机器人相关学术速递[11.15]

cs.RO机器人相关，共计7篇

【1】 Multimodal Virtual Point 3D Detection 标题：多模态虚拟点三维检测 链接：https://arxiv.org/abs/2111.06881

作者：Tianwei Yin,Xingyi Zhou,Philipp Krähenbühl 机构：UT Austin 备注：NeurIPS 2021, code available at this https URL 摘要：基于激光雷达的传感驱动当前的自主车辆。尽管进展迅速，但目前的激光雷达传感器在分辨率和成本方面仍落后于传统彩色相机20年。对于自动驾驶，这意味着靠近传感器的大型物体很容易看到，但距离较远或较小的物体仅包含一个或两个测量值。这是一个问题，尤其是当这些物体被证明是驾驶危险时。另一方面，这些相同的物体在机载RGB传感器中清晰可见。在这项工作中，我们提出了一种将RGB传感器无缝融合到基于激光雷达的3D识别中的方法。我们的方法采用一组2D检测来生成密集的3D虚拟点，以增强原本稀疏的3D点云。这些虚拟点自然地与任何基于激光雷达的标准3D探测器以及常规激光雷达测量集成在一起。由此产生的多模态检测器简单有效。在大规模nuScenes数据集上的实验结果表明，我们的框架将一个强大的中心点基线提高了6.6MAP，并且优于其他融合方法。代码和更多可视化可在https://tianweiy.github.io/mvp/ 摘要：Lidar-based sensing drives current autonomous vehicles. Despite rapid progress, current Lidar sensors still lag two decades behind traditional color cameras in terms of resolution and cost. For autonomous driving, this means that large objects close to the sensors are easily visible, but far-away or small objects comprise only one measurement or two. This is an issue, especially when these objects turn out to be driving hazards. On the other hand, these same objects are clearly visible in onboard RGB sensors. In this work, we present an approach to seamlessly fuse RGB sensors into Lidar-based 3D recognition. Our approach takes a set of 2D detections to generate dense 3D virtual points to augment an otherwise sparse 3D point cloud. These virtual points naturally integrate into any standard Lidar-based 3D detectors along with regular Lidar measurements. The resulting multi-modal detector is simple and effective. Experimental results on the large-scale nuScenes dataset show that our framework improves a strong CenterPoint baseline by a significant 6.6 mAP, and outperforms competing fusion approaches. Code and more visualizations are available at https://tianweiy.github.io/mvp/

【2】 Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation 标题：用于一致越野地面导航的自反射地形感知机器人自适应 链接：https://arxiv.org/abs/2111.06742

作者：Sriram Siva,Maggie Wigness,John G. Rogers,Long Quang,Hao Zhang 备注：13 pages, 7 figures, IJRR21 摘要：地面机器人需要具备穿越非结构化和无准备地形的关键能力，并能够避免障碍物，以完成现实世界机器人应用（如灾难响应）中的任务。当机器人在森林等越野野外环境中工作时，由于地形特征和机器人自身的变化，机器人的实际行为通常与预期或计划的行为不匹配。因此，机器人适应一致行为生成的能力对于非结构化越野地形上的机动性至关重要。为了应对这一挑战，我们提出了一种新的自反射地形感知自适应方法，用于地面机器人生成一致的控制，以便在非结构化越野地形上导航，这使得机器人能够在适应各种非结构化地形的同时，通过机器人自我反射更准确地执行预期行为。为了评估我们的方法的性能，我们在不同的非结构化越野地形上使用具有各种功能变化的真实地面机器人进行了广泛的实验。综合实验结果表明，我们的自反射地形感知自适应方法能够使地面机器人产生一致的导航行为，并且优于先前和基线技术。 摘要：Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method's performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.

【3】 Neural Motion Planning for Autonomous Parking 标题：自主停车的神经运动规划 链接：https://arxiv.org/abs/2111.06739

