cs.RO机器人相关，共计8篇
【1】 Learning Connectivity-Maximizing Network Configurations 标题：学习连接-最大化网络配置 链接：https://arxiv.org/abs/2112.07663
作者：Daniel Mox,Vijay Kumar,Alejandro Ribeiro 机构：Sensing and Perception (GRASP) Laboratory at the University of Pennsylva-nia, PAAlejandro Ribeiro is with the Electrical and Systems Engineering Depart-ment at the University of Pennsylvania 摘要：在这项工作中，我们提出了一种数据驱动的方法来优化一组机器人的代数连接。虽然已经有大量的研究致力于这个问题，但我们缺乏一种方法，这种方法能够以一种适合于多个代理的在线应用程序的方式进行扩展。为此，我们提出了一种带卷积神经网络（CNN）的监督学习方法，该方法使用基于优化的策略从专家那里学习放置通信代理。我们展示了我们的CNN在标准线和环拓扑、105k随机生成的测试用例以及在训练期间未见过的更大团队上的性能。我们还通过基于Unity的仿真展示了我们的系统如何应用于动态机器人团队。经过训练后，我们的系统生成连接配置的速度比10-20个代理团队基于优化的方案快2个数量级。 摘要：In this work we propose a data-driven approach to optimizing the algebraic connectivity of a team of robots. While a considerable amount of research has been devoted to this problem, we lack a method that scales in a manner suitable for online applications for more than a handful of agents. To that end, we propose a supervised learning approach with a convolutional neural network (CNN) that learns to place communication agents from an expert that uses an optimization-based strategy. We demonstrate the performance of our CNN on canonical line and ring topologies, 105k randomly generated test cases, and larger teams not seen during training. We also show how our system can be applied to dynamic robot teams through a Unity-based simulation. After training, our system produces connected configurations 2 orders of magnitude faster than the optimization-based scheme for teams of 10-20 agents.
【2】 Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios 标题：强制融合场景下自主车辆的交互感知轨迹预测与规划 链接：https://arxiv.org/abs/2112.07624
作者：Kaiwen Liu,Nan Li,H. Eric Tseng,Ilya Kolmanovsky,Anouck Girard 机构： and Anouck Girard are with theDepartment of Aerospace Engineering, University of Michigan 备注：15 pages, 12 figures 摘要：一般来说，对于人类驾驶员和自动驾驶车辆而言，合并都是一项具有挑战性的任务，尤其是在交通密集的情况下，因为合并车辆通常需要与其他车辆交互，以识别或创建间隙并安全地合并到车辆中。在本文中，我们考虑的问题，强制车辆合并的强制合并方案。我们提出了一种新的博弈论控制器，称为领导者-追随者博弈控制器（LFGC），其中自主自我车辆与其他具有先验不确定驾驶意图的车辆之间的相互作用被建模为部分可观测的领导者-追随者博弈。LFGC根据观察到的轨迹在线估计其他车辆的意图，然后预测其未来轨迹，并使用模型预测控制（MPC）规划ego车辆自身的轨迹，以同时实现概率保证的安全和合并目标。为了验证LFGC的性能，我们在仿真和NGSIM数据中对其进行了测试，其中LFGC的合并成功率高达97.5%。 摘要：Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into. In this paper, we consider the problem of autonomous vehicle control for forced merge scenarios. We propose a novel game-theoretic controller, called the Leader-Follower Game Controller (LFGC), in which the interactions between the autonomous ego vehicle and other vehicles with a priori uncertain driving intentions is modeled as a partially observable leader-follower game. The LFGC estimates the other vehicles' intentions online based on observed trajectories, and then predicts their future trajectories and plans the ego vehicle's own trajectory using Model Predictive Control (MPC) to simultaneously achieve probabilistically guaranteed safety and merging objectives. To verify the performance of LFGC, we test it in simulations and with the NGSIM data, where the LFGC demonstrates a high success rate of 97.5% in merging.
