1
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Zhang L, Deng J. Deep Compressed Communication and Application in Multi-Robot 2D-Lidar SLAM: An Intelligent Huffman Algorithm. SENSORS (BASEL, SWITZERLAND) 2024; 24:3154. [PMID: 38794008 PMCID: PMC11124910 DOI: 10.3390/s24103154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/09/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024]
Abstract
Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot numbers, as 2D mapping necessitates substantial processor memory and inter-robot communication bandwidth. Thus, data compression prior to transmission becomes imperative. This study investigates the problem of communication-efficient multi-robot SLAM based on 2D maps and introduces an architecture that enables compressed communication, facilitating the transmission of full maps with significantly reduced bandwidth. We propose a framework employing a lightweight feature extraction Convolutional Neural Network (CNN) for a full map, followed by an encoder combining Huffman and Run-Length Encoding (RLE) algorithms to further compress a full map. Subsequently, a lightweight recovery CNN was designed to restore map features. Experimental validation involves applying our compressed communication framework to a two-robot SLAM system. The results demonstrate that our approach reduces communication overhead by 99% while maintaining map quality. This compressed communication strategy effectively addresses bandwidth constraints in multi-robot SLAM scenarios, offering a practical solution for collaborative SLAM applications.
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Affiliation(s)
- Liang Zhang
- School of Electrical Engineering and Automation, Anhui University, Hefei 230093, China
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2
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Riley DG, Frew EW. Fielded Human-Robot Interaction for a Heterogeneous Team in the DARPA Subterranean Challenge. ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION 2023. [DOI: 10.1145/3588325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Human supervision of multiple fielded robots is a challenging task which requires a thoughtful design and implementation of both the underlying infrastructure and the human interface. It also requires a skilled human able to manage the workload and understand when to trust the autonomy, or manually intervene. We present an end-to-end system for human-robot interaction with a heterogeneous team of robots in complex, communication-limited environments. The system includes the communication infrastructure, autonomy interaction, and human interface elements. Results of the DARPA Subterranean Challenge Final Systems Competition are presented as a case study of the design and analyze the shortcomings of the system.
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Affiliation(s)
- Danny G. Riley
- University of Colorado Boulder Department of Computer Science, USA
| | - Eric W. Frew
- University of Colorado Boulder Smead Department of Aerospace Engineering Sciences, USA
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3
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Wang S, Wang Y, Li D, Zhao Q. Distributed Relative Localization Algorithms for Multi-Robot Networks: A Survey. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052399. [PMID: 36904602 PMCID: PMC10007377 DOI: 10.3390/s23052399] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 06/12/2023]
Abstract
For a network of robots working in a specific environment, relative localization among robots is the basis for accomplishing various upper-level tasks. To avoid the latency and fragility of long-range or multi-hop communication, distributed relative localization algorithms, in which robots take local measurements and calculate localizations and poses relative to their neighbors distributively, are highly desired. Distributed relative localization has the advantages of a low communication burden and better system robustness but encounters challenges in the distributed algorithm design, communication protocol design, local network organization, etc. This paper presents a detailed survey of the key methodologies designed for distributed relative localization for robot networks. We classify the distributed localization algorithms regarding to the types of measurements, i.e., distance-based, bearing-based, and multiple-measurement-fusion-based. The detailed design methodologies, advantages, drawbacks, and application scenarios of different distributed localization algorithms are introduced and summarized. Then, the research works that support distributed localization, including local network organization, communication efficiency, and the robustness of distributed localization algorithms, are surveyed. Finally, popular simulation platforms are summarized and compared in order to facilitate future research and experiments on distributed relative localization algorithms.