作者：Dongchan Kim,Kunsoo Huh 机构：Dongchan Kim and Kunsoo Huh are with the Department of Automo-tive Engineering, Hanyang University 备注：8 pages, 11 figures 摘要：本文提出了一种将深度生成网络与传统运动规划方法相结合的混合运动规划策略。现有的规划方法，如A*和混合A*广泛应用于路径规划任务中，因为它们能够在复杂环境中确定可行路径；然而，它们在效率方面有局限性。为了克服这些限制，提出了一种基于神经网络的路径规划算法，即神经混合a*。本文提出使用条件变分自动编码器（CVAE）来引导搜索算法，利用CVAE在给定停车环境信息的情况下学习规划空间信息的能力。基于演示中学习到的可行轨迹分布，采用非均匀展开策略。该方法有效地学习了给定状态的表示，并在算法性能方面有所改进。 摘要：This paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method. Existing planning methods such as A* and Hybrid A* are widely used in path planning tasks because of their ability to determine feasible paths even in complex environments; however, they have limitations in terms of efficiency. To overcome these limitations, a path planning algorithm based on a neural network, namely the neural Hybrid A*, is introduced. This paper proposes using a conditional variational autoencoder (CVAE) to guide the search algorithm by exploiting the ability of CVAE to learn information about the planning space given the information of the parking environment. A non-uniform expansion strategy is utilized based on a distribution of feasible trajectories learned in the demonstrations. The proposed method effectively learns the representations of a given state, and shows improvement in terms of algorithm performance.

【4】 A Review on Communication Protocols for Autonomous Unmanned Aerial Vehicles for Inspection Application 标题：自主式无人机巡检通信协议综述 链接：https://arxiv.org/abs/2111.06714

作者：Liping Shi,Néstor J. Hernández Marcano,Rune Hylsberg Jacobsen 机构：Department of Electrical and Computer Engineering, Aarhus University, Denmark, PREPRINT 备注：28 pages 摘要：通信系统是自主式无人机系统设计的关键部分。它必须考虑不同的因素，包括无人机的效率、可靠性和机动性。此外，多无人机系统需要一个通信系统来协助无人机团队中的信息共享、任务分配和协作。在本文中，我们将回顾支持无人机团队的通信解决方案，同时考虑在电力线路检查行业中的应用。我们对候选无线通信技术进行了综述{用于支持无人机应用中的通信。回顾了这些候选技术的性能测量和无人机相关信道建模。讨论了构建无人机网状网络的当前技术。然后，我们分析了机器人通信中间件ROS和ROS2.Base的结构、接口和性能d在我们的回顾中，介绍了通信系统各层候选解决方案的特性和依赖性。 摘要：The communication system is a critical part of the system design for the autonomous UAV. It has to address different considerations, including efficiency, reliability and mobility of the UAV. In addition, a multi-UAV system requires a communication system to assist information sharing, task allocation and collaboration in a team of UAVs. In this paper, we review communication solutions for supporting a team of UAVs while considering an application in the power line inspection industry. We provide a review of candidate wireless communication technologies {for supporting communication in UAV applications. Performance measurements and UAV-related channel modeling of those candidate technologies are reviewed. A discussion of current technologies for building UAV mesh networks is presented. We then analyze the structure, interface and performance of robotic communication middleware, ROS and ROS2. Based on our review, the features and dependencies of candidate solutions in each layer of the communication system are presented.

【5】 Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots 标题：使用腿式和空中机器人自主合作探测地下环境 链接：https://arxiv.org/abs/2111.06482

作者：Mihir Kulkarni,Mihir Dharmadhikari,Marco Tranzatto,Samuel Zimmermann,Victor Reijgwart,Paolo De Petris,Huan Nguyen,Nikhil Khedekar,Christos Papachristos,Lionel Ott,Roland Siegwart,Marco Hutter,Kostas Alexis 机构： 1University of Nevada 备注：8 pages, 5 figures. Submitted to the IEEE International Conference on Robotics and Automation, 2022. Code available at this https URL 摘要：本文提出了一种利用腿式和空中机器人对地下环境进行自主团队探索的新策略。该策略针对地下环境，如洞穴网络和地下矿山，通常涉及复杂、大规模和多分支拓扑，而其中的无线通信可以是特定的具有挑战性的是，这项工作围绕着车载探索路径规划器的协同作用而构建，该规划器允许弹性长期自治和多机器人协调框架。车载路径规划器是跨腿和飞行机器人的统一，能够在陡坡和不同几何体的环境中导航导航链接可用，团队的每个机器人共享子地图到一个集中的位置，多机器人协调框架确定探索空间的全球边界，通知每个系统它应该在哪里重新定位以最好地继续其任务。该策略通过在地下mine在瑞士使用一只腿和一个飞行机器人进行了45分钟的集体探索，并对三个系统进行了更长时间的模拟研究。 摘要：This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying robots and enables navigation in environments with steep slopes, and diverse geometries. When a communication link is available, each robot of the team shares submaps to a centralized location where a multi-robot coordination framework identifies global frontiers of the exploration space to inform each system about where it should re-position to best continue its mission. The strategy is verified through a field deployment inside an underground mine in Switzerland using a legged and a flying robot collectively exploring for 45 min, as well as a longer simulation study with three systems.

