2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)最新文献_第2页

Data Fusion-Aware Motion Planning for Ad Hoc Robotic Search Teams 自组织机器人搜索队的数据融合感知运动规划

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597681

Jack D. Center, N. Ahmed

{"title":"Data Fusion-Aware Motion Planning for Ad Hoc Robotic Search Teams","authors":"Jack D. Center, N. Ahmed","doi":"10.1109/SSRR53300.2021.9597681","DOIUrl":"https://doi.org/10.1109/SSRR53300.2021.9597681","url":null,"abstract":"This paper develops a novel algorithmic motion planning approach that allows privately-owned volunteer robotic equipment, which might otherwise remain unused, to provide value to a network of relief workers or other robots engaged in a search effort. The specific ‘Volunteer Robot Problem’ considered here is a path planning problem that asks an autonomous volunteer robot to balance information gathering tasks with data fusion when it becomes part of an ad hoc distributed robotic network supporting a deliberate relief effort. Related prior work considered optimal search strategies over information fields, but often these methods assume direct access to high performance centralized computing or to continuous communications for decentralized coordination. In this work, we provide a formal definition for the ‘Volunteer Robot Problem’ and information as it relates to general search tasks, and develop a novel information gathering planning algorithm to solve it. Our method improves upon existing sample-based planning algorithms by accounting for intermittent data fusion opportunities with other search agents, while remaining computationally lightweight and requiring minimal a priori knowledge of both ownship and other agents' states and capabilities. Simulation-based validation and comparisons to alternative planning approaches are provided of the algorithm through simulations for different multi-agent search scenarios and comparisons to other sampling-based algorithms for information-guided path planning.","PeriodicalId":423263,"journal":{"name":"2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134525581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HectorGrapher: Continuous-time Lidar SLAM with Multi-resolution Signed Distance Function Registration for Challenging Terrain HectorGrapher:具有多分辨率签名距离函数配准的具有挑战性地形的连续时间激光雷达SLAM

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597690

Kevin Daun, Marius Schnaubelt, S. Kohlbrecher, O. Stryk

引用次数: 4

Selective and Hierarchical Allocation of Sensing Resources for Anomalous Target Identification in Exploratory Missions 探测任务中异常目标识别的感知资源选择与分层分配

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597688

B. A. Blakeslee, Giuseppe Loianno

{"title":"Selective and Hierarchical Allocation of Sensing Resources for Anomalous Target Identification in Exploratory Missions","authors":"B. A. Blakeslee, Giuseppe Loianno","doi":"10.1109/SSRR53300.2021.9597688","DOIUrl":"https://doi.org/10.1109/SSRR53300.2021.9597688","url":null,"abstract":"We present an approach for selective, hierarchical allocation of sensing resources that aims to maximize information gain in exploratory missions such as search and rescue (SAR) or surveillance in an efficient manner. Specifically, we propose a methodology for perception-enabled SAR or crowd surveillance driven by anomaly detection based on low-level statistical assessment of a region. The characterizations of previously-observed regions are used to populate a window of observations that serves as “short-term memory,” providing a contextually-appropriate characterization of proximate regions in the scene. Currently-observed regions are compared with this short-term memory window, and if sufficiently dissimilar, can be considered as candidates for the presence of a SAR target or unexpected event. We adaptively allocate additional sensing resources for subsequent exploration of anomalous regions through a novel utility function that balances varied mission objectives and constraints including exploratory sensing actions, maintaining situational awareness, or ensuring some degree of confidence in self-localization. Simulation results validate the proposed approach and demonstrate its benefits with regards to efficiency in exploration while maximizing potential information gain and balancing other mission requirements and objectives.","PeriodicalId":423263,"journal":{"name":"2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131970495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lateral Skidding Motion of Tracked Vehicles using Wall Reaction Force 利用壁面反作用力的履带车辆横向打滑运动

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597863

Shotaro Kojima, Yuki Harata, K. Ohno, Takahiro Suzuki, Yoshito Okada, S. Tadokoro

引用次数: 0

Contingency-Aware Intersection System for Autonomous and Human-Driven Vehicles with Bounded Risk 具有有限风险的自动驾驶与人类驾驶车辆事故感知交叉口系统

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597687

Rashid Alyassi, Majid Khonji, Xin Huang, Sungkweon Hong, Jorge Dias

{"title":"Contingency-Aware Intersection System for Autonomous and Human-Driven Vehicles with Bounded Risk","authors":"Rashid Alyassi, Majid Khonji, Xin Huang, Sungkweon Hong, Jorge Dias","doi":"10.1109/SSRR53300.2021.9597687","DOIUrl":"https://doi.org/10.1109/SSRR53300.2021.9597687","url":null,"abstract":"Traffic intersections are natural bottlenecks in transportation networks where traffic lights have traditionally been used for vehicle coordination. With the advent of communication networks and Autonomous Vehicle (AV) technologies, new opportunities arise for more efficient automated schemes. However, with existing automated approaches, a key challenge lies in detecting and reasoning about uncertainty in the operating environment. Uncertainty arises primarily from AV trajectory tracking error and human-driven vehicle behavior. In this paper, we propose a risk-aware intelligent intersection system for AVs along with human-driven vehicles. We formulate the problem as a receding-horizon Chance-Constrained Partially Observable Markov Decision Process (CC-POMDP). We propose two fast risk estimation methods for detecting vehicle collisions. The first provides a theoretical upper bound on risk, whereas the second provides an empirical upper bound and runs faster, hence more suitable for real-time planning. We examine our approach under two scenarios: (1) a fully autonomous intersection with AVs only, and (2) a hybrid of signalized intersection for human-driven vehicles along with an intelligent scheme for AVs. We show via simulation that the system improves the intersection's efficiency and generates policies that operate within a risk threshold.","PeriodicalId":423263,"journal":{"name":"2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128732822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel Power Line Grasping Mechanism with Integrated Energy Harvester for UAV applications 基于集成能量采集器的新型无人机电力线抓取机构