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Affiliation(s)
- Shuo Wang
- School of Information, Renmin University of China, Beijing 100872, China
| | - Yongcai Wang
- School of Information, Renmin University of China, Beijing 100872, China
- Metaverse Research Center, Renmin University of China, Beijing 100872, China
| | - Deying Li
- School of Information, Renmin University of China, Beijing 100872, China
| | - Qianchuan Zhao
- Department of Automation, Tsinghua University, Beijing 100084, China
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4
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A Review of Common Techniques for Visual Simultaneous Localization and Mapping. JOURNAL OF ROBOTICS 2023. [DOI: 10.1155/2023/8872822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Mobile robots are widely used in medicine, agriculture, home furnishing, and industry. Simultaneous localization and mapping (SLAM) is the working basis of mobile robots, so it is extremely necessary and meaningful for making researches on SLAM technology. SLAM technology involves robot mechanism kinematics, logic, mathematics, perceptual detection, and other fields. However, it faces the problem of classifying the technical content, which leads to diverse technical frameworks of SLAM. Among all sorts of SLAM, visual SLAM (V-SLAM) has become the key academic research due to its advantages of low price, easy installation, and simple algorithm model. Firstly, we illustrate the superiority of V-SLAM by comparing it with other localization techniques. Secondly, we sort out some open-source V-SLAM algorithms and compare their real-time performance, robustness, and innovation. Then, we analyze the frameworks, mathematical models, and related basic theoretical knowledge of V-SLAM. Meanwhile, we review the related works from four aspects: visual odometry, back-end optimization, loop closure detection, and mapping. Finally, we prospect the future development trend and make a foundation for researchers to expand works in the future. All in all, this paper classifies each module of V-SLAM in detail and provides better readability to readers. This is undoubtedly the most comprehensive review of V-SLAM recently.
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5
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Mahboob H, Yasin JN, Jokinen S, Haghbayan MH, Plosila J, Yasin MM. DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots. SENSORS (BASEL, SWITZERLAND) 2023; 23:1025. [PMID: 36679822 PMCID: PMC9862707 DOI: 10.3390/s23021025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 06/17/2023]
Abstract
Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments.
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Affiliation(s)
- Huma Mahboob
- Autonomous Systems Laboratory, Department of Future Technologies, University of Turku, Vesilinnantie 5, 20500 Turku, Finland
| | - Jawad N. Yasin
- Autonomous Systems Laboratory, Department of Future Technologies, University of Turku, Vesilinnantie 5, 20500 Turku, Finland
- ABB Oy, 00380 Helsinki, Finland
| | - Suvi Jokinen
- Autonomous Systems Laboratory, Department of Future Technologies, University of Turku, Vesilinnantie 5, 20500 Turku, Finland
| | - Mohammad-Hashem Haghbayan
- Autonomous Systems Laboratory, Department of Future Technologies, University of Turku, Vesilinnantie 5, 20500 Turku, Finland
| | - Juha Plosila
- Autonomous Systems Laboratory, Department of Future Technologies, University of Turku, Vesilinnantie 5, 20500 Turku, Finland
| | - Muhammad Mehboob Yasin
- Department of Computer Networks, College of Computer Sciences & Information Technology, King Faisal University, Hofuf 31982, Saudi Arabia
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6
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Ji J, Zhao JS, Misyurin SY, Martins D. Localization on a-priori information of plane extraction. PLoS One 2023; 18:e0285509. [PMID: 37155677 PMCID: PMC10166544 DOI: 10.1371/journal.pone.0285509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023] Open
Abstract
Localization constitutes a critical challenge for autonomous mobile robots, with flattened walls serving as a fundamental reference for indoor localization. In numerous scenarios, prior knowledge of a wall's surface plane is available, such as planes in building information modeling (BIM) systems. This article presents a localization technique based on a-priori plane point cloud extraction. The position and pose of the mobile robot are estimated through real-time multi-plane constraints. An extended image coordinate system is proposed to represent any planes in space and establish correspondences between visible planes and those in the world coordinate system. Potentially visible points representing the constrained plane in the real-time point cloud are filtered using the filter region of interest (ROI), derived from the theoretical visible plane region within the extended image coordinate system. The number of points representing the plane influences the calculation weight in the multi-plane localization approach. Experimental validation of the proposed localization method demonstrates its allowance for redundancy in initial position and pose error.
