Automated Parking Assist (APA) systems are now facing great challenges of low adoption in applications, due to users' concerns about parking capability, reliability, and completion efficiency. To upgrade the conventional APA planners and enhance user's acceptance, this research proposes an optimal-control-based parking motion planner. Its highlight lies in its control logic: planning trajectories by mirroring the parking target. This method enables: i) parking capability in narrow spaces; ii) better parking reliability by expanding Operation Design Domain (ODD); iii) faster completion of parking process; iv) enhanced computational efficiency; v) universal to all types of parking. A comprehensive evaluation is conducted. Results demonstrate the proposed planner does enhance parking success rate by 40.6%, improve parking completion efficiency by 18.0%, and expand ODD by 86.1%. It shows its superiority in difficult parking cases, such as the parallel parking scenario and narrow spaces. Moreover, the average computation time of the proposed planner is 74 milliseconds. Results indicate that the proposed planner is ready for real-time commercial applications.
自动泊车辅助(APA)系统现在面临着在应用中采用率低的问题,因为用户对停车能力、可靠性和完成效率的担忧。为了升级传统的APA规划器并提高用户的接受度,这项研究提出了一个基于最优控制的最优停车运动规划器。其特点在于其控制逻辑:通过镜像停车目标规划计划轨迹。这种方法实现了:i)在狭窄空间中的停车能力;ii)通过扩展操作设计域(ODD)提高停车可靠性;iii)加快停车过程的完成速度;iv)提高计算效率;v)适用于所有类型的停车。进行全面的评估。结果表明,所提出的规划器通过提高停车成功率40.6%、提高停车完成效率18.0%和扩大ODD 86.1%来增强了停车成功率。这表明其在困难停车场景(如平行停车场景和狭窄空间)具有优势。此外,所提出的规划器的平均计算时间为74毫秒。结果表明,所提出的规划器准备应用于实时商业应用。
https://arxiv.org/abs/2405.07538
To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by change the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. AUV's yaw angle is limited, which result in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realizes the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for multi-AUV system.
为解决多AUV系统在运动约束下的任务分配问题,即对于未操纵的AUV或其他车辆可能存在的转向能力限制,提出了一种改进的任务分配算法,将基于工作负载平衡和邻居功能的有放大的SOM神经网络算法与Dubins路径算法相结合。首先,通过基于工作负载平衡和邻居功能的有放大的SOM神经网络方法将目标任务分配给AUV。当存在可能导致轨迹规划失败的刚性约束或障碍时,通过改变SOM神经元的权重实现任务重新分配,直到AUV可以到达所有目标。然后,在几个有限案例中生成Dubins路径。AUV的偏航角度受到限制,导致目标的新分配。计算流程旨在使MATLAB和Python中的算法能够实现对多个目标的路径规划。最后,仿真结果证明,与传统方法相比,所提出的算法可以有效完成多AUV系统的任务分配任务。
https://arxiv.org/abs/2405.07536
We present a novel approach to predicting the pressure and flow rate of flexible electrohydrodynamic pumps using the Kolmogorov-Arnold Network. Inspired by the Kolmogorov-Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectively than traditional models like Multi-Layer Perceptron and Random Forest. We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.001 for pressure and flow rate predictions, respectively. The symbolic formulas extracted from KAN provided insights into the nonlinear relationships between input parameters and pump performance. These findings demonstrate that KAN offers exceptional accuracy and interpretability, making it a promising alternative for predictive modeling in electrohydrodynamic pumping.