【3】 Real-time SIL Emulation Architecture for Cooperative Automated Vehicles 标题：协同自动化车辆的实时SIL仿真体系结构 链接：https://arxiv.org/abs/2112.07586
作者：Nitish Gupta 机构：University of Central Florida, Part of the Computer Engineering Commons 备注：None 摘要：为连接的自动化车辆开发安全应用程序需要在许多不同的场景中进行测试。然而，重新创建用于评估安全应用程序的测试场景是一项非常具有挑战性的任务。这主要是由于通信的随机性、精确再现车辆运动的困难以及某些场景的安全问题。我们建议开发一个独立的远程车辆仿真器，该仿真器可以从模拟或以前的测试中再现远程车辆的V2V消息。预计这将大大加快开发周期。远程车辆仿真器是一种独特且易于配置的仿真和仿真设置，允许对连接的车辆应用程序进行真实、安全的软件在环（SIL）测试。它将有助于裁剪大量测试场景，加快算法开发和验证，并增加发现故障模式的概率。这反过来将有助于提高安全应用程序的质量，同时节省测试时间并降低成本。 摘要：The development of safety applications for Connected Automated Vehicles requires testing in many different scenarios. However, the recreation of test scenarios for evaluating safety applications is a very challenging task. This is mainly due to the randomness in communication, difficulty in recreating vehicle movements precisely, and safety concerns for certain scenarios. We propose to develop a standalone Remote Vehicle Emulator that can reproduce V2V messages of remote vehicles from simulations or previous tests. This is expected to accelerate the development cycle significantly. Remote Vehicle Emulator is a unique and easily configurable emulation cum simulation setup to allow Software in the Loop (SIL) testing of connected vehicle applications realistically and safely. It will help in tailoring numerous test scenarios, expediting algorithm development and validation, and increasing the probability of finding failure modes. This, in turn, will help improve the quality of safety applications while saving testing time and reducing cost.
【4】 ZUPT Aided GNSS Factor Graph with Inertial Navigation Integration for Wheeled Robots 标题：带惯性导航组合的ZUPT辅助轮式机器人GNSS因子图 链接：https://arxiv.org/abs/2112.07176
作者：Cagri Kilic,Shounak Das,Eduardo Gutierrez,Ryan Watson,Jason Gross 机构：Department of Mechanical and Aerospace Engineering , West Virginia University , Morgantown, WV , USA, The Johns Hopkins University Applied Physics Laboratory, Laurel, USA 备注：9 pages, 8 figures, Preprint Version. Published in ION GNSS 2021 摘要：在这项工作中，我们展示了零速度信息对于基于全球导航卫星系统（GNSS）的导航的重要性。在惯性导航应用中使用零速度信息和零速度更新（ZUPT）的有效性已在文献中得到证明。在这里，我们利用这些信息，并将其作为位置约束添加到GNSS因子图中。我们还将其性能与GNSS/惯性导航系统（INS）耦合因子图进行了比较。我们在三个数据集上测试了ZUPT辅助因子图方法，并将其与仅GNSS因子图进行了比较。 摘要：In this work, we demonstrate the importance of zero velocity information for global navigation satellite system (GNSS) based navigation. The effectiveness of using the zero velocity information with zero velocity update (ZUPT) for inertial navigation applications have been shown in the literature. Here we leverage this information and add it as a position constraint in a GNSS factor graph. We also compare its performance to a GNSS/inertial navigation system (INS) coupled factor graph. We tested our ZUPT aided factor graph method on three datasets and compared it with the GNSS-only factor graph.