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Affiliation(s)
- Junjie Ji
- Department of Mechanical Engineering, Tsinghua University, Beijing, China
| | - Jing-Shan Zhao
- Department of Mechanical Engineering, Tsinghua University, Beijing, China
| | - Sergey Yurievich Misyurin
- Moscow Engineering Physics Institute, National Research Nuclear University MEPhI, Moscow, Russia
- Blagonravov Mechanical Engineering Research Institute RAS, Moscow, Russia
| | - Daniel Martins
- Department of Mechanical Engineering, Federal University of Santa Catarina, Florianópolis, Brazil
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7
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Tian X, Yi P, Zhang F, Lei J, Hong Y. STV-SC: Segmentation and Temporal Verification Enhanced Scan Context for Place Recognition in Unstructured Environment. SENSORS (BASEL, SWITZERLAND) 2022; 22:8604. [PMID: 36433200 PMCID: PMC9694967 DOI: 10.3390/s22228604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 10/30/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
Place recognition is an essential part of simultaneous localization and mapping (SLAM). LiDAR-based place recognition relies almost exclusively on geometric information. However, geometric information may become unreliable when faced with environments dominated by unstructured objects. In this paper, we explore the role of segmentation for extracting key structured information. We propose STV-SC, a novel segmentation and temporal verification enhanced place recognition method for unstructured environments. It contains a range image-based 3D point segmentation algorithm and a three-stage process to detect a loop. The three-stage method consists of a two-stage candidate loop search process and a one-stage segmentation and temporal verification (STV) process. Our STV process utilizes the time-continuous feature of SLAM to determine whether there is an occasional mismatch. We quantitatively demonstrate that the STV process can trigger false detections caused by unstructured objects and effectively extract structured objects to avoid outliers. Comparison with state-of-art algorithms on public datasets shows that STV-SC can run online and achieve improved performance in unstructured environments (Under the same precision, the recall rate is 1.4∼16% higher than Scan context). Therefore, our algorithm can effectively avoid the mismatching caused by the original algorithm in unstructured environment and improve the environmental adaptability of mobile agents.
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Affiliation(s)
- Xiaojie Tian
- Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
| | - Peng Yi
- Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China
| | - Fu Zhang
- Department of Mechanical Engineering, Hong Kong University, Hong Kong 999077, China
| | - Jinlong Lei
- Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China
| | - Yiguang Hong
- Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China
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8
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Wang Y, Wen X, Yin L, Xu C, Cao Y, Gao F. Certifiably Optimal Mutual Localization With Anonymous Bearing Measurements. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3190079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Yingjian Wang
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
| | - Xiangyong Wen
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
| | - Longji Yin
- Huzhou Institute, Zhejiang University, Huzhou, China
| | - Chao Xu
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
| | - Yanjun Cao
- Huzhou Institute, Zhejiang University, Huzhou, China
| | - Fei Gao
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
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9
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Chang Y, Ebadi K, Denniston CE, Ginting MF, Rosinol A, Reinke A, Palieri M, Shi J, Chatterjee A, Morrell B, Agha-mohammadi AA, Carlone L. LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3191204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yun Chang
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kamak Ebadi
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | | | - Antoni Rosinol
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Matteo Palieri
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy
| | - Jingnan Shi
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Arghya Chatterjee
- Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Benjamin Morrell
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | - Luca Carlone
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
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10
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Zhang P, Chen G, Li Y, Dong W. Agile Formation Control of Drone Flocking Enhanced With Active Vision-Based Relative Localization. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3171096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Peihan Zhang
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Gang Chen
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuzhu Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Dong
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
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11
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Jia G, Li X, Zhang D, Xu W, Lv H, Shi Y, Cai M. Visual-SLAM Classical Framework and Key Techniques: A Review. SENSORS 2022; 22:s22124582. [PMID: 35746363 PMCID: PMC9227238 DOI: 10.3390/s22124582] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 02/01/2023]
Abstract
With the significant increase in demand for artificial intelligence, environmental map reconstruction has become a research hotspot for obstacle avoidance navigation, unmanned operations, and virtual reality. The quality of the map plays a vital role in positioning, path planning, and obstacle avoidance. This review starts with the development of SLAM (Simultaneous Localization and Mapping) and proceeds to a review of V-SLAM (Visual-SLAM) from its proposal to the present, with a summary of its historical milestones. In this context, the five parts of the classic V-SLAM framework—visual sensor, visual odometer, backend optimization, loop detection, and mapping—are explained separately. Meanwhile, the details of the latest methods are shown; VI-SLAM (Visual inertial SLAM) is reviewed and extended. The four critical techniques of V-SLAM and its technical difficulties are summarized as feature detection and matching, selection of keyframes, uncertainty technology, and expression of maps. Finally, the development direction and needs of the V-SLAM field are proposed.