我们提出了一种使用Kolmogorov-Arnold网络预测灵活电液动泵的压力和流量的新方法。受到Kolmogorov-Arnold表示定理的启发,KAN用可学习的光滑曲线基激活函数取代了固定的激活函数,使得它比传统模型的性能更有效地逼近复杂非线性函数。我们在柔性EHD泵参数的数据集上评估了KAN,并将其性能与RF和MLP模型进行了比较。KAN取得了卓越的预测准确性,其压力预测的均方误差为12.186,流量预测的均方误差为0.001。从KAN中提取的符号公式揭示了输入参数与泵性能之间的非线性关系。这些发现表明,KAN具有卓越的准确性和可解释性,为电液动泵预测建模提供了有前景的替代方法。
https://arxiv.org/abs/2405.07488
Robust 3D object detection remains a pivotal concern in the domain of autonomous field robotics. Despite notable enhancements in detection accuracy across standard datasets, real-world urban environments, characterized by their unstructured and dynamic nature, frequently precipitate an elevated incidence of false positives, thereby undermining the reliability of existing detection paradigms. In this context, our study introduces an advanced post-processing algorithm that modulates detection thresholds dynamically relative to the distance from the ego object. Traditional perception systems typically utilize a uniform threshold, which often leads to decreased efficacy in detecting distant objects. In contrast, our proposed methodology employs a Neural Network with a self-adaptive thresholding mechanism that significantly attenuates false negatives while concurrently diminishing false positives, particularly in complex urban settings. Empirical results substantiate that our algorithm not only augments the performance of 3D object detection models in diverse urban and adverse weather scenarios but also establishes a new benchmark for adaptive thresholding techniques in field robotics.
稳健的3D物体检测在自主领域机器人领域仍然是一个关键问题。尽管在标准数据集和现实世界城市环境中检测准确度的提升很大,但由于这些环境的特点是动态和无结构的,因此经常导致误检率的上升,从而削弱了现有检测范式的可靠性。在这种情况下,我们的研究引入了一种高级的后处理算法,该算法相对于自车对象距离自适应地调整检测阈值。传统的感知系统通常使用统一阈值,这往往导致在检测远处物体时效力下降。相反,我们提出的方法采用了一个具有自适应阈值机制的神经网络,该机制可以显著减弱误检率,同时降低误检率,特别是在复杂的城市环境中。实验结果证实,我们的算法不仅增强了各种城市和恶劣天气场景中3D物体检测模型的性能,而且为场机器人技术中的自适应阈值技术树立了新的基准。
https://arxiv.org/abs/2405.07479
In this study, we further investigate the robustness and generalization ability of an neural network (NN) based force estimation method, using the da Vinci Research Kit Si (dVRK-Si). To evaluate our method's performance, we compare the force estimation accuracy with several baseline methods. We conduct comparative studies between the dVRK classic and dVRK-Si systems to benchmark the effectiveness of these approaches. We conclude that the NN-based method provides comparable force estimation accuracy across the two systems, as the average root mean square error (RMSE) over the average range of force ratio is approximately 3.07% for the dVRK classic, and 5.27% for the dVRK-Si. On the dVRK-Si, the force estimation RMSEs for all the baseline methods are 2 to 4 times larger than the NN-based method in all directions. One possible reason is, we made assumptions in the baseline methods that static forces remain the same or dynamics is time-invariant. These assumptions may hold for the dVRK Classic, as it has pre-loaded weight and maintains horizontal self balance. Since the dVRK-Si configuration does not have this property, assumptions do not hold anymore, therefore the NN-based method significantly outperforms.
在这项研究中,我们进一步研究了基于力估计的神经网络(NN)的鲁棒性和泛化能力。为了评估我们的方法的表现,我们与几种基线方法进行了比较。我们研究了dVRK经典和dVRK-Si系统之间的比较,以衡量这些方法的成效。我们得出结论,基于NN的方法在两个系统上的力估计精度相当,因为平均范围力比的平均 root mean square error(RMSE)在dVRK经典上约为3.07%,在dVRK-Si上约为5.27%。在dVRK-Si上,所有基线方法的力估计RMSE 在所有方向上都是NN方法的2到4倍。一个可能的理由是,基线方法中假设静态力保持不变或动态是时间不变的。这些假设可能对dVRK经典成立,因为它具有预加载的权重并保持水平自平衡。由于dVRK-Si的配置不具有这种特性,假设就不再成立,因此基于NN的方法显著优于其他方法。
https://arxiv.org/abs/2405.07453
Assistance robots are the future for people who need daily care due to limited mobility or being wheelchair-bound. Current solutions of attaching robotic arms to motorized wheelchairs only provide limited additional mobility at the cost of increased size. We present a mouth joystick control interface, augmented with voice commands, for an independent quadrupedal assistance robot with an arm. We validate and showcase our system in the Cybathlon Challenges February 2024 Assistance Robot Race, where we solve four everyday tasks in record time, winning first place. Our system remains generic and sets the basis for a platform that could help and provide independence in the everyday lives of people in wheelchairs.
