Robotics: Science and Systems XVIII最新文献_第2页

Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots 一组空中机器人对多种环境的弹性多传感器探索

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.004

Graeme Best, Rohit Garg, John Keller, Geoffrey A. Hollinger, S. Scherer

{"title":"Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots","authors":"Graeme Best, Rohit Garg, John Keller, Geoffrey A. Hollinger, S. Scherer","doi":"10.15607/rss.2022.xviii.004","DOIUrl":"https://doi.org/10.15607/rss.2022.xviii.004","url":null,"abstract":"—We present a coordinated autonomy pipeline for multi-sensor exploration of conﬁned environments. We simultane- ously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard a team of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the beneﬁts of both visual and range sensors with a new frontier-based exploration algorithm. The autonomy pipeline is evaluated with an extensive set of ﬁeld experiments, with teams of up to 3 robots that ﬂy up to 3 m/s and distances exceeding one kilometer. We provide a summary of various ﬁeld experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. We provide an extended discussion of lessons learned, release software as open source, and present a video in the supplementary material.","PeriodicalId":340265,"journal":{"name":"Robotics: Science and Systems XVIII","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121479354","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: 9

Multi-Robot Adversarial Resilience using Control Barrier Functions 基于控制障碍函数的多机器人对抗弹性

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.053

Matthew Cavorsi, Beatrice Capelli, Lorenzo Sabattini, Stephanie Gil

引用次数: 6

Gaze Complements Control Input for Goal Prediction During Assisted Teleoperation 辅助遥操作中目标预测的注视补充控制输入

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.025

Reuben M. Aronson, H. Admoni

{"title":"Gaze Complements Control Input for Goal Prediction During Assisted Teleoperation","authors":"Reuben M. Aronson, H. Admoni","doi":"10.15607/rss.2022.xviii.025","DOIUrl":"https://doi.org/10.15607/rss.2022.xviii.025","url":null,"abstract":"Shared control systems can make complex robot teleoperation tasks easier for users. These systems predict the user's goal, determine the motion required for the robot to reach that goal, and combine that motion with the user's input. Goal prediction is generally based on the user's control input (e.g., the joystick signal). In this paper, we show that this prediction method is especially effective when users follow standard noisily optimal behavior models. In tasks with input constraints like modal control, however, this effectiveness no longer holds, so additional sources for goal prediction can improve assistance. We implement a novel shared control system that combines natural eye gaze with joystick input to predict people's goals online, and we evaluate our system in a real-world, COVID-safe user study. We find that modal control reduces the efficiency of assistance according to our model, and when gaze provides a prediction earlier in the task, the system's performance improves. However, gaze on its own is unreliable and assistance using only gaze performs poorly. We conclude that control input and natural gaze serve different and complementary roles in goal prediction, and using them together leads to improved assistance.","PeriodicalId":340265,"journal":{"name":"Robotics: Science and Systems XVIII","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125993039","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: 6

Conflict-Based Steiner Search for Multi-Agent Combinatorial Path Finding 基于冲突的多智能体组合寻径的Steiner搜索

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.058

Z. Ren, S. Rathinam, H. Choset

{"title":"Conflict-Based Steiner Search for Multi-Agent Combinatorial Path Finding","authors":"Z. Ren, S. Rathinam, H. Choset","doi":"10.15607/rss.2022.xviii.058","DOIUrl":"https://doi.org/10.15607/rss.2022.xviii.058","url":null,"abstract":"—Conventional Multi-Agent Path Finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to pre-allocated destinations. This work considers a generalized version of MAPF called Multi-Agent Combinatorial Path Finding (MCPF) where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations. This problem involves not only planning collision-free paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e. multi-target sequencing). To solve the problem, we leverage the well-known Conflict-Based Search (CBS) for MAPF and propose a novel framework called Conflict-Based Steiner Search (CBSS). CBSS interleaves (1) the conflict resolving strategy in CBS to bypass the curse of dimensionality in MAPF and (2) multiple traveling salesman algorithms to handle the combinatorics in multi-target sequencing, to compute optimal or bounded sub-optimal paths for agents while visiting all the targets. Our extensive tests verify the advantage of CBSS over baseline approaches in terms of computing shorter paths and improving success rates within a runtime limit for up to 20 agents and 50 targets. We also evaluate CBSS with several MCPF variants, which demonstrates the generality of our problem formulation and the CBSS framework.","PeriodicalId":340265,"journal":{"name":"Robotics: Science and Systems XVIII","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134071324","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: 6