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Affiliation(s)
- Guanwei Jia
- School of Physics and Electronics, Henan University, Kaifeng 475004, China; (G.J.); (X.L.); (H.L.)
| | - Xiaoying Li
- School of Physics and Electronics, Henan University, Kaifeng 475004, China; (G.J.); (X.L.); (H.L.)
| | - Dongming Zhang
- School of Physics and Electronics, Henan University, Kaifeng 475004, China; (G.J.); (X.L.); (H.L.)
- Correspondence: (D.Z.); (W.X.); Tel./Fax: +86-10-82339160
| | - Weiqing Xu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; (Y.S.); (M.C.)
- Pneumatic and Thermodynamic Energy Storage and Supply Beijing Key Laboratory, Beijing 100191, China
- Correspondence: (D.Z.); (W.X.); Tel./Fax: +86-10-82339160
| | - Haojie Lv
- School of Physics and Electronics, Henan University, Kaifeng 475004, China; (G.J.); (X.L.); (H.L.)
| | - Yan Shi
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; (Y.S.); (M.C.)
- Pneumatic and Thermodynamic Energy Storage and Supply Beijing Key Laboratory, Beijing 100191, China
| | - Maolin Cai
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; (Y.S.); (M.C.)
- Pneumatic and Thermodynamic Energy Storage and Supply Beijing Key Laboratory, Beijing 100191, China
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12
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Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior on relative pose gives a severe challenge to distinguish the inliers and outliers, and once the wrong inter-robot loop closures are used to optimize the pose graph, which can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, while it does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of inter-robot measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. Firstly, a clustering method integrating topology correctness of inter-robot loop closures is proposed to split the entire measurement set into multiple clusters. Then, our method decomposes the traditional high-dimensional consistency matrix into the sub-matrix blocks corresponding to the overlapping trajectory regions. Finally, we define the weight function to find the topologically correct and consistent subset with the maximum cardinality, then convert the weight function to the maximum clique problem from graph theory and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.
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13
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Dong H, Yu J, Xu Y, Xu Z, Shen Z, Tang J, Shen Y, Wang Y. MR-GMMapping: Communication Efficient Multi-Robot Mapping System via Gaussian Mixture Model. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3145059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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14
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Nguyen TH, Nguyen TM, Xie L. Flexible and Resource-Efficient Multi-Robot Collaborative Visual-Inertial-Range Localization. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2021.3136286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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15
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Huang Y, Shan T, Chen F, Englot B. DiSCo-SLAM: Distributed Scan Context-Enabled Multi-Robot LiDAR SLAM With Two-Stage Global-Local Graph Optimization. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2021.3138156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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16
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Ohradzansky MT, Humbert JS. Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness. SENSORS (BASEL, SWITZERLAND) 2022; 22:849. [PMID: 35161595 PMCID: PMC8840438 DOI: 10.3390/s22030849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 06/14/2023]
Abstract
Navigating unknown environments is an ongoing challenge in robotics. Processing large amounts of sensor data to maintain localization, maps of the environment, and sensible paths can result in high compute loads and lower maximum vehicle speeds. This paper presents a bio-inspired algorithm for efficiently processing depth measurements to achieve fast navigation of unknown subterranean environments. Animals developed efficient sensorimotor convergence approaches, allowing for rapid processing of large numbers of spatially distributed measurements into signals relevant for different behavioral responses necessary to their survival. Using a spatial inner-product to model this sensorimotor convergence principle, environmentally relative states critical to navigation are extracted from spatially distributed depth measurements using derived weighting functions. These states are then applied as feedback to control a simulated quadrotor platform, enabling autonomous navigation in subterranean environments. The resulting outer-loop velocity controller is demonstrated in both a generalized subterranean environment, represented by an infinite cylinder, and nongeneralized environments like tunnels and caves.