辅助机器人是给那些因为行动受限或轮椅使用者需要日常护理的人的未来。目前将机器人手臂附着于电动轮椅只提供了有限的额定移动性,代价是尺寸增加。我们提出了一个带嘴部 joystick 控制界面,通过语音命令进行增强的四足助听机器人,具有手臂。我们在 2024 年 2 月的 Cybathlon 挑战赛人工智能机器人赛上验证并展示了我们的系统,在那里我们用纪录时间解决了四个日常任务,获得冠军。我们的系统仍然是通用的,并为一个帮助和支持轮椅使用者在日常生活中获得独立提供基础。
https://arxiv.org/abs/2405.07445
Unmanned aerial vehicles (UAVs) visual localization in planetary aims to estimate the absolute pose of the UAV in the world coordinate system through satellite maps and images captured by on-board cameras. However, since planetary scenes often lack significant landmarks and there are modal differences between satellite maps and UAV images, the accuracy and real-time performance of UAV positioning will be reduced. In order to accurately determine the position of the UAV in a planetary scene in the absence of the global navigation satellite system (GNSS), this paper proposes JointLoc, which estimates the real-time UAV position in the world coordinate system by adaptively fusing the absolute 2-degree-of-freedom (2-DoF) pose and the relative 6-degree-of-freedom (6-DoF) pose. Extensive comparative experiments were conducted on a proposed planetary UAV image cross-modal localization dataset, which contains three types of typical Martian topography generated via a simulation engine as well as real Martian UAV images from the Ingenuity helicopter. JointLoc achieved a root-mean-square error of 0.237m in the trajectories of up to 1,000m, compared to 0.594m and 0.557m for ORB-SLAM2 and ORB-SLAM3 respectively. The source code will be available at this https URL.
无人机(UAVs)在行星上的视觉定位旨在通过卫星地图和由机载相机捕获的图像来估计UAV在世界坐标系中的绝对位置。然而,由于行星场景通常缺乏明显的地标,卫星地图和UAV图像之间存在模态差异,因此UAV定位的准确性和实时性能将降低。为了在没有全球导航卫星系统(GNSS)的情况下准确确定UAV在行星场景中的位置,本文提出了JointLoc,它通过自适应融合绝对2度自由度(2-DoF)姿态和相对6度自由度(6-DoF)姿态来估计实时UAV在世界坐标系中的位置。在拟议的行星UAV图像跨模态定位数据集上进行了广泛的比较实验,该数据集包含通过模拟引擎生成的三种典型火星地形类型以及来自Ingenuity直升机的真实火星UAV图像。JointLoc在1000m及以上的轨迹上的根均方误差为0.237m,而ORB-SLAM2和ORB-SLAM3的轨迹上的根均方误差分别为0.594m和0.557m。源代码将在此处https URL上提供。
https://arxiv.org/abs/2405.07429
Liquids and granular media are pervasive throughout human environments, yet remain particularly challenging for robots to sense and manipulate precisely. In this work, we present a systematic approach at integrating capacitive sensing within robotic end effectors to enable robust sensing and precise manipulation of liquids and granular media. We introduce the parallel-jaw RoboCAP Gripper with embedded capacitive sensing arrays that enable a robot to directly sense the materials and dynamics of liquids inside of diverse containers, including some visually opaque. When coupled with model-based control, we demonstrate that the proposed system enables a robotic manipulator to achieve state-of-the-art precision pouring accuracy for a range of substances with varying dynamics properties. Code, designs, and build details are available on the project website.
