2020 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第6页

Probe-before-step walking strategy for multi-legged robots on terrain with risk of collapse 多足机器人在有塌陷危险的地形上的步前探测行走策略

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197154

Eranda Tennakoon, T. Peynot, Jonathan M. Roberts, N. Kottege

{"title":"Probe-before-step walking strategy for multi-legged robots on terrain with risk of collapse","authors":"Eranda Tennakoon, T. Peynot, Jonathan M. Roberts, N. Kottege","doi":"10.1109/ICRA40945.2020.9197154","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197154","url":null,"abstract":"Multi-legged robots are effective at traversing rough terrain. However, terrains that include collapsible footholds (i.e. regions that can collapse when stepped on) remain a significant challenge, especially since such situations can be extremely difficult to anticipate using only exteroceptive sensing. State-of-the-art methods typically use various stabilisation techniques to regain balance and counter changing footholds. However, these methods are likely to fail if safe footholds are sparse and spread out or if the robot does not respond quickly enough after a foothold collapse. This paper presents a novel method for multi-legged robots to probe and test the terrain for collapses using its legs while walking. The proposed method improves on existing terrain probing approaches, and integrates the probing action into a walking cycle. A follow-the-leader strategy with a suitable gait and stance is presented and implemented on a hexapod robot. The proposed method is experimentally validated, demonstrating the robot can safely traverse terrain containing collapsible footholds.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"17 2 1","pages":"5530-5536"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82885168","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: 13

Online Camera-LiDAR Calibration with Sensor Semantic Information 基于传感器语义信息的相机-激光雷达在线标定

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196627

Yufeng Zhu, Chenghui Li, Yubo Zhang

{"title":"Online Camera-LiDAR Calibration with Sensor Semantic Information","authors":"Yufeng Zhu, Chenghui Li, Yubo Zhang","doi":"10.1109/ICRA40945.2020.9196627","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196627","url":null,"abstract":"As a crucial step of sensor data fusion, sensor calibration plays a vital role in many cutting-edge machine vision applications, such as autonomous vehicles and AR/VR. Existing techniques either require quite amount of manual work and complex settings, or are unrobust and prone to produce suboptimal results. In this paper, we investigate the extrinsic calibration of an RGB camera and a light detection and ranging (LiDAR) sensor, which are two of the most widely used sensors in autonomous vehicles for perceiving the outdoor environment. Specifically, we introduce an online calibration technique that automatically computes the optimal rigid motion transformation between the aforementioned two sensors and maximizes their mutual information of perceived data, without the need of tuning environment settings. By formulating the calibration as an optimization problem with a novel calibration quality metric based on semantic features, we successfully and robustly align pairs of temporally synchronized camera and LiDAR frames in real time. Demonstrated on several autonomous driving tasks, our method outperforms state-of-the-art edge feature based auto-calibration approaches in terms of robustness and accuracy.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"13 1","pages":"4970-4976"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82909903","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: 42

A Connectivity-Prediction Algorithm and its Application in Active Cooperative Localization for Multi-Robot Systems 一种连接预测算法及其在多机器人系统主动协同定位中的应用

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197083

Liang Zhang, Zexu Zhang, R. Siegwart, Jen Jen Chung

引用次数: 3

Towards Plan Transformations for Real-World Mobile Fetch and Place 面向现实世界移动获取和放置的计划转换

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197446

Gayane Kazhoyan, Arthur Niedzwiecki, M. Beetz

引用次数: 4

Adaptive Unknown Object Rearrangement Using Low-Cost Tabletop Robot 基于低成本桌面机器人的自适应未知物体重排

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197356

Chun-Yu Chai, Wen-Hsiao Peng, Shiao-Li Tsao

引用次数: 4

Grounding Language to Landmarks in Arbitrary Outdoor Environments 在任意的室外环境中将语言与地标联系起来

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197068

Matthew Berg, Deniz Bayazit, Rebecca Mathew, Ariel Rotter-Aboyoun, Ellie Pavlick, Stefanie Tellex