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Affiliation(s)
- Michael T. Ohradzansky
- Department of Aerospace Engineering Sciences, University of Colorado Boulder, 3775 Discovery Drive, Boulder, CO 80303, USA
| | - J. Sean Humbert
- Department of Mechanical Engineering, University of Colorado Boulder, 427 UCB, 1111 Engineering Dr, Boulder, CO 80309, USA;
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17
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Wu F, Beltrame G. Direct Sparse Odometry With Planes. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2021.3130648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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18
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19
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Zhang Z, Yu J, Tang J, Xu Y, Wang Y. MR-TopoMap: Multi-Robot Exploration Based on Topological Map in Communication Restricted Environment. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3192765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Zhaoliang Zhang
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Jincheng Yu
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Jiahao Tang
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Yuanfan Xu
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Yu Wang
- Department of Electronic Engineering, Tsinghua University, Beijing, China
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20
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Tian Y, Khosoussi K, Rosen DM, How JP. Distributed Certifiably Correct Pose-Graph Optimization. IEEE T ROBOT 2021; 37:2137-2156. [DOI: 10.1109/tro.2021.3072346] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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21
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Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems. ELECTRONICS 2021. [DOI: 10.3390/electronics10212638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process. For a collaborative SLAM system that generally uses both intra-robot and inter-robot loop closures to optimize the pose graph, it is a tough job to reject those false positive loop closures without a reliable a priori knowledge of the relative pose transformation between robots. Aiming at this solving problem, this paper proposes a two-stage false positive loop-closure rejection method based on three types of consistency checks. Firstly, a multi-robot pose-graph optimization model is given which transforms the multi-robot pose optimization problem into a maximum likelihood estimation model. Then, the principle of the false positive loop-closure rejection method based on χ2 test is proposed, in which clustering is used to reject those intra-robot false loop-closures in the first step, and a largest mutually consistent loop-based χ2 test is constructed to reject inter-robot false loop closures in the second step. Finally, an open dataset and synthetic data are used to evaluate the performance of the algorithms. The experimental results demonstrate that our method improves the accuracy and robustness of the back-end pose-graph optimization with a strong ability to reject false positive loop closures, and it is not sensitive to the initial pose at the same time. In the Computer Science and Artificial Intelligence Lab (CSAIL) dataset, the absolute position error is reduced by 55.37% compared to the dynamic scaling covariance method, and the absolute rotation error is reduced by 77.27%; in the city10,000 synthetic dataset, the absolute position error is reduced by 89.37% compared to the pairwise consistency maximization (PCM) and the absolute rotation error is reduced by 97.9%.