液体和颗粒介质在人类环境中无处不在,但它们对机器人来说仍然特别具有挑战性,难以精确感知和操作。在这项工作中,我们提出了一个系统方法,将电容感知融入机器人末端执行器中,以实现对液体和颗粒介质的准确感知和操作。我们介绍了一种带有嵌入式电容感知阵列的并行爪机器人抓爪,使得机器人能够直接感知不同容器内液体的材料和动态特性,包括一些视觉上不透明的材料。当与基于模型的控制相结合时,我们证明了所提出的系统可以使机器人操作器实现对各种具有不同动力学特性的物质的精确倒入状态。代码、设计和构建细节可在项目网站上获取。
https://arxiv.org/abs/2405.07423
Notation conventions for rigid transformations are as diverse as they are fundamental to the field of robotics. A well-defined convention that is practical, consistent and unambiguous is essential for the clear communication of ideas and to foster collaboration between researchers. This work presents an analysis of conventions used in state-of-the-art robotics research, defines a new notation convention, and provides software packages to facilitate its use. To shed some light on the current state of notation conventions in robotics research, this work presents an analysis of the ICRA 2023 proceedings, focusing on the notation conventions used for rigid transformations. A total of 1655 papers were inspected to identify the convention used, and key insights about trends and usage preferences are derived. Based on this analysis, a new notation convention called RIGID is defined, which complies with the "ISO 80000 Standard on Quantities and Units". The RIGID convention is designed to be concise yet unambiguous and easy to use. Additionally, this work introduces a LaTeX package that facilitates the use of the RIGID notation in manuscripts preparation through simple customizable commands that can be easily translated into variable names for software development.
用于刚性变换的表示约定在机器人领域是多种多样的,而且这些约定在清晰地传达思想和促进研究者之间的合作方面至关重要。本文对机器人研究领域的表示约定进行了分析,定义了一种新的表示约定,并提供了软件包以方便其使用。为了对机器人研究领域的当前表示约定情况进行一些了解,本文对2023年ICRA会议论文进行了分析,重点关注用于刚性变换的表示约定。共检查了1655篇论文,以确定使用的研究约定,并从中得出了一些趋势和使用偏好的关键见解。根据这一分析,定义了一个名为RIGID的新表示约定,它符合“ISO 80000标准量值和单位”。RIGID约定旨在简洁且无歧义,且易于使用。此外,本文还介绍了一个LaTeX包,通过简单的自定义命令,方便地在论文准备过程中使用RIGID表示。
https://arxiv.org/abs/2405.07351
Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic deformation. In this paper, we describe a new deformable object manipulation method including soft contact simulation, manipulation learning, and sim-to-real transfer. We propose a novel approach utilizing Vision-Based Tactile Sensors (VBTSs) as the end-effector in simulation to produce observations like relative position, squeezed area, and object contour, which are transferable to real robots. For a more realistic contact simulation, a new simulation environment including elastic, plastic, and elastoplastic deformations is created. We utilize RL strategies to train agents in the simulation, and expert demonstrations are applied for challenging tasks. Finally, we build a real experimental platform to complete the sim-to-real transfer and achieve a 90% success rate on difficult tasks such as cylinder and sphere. To test the robustness of our method, we use plasticine of different hardness and sizes to repeat the tasks including cylinder and sphere. The experimental results show superior performances of deformable object manipulation with the proposed method.
变形对象操作是一个经典的机器人研究领域。与刚性对象操作相比,由于变形特性包括弹性、塑性和弹性塑性变形,这个问题更加复杂。在本文中,我们描述了一种新的变形对象操作方法,包括软接触仿真、操作学习和仿真到实物的转移。我们提出了一个利用基于视觉的触觉传感器(VBTSs)作为末端执行器在仿真中产生相对位置、挤压区域和物体轮廓等观察值的全新方法。为了实现更加真实的接触仿真,我们创建了一个包括弹性、塑性和弹性塑性变形的新仿真环境。我们使用强化学习策略对仿真中的代理进行训练,并应用专家演示来解决具有挑战性的任务。最后,我们构建了一个真实实验平台,以实现仿真到实物的转移,并在困难任务(如圆柱体和球体)上实现90%的成功率。为了测试我们方法的稳健性,我们使用不同硬度和大小的塑料来重复包括圆柱体和球体的任务。实验结果表明,与所提出的方法相比,变形对象操作具有卓越的性能。
https://arxiv.org/abs/2405.07237
One common and desirable application of robots is exploring potentially hazardous and unstructured environments. Air-ground collaboration offers a synergistic approach to addressing such exploration challenges. In this paper, we demonstrate a system for large-scale exploration using a team of aerial and ground robots. Our system uses semantics as lingua franca, and relies on fully opportunistic communications. We highlight the unique challenges from this approach, explain our system architecture and showcase lessons learned during our experiments. All our code is open-source, encouraging researchers to use it and build upon.