{"title":"Grounding Language to Landmarks in Arbitrary Outdoor Environments","authors":"Matthew Berg, Deniz Bayazit, Rebecca Mathew, Ariel Rotter-Aboyoun, Ellie Pavlick, Stefanie Tellex","doi":"10.1109/ICRA40945.2020.9197068","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197068","url":null,"abstract":"Robots operating in outdoor, urban environments need the ability to follow complex natural language commands which refer to never-before-seen landmarks. Existing approaches to this problem are limited because they require training a language model for the landmarks of a particular environment before a robot can understand commands referring to those landmarks. To generalize to new environments outside of the training set, we present a framework that parses references to landmarks, then assesses semantic similarities between the referring expression and landmarks in a predefined semantic map of the world, and ultimately translates natural language commands to motion plans for a drone. This framework allows the robot to ground natural language phrases to landmarks in a map when both the referring expressions to landmarks and the landmarks themselves have not been seen during training. We test our framework with a 14-person user evaluation demonstrating an end-to-end accuracy of 76.19% in an unseen environment. Subjective measures show that users find our system to have high performance and low workload. These results demonstrate our approach enables untrained users to control a robot in large unseen outdoor environments with unconstrained natural language.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"97 1","pages":"208-215"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79470739","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: 12

Collision-free Navigation of Human-centered Robots via Markov Games 基于Markov游戏的以人为中心的机器人无碰撞导航

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196810

Guo Ye, Qinjie Lin, Tzung-Han Juang, Han Liu

引用次数: 2

R3T: Rapidly-exploring Random Reachable Set Tree for Optimal Kinodynamic Planning of Nonlinear Hybrid Systems 非线性混合系统最优动力学规划的快速探索随机可达集树

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196802

A. Wu, Sadra Sadraddini, Russ Tedrake

引用次数: 17

RAVEN-S: Design and Simulation of a Robot for Teleoperated Microgravity Rodent Dissection Under Time Delay 延时条件下遥控微重力啮齿动物解剖机器人RAVEN-S的设计与仿真

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196691

Andrew Lewis, David Drajeske, J. Raiti, A. Berens, J. Rosen, B. Hannaford

{"title":"RAVEN-S: Design and Simulation of a Robot for Teleoperated Microgravity Rodent Dissection Under Time Delay","authors":"Andrew Lewis, David Drajeske, J. Raiti, A. Berens, J. Rosen, B. Hannaford","doi":"10.1109/ICRA40945.2020.9196691","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196691","url":null,"abstract":"The International Space Station (ISS) serves as a research lab for a wide variety of experiments including some that study the biological effects of microgravity and spaceflight using the Rodent Habitat and Microgravity Science Glovebox (MSG). Astronauts train for onboard dissections of rodents following basic training. An alternative approach for conducting these experiments is teleoperation of a robot located on the ISS from earth by a scientist who is proficient in rodent dissection. This pilot study addresses (1) the effects of extreme time delay on skill degradation during Fundamentals of Laparoscopic Surgery (FLS) tasks and rodent dissections using RAVEN II; (2) derivation and testing of rudimentary interaction force estimation; (3) elicitation of design requirements for an onboard dissection robot, RAVEN-S; and (4) simulation of the RAVEN-S prototype design with dissection data. The results indicate that the tasks’ completion times increased by a factor of up to 9 for a 3 s time delay while performing manipulation and cutting tasks (FLS model) and by a factor of up to 3 for a 0.75 s time delay during mouse dissection tasks (animal model). Average robot forces/torques of 14N/0.1Nm (peak 90N/0.75Nm) were measured along with average linear/angular velocities of 0.02m/s / 4rad/s (peak 0.1m/s / 40rad/s) during dissection. A triangular configuration of three arms with respect to the operation site showed the best configuration given the MSG geometry and the dissection tasks. In conclusion, the results confirm the feasibility of utilizing a surgically-inspired RAVEN-S robot for teleoperated rodent dissection for successful completion of the predefined tasks in the presence of communications time delay between the ISS and ground control.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"50 1","pages":"11332-11337"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88719069","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: 1

3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning 基于深度学习的三维场景几何感知约束相机定位

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196940

Mi Tian, Qiong Nie, Hao Shen

引用次数: 10