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22
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Majcherczyk N, Nallathambi DJ, Antonelli T, Pinciroli C. Distributed Data Storage and Fusion for Collective Perception in Resource-Limited Mobile Robot Swarms. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3076962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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23
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Lu CL, Liu ZY, Huang JT, Huang CI, Wang BH, Chen Y, Wu NH, Wang HC, Giarré L, Kuo PY. Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People. Front Robot AI 2021; 8:654132. [PMID: 34239900 PMCID: PMC8258111 DOI: 10.3389/frobt.2021.654132] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/27/2021] [Indexed: 11/13/2022] Open
Abstract
Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep reinforcement learning (DRL)-based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study. Typically, a simultaneous localization and mapping (SLAM) framework is used to estimate the robot pose and navigational goal; however, SLAM frameworks are vulnerable in certain dynamic environments. The proposed navigation method is a learning approach based on state-of-the-art DRL and can effectively avoid obstacles. When used with UWB beacons, the proposed strategy is suitable for environments with dynamic pedestrians. We also designed a handle device with an audio interface that enables BVI users to interact with the guiding robot through intuitive feedback. The UWB beacons were installed with an audio interface to obtain environmental information. The on-handle and on-beacon verbal feedback provides points of interests and turn-by-turn information to BVI users. BVI users were recruited in this study to conduct navigation tasks in different scenarios. A route was designed in a simulated ward to represent daily activities. In real-world situations, SLAM-based state estimation might be affected by dynamic obstacles, and the visual-based trail may suffer from occlusions from pedestrians or other obstacles. The proposed system successfully navigated through environments with dynamic pedestrians, in which systems based on existing SLAM algorithms have failed.
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Affiliation(s)
- Chen-Lung Lu
- Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Zi-Yan Liu
- Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Jui-Te Huang
- Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Ching-I Huang
- Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Bo-Hui Wang
- Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yi Chen
- Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Nien-Hsin Wu
- College of Technology Management, Institute of Service Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Hsueh-Cheng Wang
- Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Laura Giarré
- Department of Engineering, University of Modena and Reggio Emilia, Modena, Italy
| | - Pei-Yi Kuo
- College of Technology Management, Institute of Service Science, National Tsing Hua University, Hsinchu, Taiwan
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24
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Robust Multipath-Assisted SLAM with Unknown Process Noise and Clutter Intensity. REMOTE SENSING 2021. [DOI: 10.3390/rs13091625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In multipath-assisted simultaneous localization and mapping (SLAM), the geometric association of specular multipath components based on radio signals with environmental features is used to simultaneously localize user equipment and map the environment. We must contend with two notable model parameter uncertainties in multipath-assisted SLAM: process noise and clutter intensity. Knowledge of these two parameters is critically important to multipath-assisted SLAM, the uncertainty of which will seriously affect the SLAM accuracy. Conventional multipath-assisted SLAM algorithms generally regard these model parameters as fixed and known, which cannot meet the challenges presented in complicated environments. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing a robust multipath-assisted SLAM algorithm that can accommodate model mismatch in process noise and clutter intensity. Specifically, we describe the evolution of the process noise variance and clutter intensity via Markov chain models and integrate them into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the user equipment, environmental features and unknown model parameters to achieve the goals of simultaneous localization and mapping, as well as adaptively learning the process noise variance and clutter intensity. Finally, the simulation results demonstrate that the proposed approach is robust against the uncertainty of the process noise and clutter intensity and shows excellent performances in challenging indoor environments.
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25
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DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments. J INTELL ROBOT SYST 2021. [DOI: 10.1007/s10846-021-01362-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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26
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Ziegler T, Karrer M, Schmuck P, Chli M. Distributed Formation Estimation Via Pairwise Distance Measurements. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3062347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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27
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Kegeleirs M, Grisetti G, Birattari M. Swarm SLAM: Challenges and Perspectives. Front Robot AI 2021; 8:618268. [PMID: 33816567 PMCID: PMC8010569 DOI: 10.3389/frobt.2021.618268] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/25/2021] [Indexed: 11/13/2022] Open
Abstract
A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems-i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints.
