Autonomous Robots最新文献_第9页

Complex environment localization system using complementary ceiling and ground map information 利用互补的天花板和地面地图信息的复杂环境定位系统

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-28 DOI: 10.1007/s10514-023-10116-6

Chee-An Yu, Hao-Yun Chen, Chun-Chieh Wang, Li-Chen Fu

{"title":"Complex environment localization system using complementary ceiling and ground map information","authors":"Chee-An Yu, Hao-Yun Chen, Chun-Chieh Wang, Li-Chen Fu","doi":"10.1007/s10514-023-10116-6","DOIUrl":"10.1007/s10514-023-10116-6","url":null,"abstract":"<div><p>This paper proposes a robust localization system using complementary information extracted from ceiling and ground plans, particularly applicable to dynamic and complex environments. The ceiling perception provides the robot with stable and time-invariant environmental features independent of the dynamic changes on the ground, whereas the ground perception allows the robot to navigate in the ground plane while avoiding stationary obstacles. We propose an architecture to fuse ground 2D LiDAR scan and ceiling 3D LiDAR scan with our enhanced mapping algorithm associating perception from both sources efficiently. The localization ability and the navigation performance can be promisingly secured even in a harsh environment with our complementary sensed information from the ground and ceiling. The salient feature of our work is that our system can simultaneously map both the ceiling and ground plane efficiently without extra efforts of deploying articulated landmarks and apply such hybrid information effectively, which facilitates the robot to travel through any indoor environment with human crowds without getting lost.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 6","pages":"669 - 683"},"PeriodicalIF":3.5,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10116-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41898236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event-based neural learning for quadrotor control 基于事件的四旋翼控制神经学习

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-23 DOI: 10.1007/s10514-023-10115-7

Estéban Carvalho, Pierre Susbielle, Nicolas Marchand, Ahmad Hably, Jilles S. Dibangoye

引用次数: 0

Learning latent representations to co-adapt to humans 学习潜在表征以共同适应人类

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-17 DOI: 10.1007/s10514-023-10109-5

Sagar Parekh, Dylan P. Losey

{"title":"Learning latent representations to co-adapt to humans","authors":"Sagar Parekh, Dylan P. Losey","doi":"10.1007/s10514-023-10109-5","DOIUrl":"10.1007/s10514-023-10109-5","url":null,"abstract":"<div><p>When robots interact with humans in homes, roads, or factories the human’s behavior often changes in response to the robot. Non-stationary humans are challenging for robot learners: actions the robot has learned to coordinate with the original human may fail after the human adapts to the robot. In this paper we introduce an algorithmic formalism that enables robots (i.e., ego agents) to <i>co-adapt</i> alongside dynamic humans (i.e., other agents) using only the robot’s low-level states, actions, and rewards. A core challenge is that humans not only react to the robot’s behavior, but the way in which humans react inevitably changes both over time and between users. To deal with this challenge, our insight is that—instead of building an exact model of the human–robots can learn and reason over <i>high-level representations</i> of the human’s policy and policy dynamics. Applying this insight we develop RILI: Robustly Influencing Latent Intent. RILI first embeds low-level robot observations into predictions of the human’s latent strategy and strategy dynamics. Next, RILI harnesses these predictions to select actions that influence the adaptive human towards advantageous, high reward behaviors over repeated interactions. We demonstrate that—given RILI’s measured performance with users sampled from an underlying distribution—we can probabilistically bound RILI’s expected performance across new humans sampled from the same distribution. Our simulated experiments compare RILI to state-of-the-art representation and reinforcement learning baselines, and show that RILI better learns to coordinate with imperfect, noisy, and time-varying agents. Finally, we conduct two user studies where RILI co-adapts alongside actual humans in a game of tag and a tower-building task. See videos of our user studies here: https://youtu.be/WYGO5amDXbQ</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 6","pages":"771 - 796"},"PeriodicalIF":3.5,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10109-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45856995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A learning-based approach to surface vehicle dynamics modeling for robust multistep prediction 基于学习的地面车辆动力学建模鲁棒多步预测方法

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-14 DOI: 10.1007/s10514-023-10114-8

Junwoo Jang, Changyu Lee, Jinwhan Kim

引用次数: 0

Regulated pure pursuit for robot path tracking 用于机器人路径跟踪的调节纯追击

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-10 DOI: 10.1007/s10514-023-10097-6

