超车的操作。拟议的算法在与交通情况不同的模拟中进行了经验评估，这表明所提出的方法可以改善超车手动过程中的安全性。此外，使用自动班车Iseauto在现实世界实验中证明了该方法。

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

无人驾驶道路维护可能对所有利益相关者都非常有利，其关键目标是提高所有公路参与者的安全性，更有效的交通管理以及降低的道路维护成本，因此道路基础设施的标准足以使用它在自动驾驶（AD）中。本文介绍了如何扩展技术状态以实现这些目标。在使用公路标记机作为系统的“遥控道路标记系统”项目中，讨论并开发了基于远程操作的不同操作模式。此外，考虑到硬件和软件元素的功能系统概述通过实际的公路标记机对实验进行了验证，应作为在此和类似领域的未来工作的基准。