机器人在探索可能具有危险性和无序环境中有许多共同和有益的应用。空地合作提供了一种协同解决这种探索挑战的方法。在本文中,我们展示了使用一支无人机和地面机器人为大规模探索的系统。我们的系统使用语义作为通用的语言,并依赖于完全的机会主义通信。我们强调了这种方法独特的挑战,解释了我们的系统架构,并展示了我们在实验中获得的教训。我们所有的代码都是开源的,鼓励研究人员使用它并在此基础上进行构建。
https://arxiv.org/abs/2405.07169
This paper presents an approach to teleoperate a manipulator using a mobile phone as a leader device. Using its IMU and camera, the phone estimates its Cartesian pose which is then used to to control the Cartesian pose of the robot's tool. The user receives visual feedback in the form of multi-view video - a point cloud rendered in a virtual reality environment. This enables the user to observe the scene from any position. To increase immersion, the robot's estimate of external forces is relayed using the phone's haptic actuator. Leader and follower are connected through wireless networks such as 5G or Wi-Fi. The paper describes the setup and analyzes its performance.
本文提出了一种使用智能手机作为领导设备进行遥控操作六连杆的方法。通过其加速度计和摄像头,智能手机估计其刚体姿态,然后用于控制机器人工具的刚体姿态。用户通过多视角视频获得视觉反馈——虚拟现实环境中渲染的点云。这使得用户能够从任何位置观察场景。为了提高沉浸感,机器人通过智能手机的触觉驱动器传递其外部力的估计。领导者并通过无线网络(如5G或Wi-Fi)与跟随者连接。本文描述了设置和分析了其性能。
https://arxiv.org/abs/2405.07128
Continuum Dexterous Manipulators (CDMs) are well-suited tools for minimally invasive surgery due to their inherent dexterity and reachability. Nonetheless, their flexible structure and non-linear curvature pose significant challenges for shape-based feedback control. The use of Fiber Bragg Grating (FBG) sensors for shape sensing has shown great potential in estimating the CDM's tip position and subsequently reconstructing the shape using optimization algorithms. This optimization, however, is under-constrained and may be ill-posed for complex shapes, falling into local minima. In this work, we introduce a novel method capable of directly estimating a CDM's shape from FBG sensor wavelengths using a deep neural network. In addition, we propose the integration of uncertainty estimation to address the critical issue of uncertainty in neural network predictions. Neural network predictions are unreliable when the input sample is outside the training distribution or corrupted by noise. Recognizing such deviations is crucial when integrating neural networks within surgical robotics, as inaccurate estimations can pose serious risks to the patient. We present a robust method that not only improves the precision upon existing techniques for FBG-based shape estimation but also incorporates a mechanism to quantify the models' confidence through uncertainty estimation. We validate the uncertainty estimation through extensive experiments, demonstrating its effectiveness and reliability on out-of-distribution (OOD) data, adding an additional layer of safety and precision to minimally invasive surgical robotics.
连续可操作性手臂(CDMs)由于其固有的灵活性和可到达性,非常适合微创手术。然而,其柔韧的结构和非线性曲率对其形状基于反馈控制提出了重大挑战。使用光纤布偶格子(FBG)传感器进行形状感知的应用在估计CDM尖端位置并使用优化算法重构形状方面显示出巨大的潜力。然而,这种优化在复杂形状上受到约束,可能陷入局部最小值。在这项工作中,我们提出了一种新方法,利用深度神经网络从FBG传感器波长直接估计CDM的形状。此外,我们还提出了一种不确定性估计来解决神经网络预测中不确定性关键问题。当输入样本超出训练分布或受到噪声污染时,神经网络预测不可靠。识别这些偏差对于将神经网络集成到手术机器人中至关重要,因为不准确的估计可能对患者造成严重风险。我们提出了一个稳健的方法,不仅提高了现有的基于FBG-形状估计技术的精度,而且通过不确定性估计机制来量化模型的置信度。我们对该方法在离散(OOD)数据上的效果和可靠性进行了广泛实验验证,为微创手术机器人添加了额外的安全性和精度。
https://arxiv.org/abs/2405.07104
Front-following is more technically difficult to implement than the other two human following technologies, but front-following technology is more practical and can be applied in more areas to solve more practical problems. In this paper, we will analyze the detailed design of LRF groups, the structure and combination design of coordinate system of Robot Detection System. We use enough beautiful figures to display our novel design idea. Our research result is open source in 2018, and this paper is just to expand the research result propagation granularity. Abundant magic design idea are included in this paper, more idea and analyzing can sear and see other paper naming with a start of Robot Design System with Jinwei Lin, the only author of this series papers.