【5】 Fast Footstep Planning on Uneven Terrain Using Deep Sequential Models 标题：基于深度序列模型的不平坦地形快速足迹规划 链接：https://arxiv.org/abs/2112.07080
作者：Hersh Sanghvi,Camillo Jose Taylor 机构：School of Engineering and Applied Science, University of Pennsylvania 备注：6 pages, 4 figures, submitted to ICRA 2022 摘要：实现腿部机器人潜力的基本挑战之一是制定穿越具有挑战性地形的计划。必须仔细选择控制动作，以便机器人不会碰撞或滑倒。关节空间的高维使得直接规划低级别的动作难以从板上感知，并且不考虑机器人在规划中的低级机制的控制堆栈不适合处理细粒度障碍物。处理此问题的一种方法是基于地形特征选择足迹位置。然而，将机器人动力学纳入足迹规划需要大量计算，比准静态情况下的计算量要大得多。在这项工作中，我们提出了一个基于LSTM的规划框架，该框架使用地形前瞻和机器人的动力学来学习可能足迹位置的概率分布，并利用LSTM的顺序性质在线性时间内查找足迹。我们的框架也可以作为一个模块来加速基于抽样的计划。我们在各种不平坦地形上的模拟单腿料斗上验证了我们的方法。 摘要：One of the fundamental challenges in realizing the potential of legged robots is generating plans to traverse challenging terrains. Control actions must be carefully selected so the robot will not crash or slip. The high dimensionality of the joint space makes directly planning low-level actions from onboard perception difficult, and control stacks that do not consider the low-level mechanisms of the robot in planning are ill-suited to handle fine-grained obstacles. One method for dealing with this is selecting footstep locations based on terrain characteristics. However, incorporating robot dynamics into footstep planning requires significant computation, much more than in the quasi-static case. In this work, we present an LSTM-based planning framework that learns probability distributions over likely footstep locations using both terrain lookahead and the robot's dynamics, and leverages the LSTM's sequential nature to find footsteps in linear time. Our framework can also be used as a module to speed up sampling-based planners. We validate our approach on a simulated one-legged hopper over a variety of uneven terrains.
【6】 Teaching a Robot to Walk Using Reinforcement Learning 标题：用强化学习教机器人行走 链接：https://arxiv.org/abs/2112.07031
作者：Jack Dibachi,Jacob Azoulay 机构：Stanford University, AA,: Decision Making under Uncertainty 备注：6 pages, 9 figures 摘要：经典控制技术，如PID和LQR，已被有效地用于维持系统状态，但当模型动态的复杂性和灵敏度增加时，这些技术变得更难实现。对于具有多个自由度的自适应机器人运动任务，该任务在经典控制技术下变得不可行。相反，强化学习可以轻松地训练最佳步行策略。我们应用深度Q-学习和增强随机搜索（ARS）在OpenAI Gym BipedalWalker-v3环境中教一个模拟的二维两足机器人如何行走。深度Q-学习不会产生高回报策略，通常过早地收敛到次优局部极大值，这可能是由于粗略离散的动作空间。然而，ARS产生了一个训练有素的机器人，并产生了一个最优策略，正式“解决”了Bipedalker-v3问题。各种简单策略，包括随机策略、手动编码的英寸前进策略和静止策略，被用作评估学习算法结果熟练程度的基准。 摘要：Classical control techniques such as PID and LQR have been used effectively in maintaining a system state, but these techniques become more difficult to implement when the model dynamics increase in complexity and sensitivity. For adaptive robotic locomotion tasks with several degrees of freedom, this task becomes infeasible with classical control techniques. Instead, reinforcement learning can train optimal walking policies with ease. We apply deep Q-learning and augmented random search (ARS) to teach a simulated two-dimensional bipedal robot how to walk using the OpenAI Gym BipedalWalker-v3 environment. Deep Q-learning did not yield a high reward policy, often prematurely converging to suboptimal local maxima likely due to the coarsely discretized action space. ARS, however, resulted in a better trained robot, and produced an optimal policy which officially "solves" the BipedalWalker-v3 problem. Various naive policies, including a random policy, a manually encoded inch forward policy, and a stay still policy, were used as benchmarks to evaluate the proficiency of the learning algorithm results.
