ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Information Science and Technology 14 July 2022

Earthshaker: A mobile rescue robot for emergencies and disasters through teleoperation and autonomous navigation

  • 1.

    Department of Precision Machinery and Precision Instruments, University of Science and Technology of China, Hefei 230027, China

  • 2.

    College of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0066
More Information
  • Author Bio:

    Yu Zhang received the B.E. degree from the University of Science and Technology of China (USTC) and is currently a master’s student in the Bio-Inspired Robotics Laboratory at USTC. His research interests include system design for special robots and legged robots

    Yuxiang Li received the master’s degree in Instrument and Meter Engineering from Zhengzhou University. He is currently pursuing his Ph.D. degree in Harbin Institute of Technology Shenzhen. His research interests include robotics and artificial intelligence

    Wei Gao is currently an Associate Research Fellow in the Department of Precision Machinery and Precision Instrumentation at the University of Science and Technology of China (USTC). He received his B.E. degree from the Department of Mechanical Engineering at Northwestern Polytechnical University in 2011, and his Ph.D. degree from the Department of Mechanical Engineering at Florida State University (FSU) in 2019. He was a postdoctoral research fellow in FSU from 2019 to 2020, and in USTC from 2020 to 2022. His research focuses on dynamic control of mobile robots

    Haoyao Chen is currently a Professor in Harbin Institute of Technology Shenzhen and the State Key Laboratory of Robotics and System of China. He received the bachelor’s degree in Mechatronics and Automation from the University of Science and Technology of China (USTC) in 2004, and the Ph.D. degree in Robotics from both USTC and the City University of Hong Kong in 2009. He worked as a Visiting Scholar in the Autonomous Systems Lab in ETHz, Switzerland. His research interests include aerial manipulation and transportation, robotic perception and cognition, multi-robot systems

    Shiwu Zhang is currently a Professor in the Department of Precision Machinery and Precision Instrumentation at the University of Science and Technology of China (USTC). He received his B.E. degree in Mechanical and Electronic Engineering at USTC in 1997, and his Ph.D. degree in Precision Instrumentation and Machinery at USTC in 2003. He has been a Visiting Scholar in University of Wollongong, Australia in 2016 and in the Ohio State University, USA in 2012, respectively. His research interests include amphibious robots, soft robots, legged robots, liquid metal robots and rescue robots

  • Corresponding author: E-mail: weigao@ustc.edu.cn; E-mail: hychen5@hit.edu.cn; E-mail: swzhang@ustc.edu.cn
  • Received Date: 15 April 2022
  • Accepted Date: 25 May 2022
    • Available Online: 14 July 2022
    Abstract

  • Abstract

    To deal with emergencies and disasters without rescue workers being exposed to dangerous environments, this paper presents a mobile rescue robot, Earthshaker. As a combination of a tracked chassis and a six-degree-of-freedom robotic arm, as well as miscellaneous sensors and controllers, Earthshaker is capable of traversing diverse terrains and fulfilling dexterous manipulation. Specifically, Earthshaker has a unique swing arm—dozer blade structure that can help clear up cumbersome obstacles and stabilize the robot on stairs, a multimodal teleoperation system that can adapt to different transmission conditions, a depth camera-aided robotic arm and gripper that can realize semiautonomous manipulation and a LiDAR aided base that can achieve autonomous navigation in unknown areas. It was these special systems that supported Earthshaker to win the first Advanced Technology & Engineering Challenge (A-TEC) championships, standing out of 40 robots from the world and showing the efficacy of system integration and the advanced control philosophy behind it.

    Graphical abstract

    Overview of the rescue robot Earthshaker, the first place in the Advanced Technology & Engineering Challenge (A-TEC) championships.

    Abstract

    To deal with emergencies and disasters without rescue workers being exposed to dangerous environments, this paper presents a mobile rescue robot, Earthshaker. As a combination of a tracked chassis and a six-degree-of-freedom robotic arm, as well as miscellaneous sensors and controllers, Earthshaker is capable of traversing diverse terrains and fulfilling dexterous manipulation. Specifically, Earthshaker has a unique swing arm—dozer blade structure that can help clear up cumbersome obstacles and stabilize the robot on stairs, a multimodal teleoperation system that can adapt to different transmission conditions, a depth camera-aided robotic arm and gripper that can realize semiautonomous manipulation and a LiDAR aided base that can achieve autonomous navigation in unknown areas. It was these special systems that supported Earthshaker to win the first Advanced Technology & Engineering Challenge (A-TEC) championships, standing out of 40 robots from the world and showing the efficacy of system integration and the advanced control philosophy behind it.