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Abstract
AbstractCurrent visual-based simultaneous localization and mapping(SLAM) system suffers from feature loss caused by fast motion and unstructured scene in complex environments. Addressing this problem, a fast semi-direct SLAM algorithm is proposed in this paper. The main idea of this method is to combine the feature point method with the direct method in order to improve the robustness of the system in the environment of scarce visual features and low texture. First, the feature enhancement module based on subgraph is developed to extract image feature points more stably. Second, an apparent shape weighted fusion method is proposed for camera pose estimation, which can still work robustly in the absence of feature points. Third, an incremental dynamic covariance scaling algorithm is studied for optimizing the error of camera pose estimation. Finally, based on the optimized camera pose, a face element model is designed to estimate and fuse the point cloud pose, and obtain an ideal three-dimensional point cloud map. The proposed algorithm has been tested extensively on the benchmark TUM dataset and the real environment. The results show that the algorithm has better performance than existing visual based SLAM algorithms.
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29
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Dang T, Tranzatto M, Khattak S, Mascarich F, Alexis K, Hutter M. Graph‐based subterranean exploration path planning using aerial and legged robots. J FIELD ROBOT 2020. [DOI: 10.1002/rob.21993] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Tung Dang
- Department of Computer Science and Engineering University of Nevada Reno Nevada USA
| | - Marco Tranzatto
- Department of Mechanical and Process Engineering ETH Zurich Zürich Switzerland
| | - Shehryar Khattak
- Department of Computer Science and Engineering University of Nevada Reno Nevada USA
| | - Frank Mascarich
- Department of Computer Science and Engineering University of Nevada Reno Nevada USA
| | - Kostas Alexis
- Department of Computer Science and Engineering University of Nevada Reno Nevada USA
| | - Marco Hutter
- Department of Mechanical and Process Engineering ETH Zurich Zürich Switzerland
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30
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Tian Y, Koppel A, Bedi AS, How JP. Asynchronous and Parallel Distributed Pose Graph Optimization. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.3010216] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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31
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Do H, Hong S, Kim J. Robust Loop Closure Method for Multi-Robot Map Fusion by Integration of Consistency and Data Similarity. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.3010731] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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32
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Lee G, Moon BC, Lee S, Han D. Fusion of the SLAM with Wi-Fi-Based Positioning Methods for Mobile Robot-Based Learning Data Collection, Localization, and Tracking in Indoor Spaces. SENSORS 2020; 20:s20185182. [PMID: 32932851 PMCID: PMC7570627 DOI: 10.3390/s20185182] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/05/2020] [Accepted: 09/10/2020] [Indexed: 11/23/2022]
Abstract
The ability to estimate the current locations of mobile robots that move in a limited workspace and perform tasks is fundamental in robotic services. However, even if the robot is given a map of the workspace, it is not easy to quickly and accurately determine its own location by relying only on dead reckoning. In this paper, a new signal fluctuation matrix and a tracking algorithm that combines the extended Viterbi algorithm and odometer information are proposed to improve the accuracy of robot location tracking. In addition, to collect high-quality learning data, we introduce a fusion method called simultaneous localization and mapping and Wi-Fi fingerprinting techniques. The results of the experiments conducted in an office environment confirm that the proposed methods provide accurate and efficient tracking results. We hope that the proposed methods will also be applied to different fields, such as the Internet of Things, to support real-life activities.
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Coppola M, McGuire KN, De Wagter C, de Croon GCHE. A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints. Front Robot AI 2020; 7:18. [PMID: 33501187 PMCID: PMC7806031 DOI: 10.3389/frobt.2020.00018] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 02/04/2020] [Indexed: 11/30/2022] Open
Abstract
This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.
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Affiliation(s)
- Mario Coppola
- Micro Air Vehicle Laboratory (MAVLab), Department of Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
- Department of Space Systems Engineering, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
| | - Kimberly N. McGuire
- Micro Air Vehicle Laboratory (MAVLab), Department of Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
| | - Christophe De Wagter
- Micro Air Vehicle Laboratory (MAVLab), Department of Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
| | - Guido C. H. E. de Croon
- Micro Air Vehicle Laboratory (MAVLab), Department of Control and Simulation, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
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