【7】 Biomorphic propulsion system diving thunniform robotic fish 标题：生物形态推进系统潜水锥形机器鱼 链接：https://arxiv.org/abs/2112.06991
作者：I. V. Mitin,R. A. Korotaev,A. A. Ermolaev,V. B. Kazantsev 机构： 3 1 Immanuel Kant Baltic Federal University, Russia 2 National Research Lobachevsky State University of Nizhny Novgorod 备注：8 pages, 6 figures 摘要：介绍了一种用于水下机器鱼的生物形态推进系统。该系统基于弹性弦和固定在其上的尾鳍的组合。尾鳍通过两个对称的可移动推力连接到伺服电机，模拟肌肉收缩。推进系统提供振幅和频率可控的振荡尾翼运动。尾巴摆动导致机器鱼的平移运动，实现了thunniform运动原理。通过在水介质中模拟虚拟体的计算模型，设计了机器鱼的身体形状和尾鳍。构建了机器鱼装置的原型，并在实验条件下进行了测试。分析了鱼速对尾振荡幅度和频率的依赖关系。 摘要：A biomorphic propulsion systemfor underwater robotic fish is presented. The system is based on a combination of an elastic chord with a tail fin fixed on it. The tail fin is connected to servomotor by two symmetric movable thrusts simulating muscle contraction. The propulsion system provides oscillatory tail movement with controllable amplitude and frequency. Tail oscillations results in translational movement of the robotic fish implementing the thunniform principle of locomotion. The shape of the body of the robotic fish and the tail fin were designed using computational model simulating virtual body in an aquatic medium. A prototype of robotic fish device was constructed and tested in experimental conditions. Dependencies of fish velocity on the amplitude and frequency of tail oscillations were analyzed.
【8】 Structure-Exploiting Newton-Type Method for Optimal Control of Switched Systems 标题：切换系统最优控制的结构利用牛顿型方法 链接：https://arxiv.org/abs/2112.07232
作者：Sotaro Katayama,Toshiyuki Ohtsuka 机构： CIA decomposition relaxes theMINLP into a nonlinear program (NLP) in which the binarySotaroKatayamaandToshiyukiOhtsukaarewithGraduateSchoolofInformatics, KyotoUniversity 备注：15 pages, 6 figures, 2 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible 摘要：本文提出了一种有效的牛顿型方法，用于给定模式序列下切换系统的最优控制。采用基于网格细化的方法对连续时间最优控制问题（OCP）进行离散化，采用直接多重打靶法编制非线性规划（NLP），保证了牛顿型方法的局部收敛性。由于NLP的稀疏结构不同于标准OCP，因此提出了一种专用的结构开发算法（Riccati递归），该算法有效地执行了NLP的牛顿型方法。作为标准的Riccati递归算法，该方法以线性时间复杂度计算离散化网格总数的每个牛顿步。此外，如果解足够接近于局部极小值，它总是可以解牛顿型方法中出现的Karush-Kuhn-Tucker（KKT）系统。相反，一般二次规划（QP）解算器无法实现这一点，因为Hessian矩阵本质上是不确定的。此外，利用Riccati递归算法作为QP子问题的动态规划的性质，对简化Hessian矩阵进行了改进，以增强收敛性。与现成的NLP解算器进行了数值比较，结果表明，所提出的方法速度快了两个数量级。本文还对四足步态的全身最优控制进行了研究，结果表明，该方法可以实现具有刚性接触的机器人系统的全身模型预测控制。 摘要：This study proposes an efficient Newton-type method for the optimal control of switched systems under a given mode sequence. A mesh-refinement-based approach is utilized to discretize continuous-time optimal control problems (OCPs) using the direct multiple-shooting method to formulate a nonlinear program (NLP), which guarantees the local convergence of a Newton-type method. A dedicated structure-exploiting algorithm (Riccati recursion) is proposed that efficiently performs a Newton-type method for the NLP because its sparsity structure is different from a standard OCP. The proposed method computes each Newton step with linear time-complexity for the total number of discretization grids as the standard Riccati recursion algorithm. Additionally, it can always solve the Karush-Kuhn-Tucker (KKT) systems arising in the Newton-type method if the solution is sufficiently close to a local minimum. Conversely, general quadratic programming (QP) solvers cannot accomplish this because the Hessian matrix is inherently indefinite. Moreover, a modification on the reduced Hessian matrix is proposed using the nature of the Riccati recursion algorithm as the dynamic programming for a QP subproblem to enhance the convergence. A numerical comparison is conducted with off-the-shelf NLP solvers, which demonstrates that the proposed method is up to two orders of magnitude faster. Whole-body optimal control of quadrupedal gaits is also demonstrated and shows that the proposed method can achieve the whole-body model predictive control (MPC) of robotic systems with rigid contacts.