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597692

Nicolai Iversen, Aljaz Kramberger, Oscar Bowen Schofield, E. Ebeid

引用次数: 2

Humanoid Interaction with Material-Moving Carts and Wheelbarrows 与材料移动推车和手推车的类人交互

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597679

Jean Chagas Vaz, Norberto Torres-Reyes, P. Oh

引用次数: 0

Machine Learning Methods for Local Motion Planning: A Study of End-to-End vs. Parameter Learning 局部运动规划的机器学习方法:端到端与参数学习的研究

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597689

Zifan Xu, Xuesu Xiao, Garrett A. Warnell, Anirudh Nair, P. Stone

{"title":"Machine Learning Methods for Local Motion Planning: A Study of End-to-End vs. Parameter Learning","authors":"Zifan Xu, Xuesu Xiao, Garrett A. Warnell, Anirudh Nair, P. Stone","doi":"10.1109/SSRR53300.2021.9597689","DOIUrl":"https://doi.org/10.1109/SSRR53300.2021.9597689","url":null,"abstract":"While decades of research efforts have been devoted to developing classical autonomous navigation systems to move robots from one point to another in a collision-free manner, machine learning approaches to navigation have been recently proposed to learn navigation behaviors from data. Two representative paradigms are end-to-end learning (directly from perception to motion) and parameter learning (from perception to parameters used by a classical underlying planner). These two types of methods are believed to have complementary pros and cons: parameter learning is expected to be robust to different scenarios, have provable guarantees, and exhibit explainable behaviors; end-to-end learning does not require extensive engineering and has the potential to outperform approaches that rely on classical systems. However, these beliefs have not been verified through real-world experiments in a comprehensive way. In this paper, we report on an extensive study to compare end-to-end and parameter learning for local motion planners in a large suite of simulated and physical experiments. In particular, we test the performance of end-to-end motion policies, which directly compute raw motor commands, and parameter policies, which compute parameters to be used by classical planners, with different inputs (e.g., raw sensor data, costmaps), and provide an analysis of the results.","PeriodicalId":423263,"journal":{"name":"2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134462396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

A Simulation-Based Framework for Generating Alerts for Human-Supervised Multi-Robot Teams in Challenging Environments 挑战性环境下人类监督多机器人团队警报生成的仿真框架

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597684

Sarah Al-Hussaini, J. Gregory, N. Dhanaraj, Satyandra K. Gupta

{"title":"A Simulation-Based Framework for Generating Alerts for Human-Supervised Multi-Robot Teams in Challenging Environments","authors":"Sarah Al-Hussaini, J. Gregory, N. Dhanaraj, Satyandra K. Gupta","doi":"10.1109/SSRR53300.2021.9597684","DOIUrl":"https://doi.org/10.1109/SSRR53300.2021.9597684","url":null,"abstract":"In a multi-agent mission with failures, uncertainty, complex dependencies, and intermittent information flow, the role of human supervisors is stressful and challenging. Alerts based on future mission predictions can be useful to assist the supervisors in responding to mission updates quickly, and devise more effective strategies. Monte-Carlo forward simulations can be used to estimate future mission states and build probability distributions of possible mission outcomes and generate alerts. However, in order to get reasonable estimates, we need a large number of simulations, representing a long-duration multi robot mission. All this needs to be performed within seconds, and therefore traditional physics based robotic simulations are infeasible. We adapt ideas from discrete event simulation paradigm, and present our novel simulation techniques like adaptive time step size, robot grouping, and intelligent time interval selection. Our technique achieves a sufficient level of accuracy in estimating probabilities, thereby generating higher quality alerts, while lowering overall fidelity of the discrete simulations for faster computation. We also provide theoretical insights on error levels in probability estimation using our method, which can guide in choosing appropriate levels of fidelity while maintaining accuracy requirements in different application scenarios. Lastly, we demonstrate sufficiently accurate real-time alert generation for a few representative mission scenarios, where the computational time is in the order of seconds using our adaptive techniques.","PeriodicalId":423263,"journal":{"name":"2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131603041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Bid Prediction for Multi-Robot Exploration with Disrupted Communications 通信中断情况下多机器人勘探出价预测

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Pub Date : 2021-10-25 DOI: 10.1109/SSRR53300.2021.9597871

Bradley Woosley, Carlos Nieto-Granda, J. Rogers, Nicholas Fung, Arthur Schang

引用次数: 2