Steve Macenski, Shrijit Singh, Francisco Martín, Jonatan Ginés

{"title":"Regulated pure pursuit for robot path tracking","authors":"Steve Macenski, Shrijit Singh, Francisco Martín, Jonatan Ginés","doi":"10.1007/s10514-023-10097-6","DOIUrl":"10.1007/s10514-023-10097-6","url":null,"abstract":"<div><p>The accelerated deployment of service robots have spawned a number of algorithm variations to better handle real-world conditions. Many local trajectory planning techniques have been deployed on practical robot systems successfully. While most formulations of Dynamic Window Approach and Model Predictive Control can progress along paths and optimize for additional criteria, the use of pure path tracking algorithms is still commonplace. Decades later, Pure Pursuit and its variants continues to be one of the most commonly utilized classes of local trajectory planners. However, few Pure Pursuit variants have been proposed with schema for variable linear velocities—they either assume a constant velocity or fails to address the point at all. This paper presents a variant of Pure Pursuit designed with additional heuristics to regulate linear velocities, built atop the existing Adaptive variant. The <i>Regulated Pure Pursuit algorithm</i> makes incremental improvements on state of the art by adjusting linear velocities with particular focus on safety in constrained and partially observable spaces commonly negotiated by deployed robots. We present experiments with the Regulated Pure Pursuit algorithm on industrial-grade service robots. We also provide a high-quality reference implementation that is freely included ROS 2 Nav2 framework at https://github.com/ros-planning/navigation2 for fast evaluation.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 6","pages":"685 - 694"},"PeriodicalIF":3.5,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42455935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An overview of space-variant and active vision mechanisms for resource-constrained human inspired robotic vision 资源受限的人启发机器人视觉的空间变型和主动视觉机制综述

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-09 DOI: 10.1007/s10514-023-10107-7

Rui Pimentel de Figueiredo, Alexandre Bernardino

{"title":"An overview of space-variant and active vision mechanisms for resource-constrained human inspired robotic vision","authors":"Rui Pimentel de Figueiredo, Alexandre Bernardino","doi":"10.1007/s10514-023-10107-7","DOIUrl":"10.1007/s10514-023-10107-7","url":null,"abstract":"<div><p>In order to explore and understand the surrounding environment in an efficient manner, humans have developed a set of space-variant vision mechanisms that allow them to actively attend different locations in the surrounding environment and compensate for memory, neuronal transmission bandwidth and computational limitations in the brain. Similarly, humanoid robots deployed in everyday environments have limited on-board resources, and are faced with increasingly complex tasks that require interaction with objects arranged in many possible spatial configurations. The main goal of this work is to describe and overview biologically inspired, space-variant human visual mechanism benefits, when combined with state-of-the-art algorithms for different visual tasks (e.g. object detection), ranging from low-level hardwired attention vision (i.e. foveal vision) to high-level visual attention mechanisms. We overview the state-of-the-art in biologically plausible space-variant resource-constrained vision architectures, namely for active recognition and localization tasks.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 8","pages":"1119 - 1135"},"PeriodicalIF":3.5,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10107-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47815592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RLSS: real-time, decentralized, cooperative, networkless multi-robot trajectory planning using linear spatial separations RLSS:实时、分散、协作、无网络的多机器人轨迹规划，使用线性空间分离

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-05-30 DOI: 10.1007/s10514-023-10104-w

Baskın Şenbaşlar, Wolfgang Hönig, Nora Ayanian

{"title":"RLSS: real-time, decentralized, cooperative, networkless multi-robot trajectory planning using linear spatial separations","authors":"Baskın Şenbaşlar, Wolfgang Hönig, Nora Ayanian","doi":"10.1007/s10514-023-10104-w","DOIUrl":"10.1007/s10514-023-10104-w","url":null,"abstract":"<div><p>Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial Separations, or RLSS: a real-time decentralized trajectory planning algorithm for cooperative multi-robot teams in static environments. The algorithm requires relatively few robot capabilities, namely sensing the positions of robots and obstacles without higher-order derivatives and the ability of distinguishing robots from obstacles. There is no communication requirement and the robots’ dynamic limits are taken into account. RLSS generates and solves convex quadratic optimization problems that are kinematically feasible and guarantees collision avoidance if the resulting problems are feasible. We demonstrate the algorithm’s performance in real-time in simulations and on physical robots. We compare RLSS to two state-of-the-art planners and show empirically that RLSS does avoid deadlocks and collisions in forest-like and maze-like environments, significantly improving prior work, which result in collisions and deadlocks in such environments.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 7","pages":"921 - 946"},"PeriodicalIF":3.5,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10104-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135478964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Data-driven gait model for bipedal locomotion over continuous changing speeds and inclines 连续变化速度和坡度的两足运动数据驱动步态模型