【6】 Towards Transferring Human Preferences from Canonical to Actual Assembly Tasks 标题：将人的偏好从规范的装配任务转移到实际的装配任务 链接：https://arxiv.org/abs/2111.06454

作者：Heramb Nemlekar,Runyu Guan,Guanyang Luo,Satyandra K. Gupta,Stefanos Nikolaidis 机构：GuptaandStefanosNikolaidisarewiththeUniversityofSouth-ern California 备注：7 pages, 7 figures 摘要：为了根据人类用户在装配任务中的个人偏好为其提供帮助，机器人通常需要在给定任务中进行用户演示。然而，在实际装配任务中提供演示可能会非常繁琐和耗时。我们的论文是，我们可以从具有代表性的ca中的演示中了解用户在装配任务中的偏好非标准任务。受人类运动经济性之前工作的启发，我们建议将用户偏好表示为抽象任务不可知特征的线性函数，如运动以及用户所需的体力和脑力。对于每个用户，我们从标准任务的演示中了解他们的偏好，并使用学习到的偏好在实际装配任务中预测他们的行动，而不需要用户在实际任务中进行任何演示。我们在模型飞机装配研究中评估了我们提出的方法，并表明偏好可以有效地从规范装配任务转移到实际装配任务，从而使机器人能够预测用户的行动。 摘要：To assist human users according to their individual preference in assembly tasks, robots typically require user demonstrations in the given task. However, providing demonstrations in actual assembly tasks can be tedious and time-consuming. Our thesis is that we can learn user preferences in assembly tasks from demonstrations in a representative canonical task. Inspired by previous work in economy of human movement, we propose to represent user preferences as a linear function of abstract task-agnostic features, such as movement and physical and mental effort required by the user. For each user, we learn their preference from demonstrations in a canonical task and use the learned preference to anticipate their actions in the actual assembly task without any user demonstrations in the actual task. We evaluate our proposed method in a model-airplane assembly study and show that preferences can be effectively transferred from canonical to actual assembly tasks, enabling robots to anticipate user actions.

【7】 Scalable Operator Allocation for Multi-Robot Assistance: A Restless Bandit Approach 标题：多机器人辅助的可扩展算子分配：一种无休止的强盗方法 链接：https://arxiv.org/abs/2111.06437

作者：Abhinav Dahiya,Nima Akbarzadeh,Aditya Mahajan,Stephen L. Smith 机构： University of Waterloo 备注：11 pages + 4 page Appendix, 7 Figures 摘要：本文考虑具有多个半自主机器人的系统中的人类操作者分配问题。每个机器人都需要执行一系列独立的任务，每项任务都有可能失败并陷入故障状态。如果需要，人类操作员可以协助或遥控机器人。用于解决此类问题的传统MDP技术面临可伸缩性问题，因为状态和动作空间随着机器人和操作员数量呈指数增长。在本文中，我们推导了算子分配问题是可索引的条件，使惠特尔指数启发式的使用成为可能。这些条件可以很容易地检查以验证可转位性，并且我们表明它们适用于广泛的感兴趣的问题。我们的关键洞察是利用单个机器人的价值函数的结构，从而为每个机器人的每个状态分别验证条件。我们将这些条件应用于远程机器人监控系统中常见的两种类型的转换。通过数值模拟，我们证明了Whittle指数策略作为一种接近最优且可扩展的方法的有效性，其性能优于现有的可扩展方法。 摘要：In this paper, we consider the problem of allocating human operators in a system with multiple semi-autonomous robots. Each robot is required to perform an independent sequence of tasks, subjected to a chance of failing and getting stuck in a fault state at every task. If and when required, a human operator can assist or teleoperate a robot. Conventional MDP techniques used to solve such problems face scalability issues due to exponential growth of state and action spaces with the number of robots and operators. In this paper we derive conditions under which the operator allocation problem is indexable, enabling the use of the Whittle index heuristic. The conditions can be easily checked to verify indexability, and we show that they hold for a wide range of problems of interest. Our key insight is to leverage the structure of the value function of individual robots, resulting in conditions that can be verified separately for each state of each robot. We apply these conditions to two types of transitions commonly seen in remote robot supervision systems. Through numerical simulations, we demonstrate the efficacy of Whittle index policy as a near-optimal and scalable approach that outperforms existing scalable methods.