Occupancy-SLAM: Simultaneously Optimizing Robot Poses and Continuous Occupancy Map 占用slam:同时优化机器人姿态和连续占用地图

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.003

Liang Zhao, Yingyu Wang, Shoudong Huang

{"title":"Occupancy-SLAM: Simultaneously Optimizing Robot Poses and Continuous Occupancy Map","authors":"Liang Zhao, Yingyu Wang, Shoudong Huang","doi":"10.15607/rss.2022.xviii.003","DOIUrl":"https://doi.org/10.15607/rss.2022.xviii.003","url":null,"abstract":"—In this paper, we propose an optimization based SLAM approach to simultaneously optimize the robot trajectory and the occupancy map using 2D laser scans (and odometry) information. The key novelty is that the robot poses and the occupancy map are optimized together, which is significantly different from existing occupancy mapping strategies where the robot poses need to be obtained first before the map can be esti- mated. In our formulation, the map is represented as a continuous occupancy map where each 2D point in the environment has a corresponding evidence value. The Occupancy-SLAM problem is formulated as an optimization problem where the variables include all the robot poses and the occupancy values at the selected discrete grid cell nodes. We propose a variation of Gauss-Newton method to solve this new formulated problem, obtaining the optimized occupancy map and robot trajectory together with their uncertainties. Our algorithm is an offline approach since it is based on batch optimization and the number of variables involved is large. Evaluations using simulations and publicly available practical 2D laser datasets demonstrate that the proposed approach can estimate the maps and robot trajectories more accurately than the state-of-the-art techniques, when a relatively accurate initial guess is provided to our algorithm. The video shows the convergence process of the proposed Occupancy- SLAM and comparison of results to Cartographer can be found at https://youtu.be/4oLyVEUC4iY.","PeriodicalId":340265,"journal":{"name":"Robotics: Science and Systems XVIII","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116600934","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: 3

Distributed Optimisation and Deconstruction of Bridges by Self-Assembling Robots 基于自组装机器人的桥梁分布式优化与解构

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.030

Edward Bray, Roderich Groß

引用次数: 2

PROX-QP: Yet another Quadratic Programming Solver for Robotics and beyond PROX-QP:机器人及其他领域的另一个二次规划求解器

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.040

Antoine Bambade, Sarah El-Kazdadi, Adrien B. Taylor, Justin Carpentier

{"title":"PROX-QP: Yet another Quadratic Programming Solver for Robotics and beyond","authors":"Antoine Bambade, Sarah El-Kazdadi, Adrien B. Taylor, Justin Carpentier","doi":"10.15607/rss.2022.xviii.040","DOIUrl":"https://doi.org/10.15607/rss.2022.xviii.040","url":null,"abstract":"—Quadratic programming (QP) has become a core modelling component in the modern engineering toolkit. This is particularly true for simulation, planning and control in robotics. Yet, modern numerical solvers have not reached the level of efficiency and reliability required in practical applications where speed, robustness, and accuracy are all necessary. In this work, we introduce a few variations of the well-established augmented Lagrangian method, specifically for solving QPs, which include heuristics for improving practical numerical performances. Those variants are embedded within an open-source software which includes an efficient C++ implementation, a modular API, as well as best-performing heuristics for our test-bed. Relying on this framework, we present a benchmark studying the practical performances of modern optimization solvers for convex QPs on generic and complex problems of the literature as well as on common robotic scenarios. This benchmark notably highlights that this approach outperforms modern solvers in terms of efficiency, accuracy and robustness for small to medium-sized problems, while remaining competitive for higher dimensions.","PeriodicalId":340265,"journal":{"name":"Robotics: Science and Systems XVIII","volume":"7 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131148416","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: 16