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-05-27 DOI: 10.1007/s10514-023-10108-6

Bharat Singh, Suchit Patel, Ankit Vijayvargiya, Rajesh Kumar

{"title":"Data-driven gait model for bipedal locomotion over continuous changing speeds and inclines","authors":"Bharat Singh, Suchit Patel, Ankit Vijayvargiya, Rajesh Kumar","doi":"10.1007/s10514-023-10108-6","DOIUrl":"10.1007/s10514-023-10108-6","url":null,"abstract":"<div><p>Trajectory generation for biped robots is very complex due to the challenge posed by real-world uneven terrain. To address this complexity, this paper proposes a data-driven Gait model that can handle continuously changing conditions. Data-driven approaches are used to incorporate the joint relationships. Therefore, the deep learning methods are employed to develop seven different data-driven models, namely DNN, LSTM, GRU, BiLSTM, BiGRU, LSTM+GRU, and BiLSTM+BiGRU. The dataset used for training the Gait model consists of walking data from 10 able subjects on continuously changing inclines and speeds. The objective function incorporates the standard error from the inter-subject mean trajectory to guide the Gait model to not accurately follow the high variance points in the gait cycle, which helps in providing a smooth and continuous gait cycle. The results show that the proposed Gait models outperform the traditional finite state machine (FSM) and Basis models in terms of mean and maximum error summary statistics. In particular, the LSTM+GRU-based Gait model provides the best performance compared to other data-driven models.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 6","pages":"753 - 769"},"PeriodicalIF":3.5,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46959244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cooperative estimation and control of a diffusion-based spatiotemporal process using mobile sensors and actuators 利用移动传感器和执行器对基于扩散的时空过程进行协同估计和控制

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-05-24 DOI: 10.1007/s10514-023-10105-9

Sheng Cheng, Derek A. Paley

{"title":"Cooperative estimation and control of a diffusion-based spatiotemporal process using mobile sensors and actuators","authors":"Sheng Cheng, Derek A. Paley","doi":"10.1007/s10514-023-10105-9","DOIUrl":"10.1007/s10514-023-10105-9","url":null,"abstract":"<div><p>Monitoring and controlling a large-scale spatiotemporal process can be costly and dangerous for human operators, which can delegate the task to mobile robots for improved efficiency at a lower cost. The complex evolution of the spatiotemporal process and limited onboard resources of the robots motivate a holistic design of the robots’ actions to complete the tasks efficiently. This paper describes a cooperative framework for estimating and controlling a spatiotemporal process using a team of mobile robots that have limited onboard resources. We model the spatiotemporal process as a 2D diffusion equation that can characterize the intrinsic dynamics of the process with a partial differential equation (PDE). Measurement and actuation of the diffusion process are performed by mobile robots carrying sensors and actuators. The core of the framework is a nonlinear optimization problem, that simultaneously seeks the actuation and guidance of the robots to control the spatiotemporal process subject to the PDE dynamics. The limited onboard resources are formulated as inequality constraints on the actuation and speed of the robots. Extensive numerical studies analyze and evaluate the proposed framework using nondimensionalization and compare the optimal strategy to baseline strategies. The framework is demonstrated on an outdoor multi-quadrotor testbed using hardware-in-the-loop simulations.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":"47 6","pages":"715 - 731"},"PeriodicalIF":3.5,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44384325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Dynamic stochastic modeling for adaptive sampling of environmental variables using an AUV 基于AUV的环境变量自适应采样动态随机建模

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-04-27 DOI: 10.1007/s10514-023-10095-8

Gunhild Elisabeth Berget, Jo Eidsvik, Morten Omholt Alver, Tor Arne Johansen

引用次数: 0