前馈跟踪技术是实现其他两种人类跟踪技术更加困难的技术,但前馈跟踪技术更加实用,可以应用于更多的领域来解决更实际的问题。在本文中,我们将分析LRF组详细设计以及机器人检测系统坐标系统的结构与组合设计。我们使用足够的美丽图形来展示我们新颖的设计理念。我们的研究成果于2018年公开发布,而本文旨在扩展研究结果的传播粒度。本文含有丰富的魔法设计理念,您可以将其与其他使用机器人设计系统并在Jinwei Lin先生领导下撰写的论文进行比较。
https://arxiv.org/abs/2405.08022
Visual Language Navigation (VLN) powered navigation robots have the potential to guide blind people by understanding and executing route instructions provided by sighted passersby. This capability allows robots to operate in environments that are often unknown a priori. Existing VLN models are insufficient for the scenario of navigation guidance for blind people, as they need to understand routes described from human memory, which frequently contain stutters, errors, and omission of details as opposed to those obtained by thinking out loud, such as in the Room-to-Room dataset. However, currently, there is no benchmark that simulates instructions that were obtained from human memory in environments where blind people navigate. To this end, we present our benchmark, Memory-Maze, which simulates the scenario of seeking route instructions for guiding blind people. Our benchmark contains a maze-like structured virtual environment and novel route instruction data from human memory. To collect natural language instructions, we conducted two studies from sighted passersby onsite and annotators online. Our analysis demonstrates that instructions data collected onsite were more lengthy and contained more varied wording. Alongside our benchmark, we propose a VLN model better equipped to handle the scenario. Our proposed VLN model uses Large Language Models (LLM) to parse instructions and generate Python codes for robot control. We further show that the existing state-of-the-art model performed suboptimally on our benchmark. In contrast, our proposed method outperformed the state-of-the-art model by a fair margin. We found that future research should exercise caution when considering VLN technology for practical applications, as real-world scenarios have different characteristics than ones collected in traditional settings.
视觉语言导航(VLN)驱动的机器人可以利用被看见的行人提供的路线指示来引导盲人。这种能力使得机器人能够在不确定环境中操作。现有的VLN模型对于盲人导航指导不够充足,因为他们需要理解来自人类记忆的路线描述,而这些描述往往包含语病、错误和遗漏,与通过大声思考获得的路线描述相比,后者在Room-to-Room数据集中。然而,目前尚无针对从人类记忆中获得的指令在盲人导航环境中进行模拟的基准。因此,我们提出了我们的基准,Memory-Maze,它模拟了引导盲人寻路的情景。我们的基准包含一个类似于迷宫的虚拟环境和新路线指令数据。为了收集自然语言指令,我们在现场进行了两次研究,同时还在网上招募了注释者。我们的分析表明,现场收集到的指令数据更长,并且包含更多的变体。与我们的基准一起,我们提出了一个更好的VLN模型,该模型使用大型语言模型(LLM)进行解析并生成机器人控制的Python代码。我们进一步表明,现有的最先进模型在我们的基准上表现不佳。相比之下,我们的方法在基准上取得了显著的优势。我们发现,在考虑将VLN技术应用于实际应用时,未来的研究应该三思而后行,因为现实世界的场景与传统环境中的场景有所不同。
https://arxiv.org/abs/2405.07060
We present a novel framework for addressing the challenges of multi-Agent planning and formation control within intricate and dynamic environments. This framework transforms the Multi-Agent Path Finding (MAPF) problem into a Multi-Agent Trajectory Planning (MATP) problem. Unlike traditional MAPF solutions, our multilayer optimization scheme consists of a global planner optimization solver, which is dedicated to determining concise global paths for each individual robot, and a local planner with an embedded optimization solver aimed at ensuring the feasibility of local robot trajectories. By implementing a hierarchical prioritization strategy, we enhance robots' efficiency and approximate the global optimal solution. Specifically, within the global planner, we employ the Augmented Graph Search (AGS) algorithm, which significantly improves the speed of solutions. Meanwhile, within the local planner optimization solver, we utilize Control Barrier functions (CBFs) and introduced an oblique cylindrical obstacle bounding box based on the time axis for obstacle avoidance and construct a single-robot locally aware-communication circle to ensure the simplicity, speed, and accuracy of locally optimized solutions. Additionally, we integrate the weight and priority of path traces to prevent deadlocks in limiting scenarios. Compared to the other state-of-the-art methods, including CBS, ECBS and other derivative algorithms, our proposed method demonstrates superior performance in terms of capacity, flexible scalability and overall task optimality in theory, as validated through simulations and experiments.