Collocation Methods for Second Order Systems 二阶系统的配置方法

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.038

Siro Moreno-Martin, Llu�s Ros, E. Celaya

{"title":"Collocation Methods for Second Order Systems","authors":"Siro Moreno-Martin, Llu�s Ros, E. Celaya","doi":"10.15607/rss.2022.xviii.038","DOIUrl":"https://doi.org/10.15607/rss.2022.xviii.038","url":null,"abstract":"—Collocation methods for numerical optimal control commonly assume that the system dynamics is expressed as a ﬁrst order ODE of the form ˙ x = f ( x , u , t ) , where x is the state and u the control vector. However, in many systems in robotics, the dynamics adopts the second order form ¨ q = g ( q , ˙ q , u , t ) , where q is the conﬁguration. To preserve the ﬁrst order form, the usual procedure is to introduce the velocity variable v = ˙ q and deﬁne the state as x = ( q , v ) , where q and v are treated as independent in the collocation method. As a consequence, the resulting trajectories do not fulﬁll the mandatory relationship v ( t ) = ˙ q ( t ) for all times, and even violate ¨ q = g ( q , ˙ q , u , t ) at the collocation points. This prevents the possibility of reaching a correct solution for the problem, and makes the trajectories less compliant with the system dynamics. In this paper we propose a formulation for the trapezoidal and Hermite-Simpson collocation methods that is able to deal with second order dynamics and grants the mutual consistency of the trajectories for q and v while ensuring ¨ q = g ( q , ˙ q , u , t ) at the collocation points. As a result, we obtain trajectories with a much smaller dynamical error in similar computation times, so the robot will behave closer to what is predicted by the solution. We illustrate these points by way of examples, using well-established benchmark problems from the literature.","PeriodicalId":340265,"journal":{"name":"Robotics: Science and Systems XVIII","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132734311","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: 4

Embodied Multi-Agent Task Planning from Ambiguous Instruction 基于模糊指令的多智能体任务规划

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.032

Xinzhu Liu, Xinghang Li, Di Guo, Sinan Tan, Huaping Liu, F. Sun

{"title":"Embodied Multi-Agent Task Planning from Ambiguous Instruction","authors":"Xinzhu Liu, Xinghang Li, Di Guo, Sinan Tan, Huaping Liu, F. Sun","doi":"10.15607/rss.2022.xviii.032","DOIUrl":"https://doi.org/10.15607/rss.2022.xviii.032","url":null,"abstract":"—In human-robots collaboration scenarios, a human would give robots an instruction that is intuitive for the human himself to accomplish. However, the instruction given to robots is likely ambiguous for them to understand as some information is implicit in the instruction. Therefore, it is necessary for the robots to jointly reason the operation details and perform the embodied multi-agent task planning given the ambiguous instruction. This problem exhibits significant challenges in both language understanding and dynamic task planning with the perception information. In this work, an embodied multi-agent task planning framework is proposed to utilize external knowledge sources and dynamically perceived visual information to resolve the high-level instructions, and dynamically allocate the decomposed tasks to multiple agents. Furthermore, we utilize the semantic information to perform environment perception and generate sub-goals to achieve the navigation motion. This model effectively bridges the difference between the simulation environment and the physical environment, thus it can be simultaneously applied in both simulation and physical scenarios and avoid the notori- ous sim2real problem. Finally, we build a benchmark dataset to validate the embodied multi-agent task planning problem, which includes three types of high-level instructions in which some target objects are implicit in instructions. We perform the evaluation experiments on the simulation platform and in physical scenarios, demonstrating that the proposed model can achieve promising results for multi-agent collaborative tasks.","PeriodicalId":340265,"journal":{"name":"Robotics: Science and Systems XVIII","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130108406","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: 8

DiPCAN: Distilling Privileged Information for Crowd-Aware Navigation DiPCAN:群体感知导航的特权信息提取

Robotics: Science and Systems XVIII Pub Date : 2022-06-27 DOI: 10.15607/rss.2022.xviii.045

G. Monaci, Michel Aractingi, T. Silander

引用次数: 5