我们提出了一个用于解决复杂和动态环境中多代理器规划与组成控制挑战的新框架。该框架将多代理器路径寻找(MAPF)问题转化为多代理器轨迹规划(MATP)问题。与传统MAPF解决方案不同,我们的多层优化方案包括一个全局规划器优化求解器,该求解器致力于确定每个单独机器人的简洁全局路径,和一个局部规划器,它具有内嵌的优化求解器,旨在确保局部机器人的轨迹可行性。通过实现分层优先级策略,我们提高了机器人的效率,并近似全局最优解。具体来说,在全局规划器中,我们采用了增强型图搜索(AGS)算法,显著提高了解决方案的速度。同时,在局部规划器优化求解器中,我们使用了控制障碍物函数(CBFs),并引入了基于时间轴的倾斜圆柱障碍物边界框以实现单机器人局部感知通信圆环,以确保局部优化解的简单性、速度和准确性。此外,我们将路径迹的权重和优先级集成到限制定员中,以防止死锁。与包括CBS、ECBS和其他派生算法在内的其他最先进方法相比,我们在理论上是表现出优越的性能,这一结论通过仿真和实验得到了验证。
https://arxiv.org/abs/2405.07043
We propose a visual servoing method consisting of a detection network and a velocity trajectory planner. First, the detection network estimates the objects position and orientation in the image space. Furthermore, these are normalized and filtered. The direction and orientation is then the input to the trajectory planner, which considers the kinematic constrains of the used robotic system. This allows safe and stable control, since the kinematic boundary values are taken into account in planning. Also, by having direction estimation and velocity planner separated, the learning part of the method does not directly influence the control value. This also enables the transfer of the method to different robotic systems without retraining, therefore being robot agnostic. We evaluate our method on different visual servoing tasks with and without clutter on two different robotic systems. Our method achieved mean absolute position errors of <0.5 mm and orientation errors of <1°. Additionally, we transferred the method to a new system which differs in robot and camera, emphasizing robot agnostic capability of our method.
我们提出了一个视觉伺服系统,由检测网络和速度轨迹规划器组成。首先,检测网络在图像空间中估计物体的位置和方向。此外,这些值进行了归一化和滤波处理。方向和朝向作为轨迹规划器的输入,考虑了所使用的机器人系统的运动约束。这允许安全且稳定的控制,因为考虑了运动边界值。此外,通过将方向估计和速度规划器分离,学习部分的方法不直接影响控制值。这还使得在不重新训练的情况下,将方法传递给不同的机器人系统。我们在两种不同机器人系统上评估我们的方法,分别考虑杂乱度和两个系统的差异。我们的方法获得了平均绝对位置误差小于0.5毫米和方向误差小于1度的结果。此外,我们将方法传递给与机器人不同但相机相同的系统,强调了我们的方法的机器人无关性。
https://arxiv.org/abs/2405.07017
We address the problem of robot guided assembly tasks, by using a learning-based approach to identify contact model parameters for known and novel parts. First, a Variational Autoencoder (VAE) is used to extract geometric features of assembly parts. Then, we combine the extracted features with physical knowledge to derive the parameters of a contact model using our newly proposed neural network structure. The measured force from real experiments is used to supervise the predicted forces, thus avoiding the need for ground truth model parameters. Although trained only on a small set of assembly parts, good contact model estimation for unknown objects were achieved. Our main contribution is the network structure that allows us to estimate contact models of assembly tasks depending on the geometry of the part to be joined. Where current system identification processes have to record new data for a new assembly process, our method only requires the 3D model of the assembly part. We evaluate our method by estimating contact models for robot-guided assembly tasks of pin connectors as well as electronic plugs and compare the results with real experiments.
我们通过使用基于学习的接触模型参数识别方法来解决机器人引导装配任务的问题。首先,我们使用变分自编码器(VAE)提取装配部件的几何特征。然后,我们将提取的特征与物理知识相结合,利用我们新提出的神经网络结构计算接触模型的参数。使用实实验测量的力来监督预测力,从而避免了需要真实模型参数的情况。 尽管我们的训练仅基于一小部分装配部件,但我们成功地估计了未知物体的接触模型。我们主要的贡献是允许根据部件的几何形状估计装配任务的接触模型。与现有的系统识别过程需要记录新的装配过程数据不同,我们的方法只需要装配部件的3D模型。我们通过估算机器人引导装配任务中的引脚连接器接触模型以及电子插座的接触模型,并与实际实验结果进行比较来评估我们的方法。
https://arxiv.org/abs/2405.06991
Nonuniform motion constraints are ubiquitous in robotic applications. Geofencing control is one such paradigm where the motion of a robot must be constrained within a predefined boundary. This paper addresses the problem of stabilizing a unicycle robot around a desired circular orbit while confining its motion within a nonconcentric external circular boundary. Our solution approach relies on the concept of the so-called Mobius transformation that, under certain practical conditions, maps two nonconcentric circles to a pair of concentric circles, and hence, results in uniform spatial motion constraints. The choice of such a Mobius transformation is governed by the roots of a quadratic equation in the post-design analysis that decides how the regions enclosed by the two circles are mapped onto the two planes. We show that the problem can be formulated either as a trajectory-constraining problem or an obstacle-avoidance problem in the transformed plane, depending on these roots. Exploiting the idea of the barrier Lyapunov function, we propose a unique control law that solves both these contrasting problems in the transformed plane and renders a solution to the original problem in the actual plane. By relating parameters of two planes under Mobius transformation and its inverse map, we further establish a connection between the control laws in two planes and determine the control law to be applied in the actual plane. Simulation and experimental results are provided to illustrate the key theoretical developments.
非均匀运动约束在机器人应用中随处可见。地平线控制是一种这样的范例,其中机器人的运动必须受到预定义边界约束。本文解决了在围绕所需圆环稳定一个独轮车机器人同时将运动限制在非同心外圆环内的过程中存在的问题。我们的解决方案依赖于称为Mobius变换的概念。在某些实际条件下,该变换将两个非同心圆映射到两个同心圆,因此导致均匀的空间运动约束。这样的Mobius变换的选择由后设计分析中二次方程的根决定。我们证明了问题可以将其表述为在变换平面上的轨迹约束问题或避障问题,具体取决于这些根。通过利用障碍李雅普诺夫函数的概念,我们提出了一个在变换平面和实际平面上都解决这些不同问题的独特控制律。通过将两个平面上的参数之间的关系与Mobius变换的逆映射建立联系,我们进一步建立了两个平面上的控制律与实际平面上的解之间的关系。通过提供仿真和实验结果来阐述这些关键理论发展。
https://arxiv.org/abs/2405.06989
Front-following is more technically difficult to implement than the other two human following technologies, but front-following technology is more practical and can be applied in more areas to solve more practical problems. The design of sensors structure is an important part of robot detection system. In this paper, we will discuss basic and significant principles and general design idea of sensor system design of robot detction system. Besides, various of novel and special useful methods will be presented and provided. We use enough beautiful figures to display our novel design idea. Our research result is open source in 2018, and this paper is just to expand the research result propagation granularity. Abundant magic design idea are included in this paper, more idea and analyzing can sear and see other paper naming with a start of Robot Design System with Jinwei Lin, the only author of this series papers.
前馈跟踪技术是三种人类跟随技术中技术难度更大的一种,但前馈跟踪技术更加实用,可以应用于更多领域,解决更实际的问题。传感器结构设计是机器人检测系统的一个重要组成部分。在本文中,我们将讨论机器人检测系统传感器系统设计的基本原则和重要理念。此外,我们将介绍许多新颖和特殊的有用方法,并提供详细的研究结果。我们在2018年发布了研究结果,而本文旨在扩展研究结果的传播粒度。本文含有丰富的魔法设计理念,可以在《机器人设计系统》系列论文中与其他论文进行比较和参考。
https://arxiv.org/abs/2405.08016