    • Public Summary

    • Earthshaker, a mobile rescue robot that combines a tracked chassis, a robotic arm and gripper, and various sensors and controllers, has been created to deal with various emergencies and disasters.
    • Earthshaker’s multimodal teleoperation system can adapt to different transmission conditions, and it can achieve both semi-autonomous manipulation with its arm and gripper and autonomous navigation in unknown areas.
    • Earthshaker won the first A-TEC championships, standing out of 40 robots from the world, showing the efficacy of the system integration and the control philosophy behind it.

    FullText(HTML)
  • loading
    • References(43)
  • [1]
    de Greeff J, Mioch T, van Vught W, et al. Persistent robot-assisted disaster response. In: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. New York: ACM, 2018: 99–100.
    [2]
    Matsuno F, Sato N, Kon K, et al. Utilization of robot systems in disaster sites of the great eastern Japan earthquake. In: Yoshida K, Tadokoro S, editors. Field and Service Robotics. Berlin: Springer. 2013: 1–17.
    [3]
    Queralta J P, Taipalmaa J, Can Pullinen B, et al. Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision. IEEE Access, 2020, 8: 191617–191643. doi: 10.1109/access.2020.3030190
    [4]
    Nagatani K, Kiribayashi S, Okada Y, et al. Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots. Journal of Field Robotics, 2013, 30 (1): 44–63. doi: 10.1002/rob.21439
    [5]
    Delmerico J, Mintchev S, Giusti A, et al. The current state and future outlook of rescue robotics. Journal of Field Robotics, 2019, 36 (7): 1171–1191. doi: 10.1002/rob.21887
    [6]
    Atkeson C G, Babu B P W, Banerjee N, et al. No falls, no resets: Reliable humanoid behavior in the DARPA robotics challenge. In: 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids). Seoul, South Korea: IEEE, 2015: 623–630.
    [7]
    Feng S, Whitman E, Xinjilefu X, et al. Optimization-based full body control for the DARPA robotics challenge. Journal of Field Robotics, 2015, 32 (2): 293–312. doi: 10.1002/rob.21559
    [8]
    Spenko M, Buerger S, Iagnemma K. The DARPA robotics challenge finals: Humanoid robots to the rescue. Cham: Springer International Publishing, 2018.
    [9]
    Sheh R, Schwertfeger S, Visser A. 16 years of RoboCup rescue. KI―Künstliche Intelligenz, 2016, 30: 267–277. doi: 10.1007/s13218-016-0444-x
    [10]
    Karumanchi S, Edelberg K, Baldwin I, et al. Team RoboSimian: Semi-autonomous mobile manipulation at the 2015 DARPA robotics challenge finals. Journal of Field Robotics, 2017, 34 (2): 305–332. doi: 10.1002/rob.21676
    [11]
    Schwarz M, Beul M, Droeschel D, et al. DRC team NimbRo rescue: Perception and control for centaur-like mobile manipulation robot momaro. In: Spenko M, Buerger S, Iagnemma K, editors. The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue. Cham: Springer International Publishing, 2018: 145–190.
    [12]
    Stentz A, Herman H, Kelly A, et al. CHIMP, the CMU highly intelligent mobile platform. Journal of Field Robotics, 2015, 32 (2): 209–228. doi: 10.1002/rob.21569
    [13]
    Hutter M, Gehring C, Lauber A, et al. ANYmal—toward legged robots for harsh environments. Advanced Robotics, 2017, 31 (17): 918–931. doi: 10.1080/01691864.2017.1378591
    [14]
    Kruijff-Korbayová I, Freda L, Gianni M, et al. Deployment of ground and aerial robots in earthquake-struck Amatrice in Italy (brief report). In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). Lausanne, Switzerland: IEEE, 2016: 278–279.
    [15]
    Autonomous robot for gas and oil sites challenge. http://www.argos–challenge.com/en/challenge. Accessed January 10, 2022.
    [16]
    TAUROB—ARGOS winner. http://taurob.com/text–argos–gewinner/.Accessed January 10, 2022.
    [17]
    Endo D, Nagatani K. Assessment of a tracked vehicle’s ability to traverse stairs. ROBOMECH Journal, 2016, 3: 20. doi: 10.1186/s40648-016-0058-y
    [18]
    Yamauchi G, Nagatani K, Hashimoto T, et al. Slip-compensated odometry for tracked vehicle on loose and weak slope. ROBOMECH Journal, 2017, 4: 27. doi: 10.1186/s40648-017-0095-1
    [19]
    Rouček T, Pecka M, Čížek P, et al. System for multi-robotic exploration of underground environments CTU-CRAS-NORLAB in the DARPA Subterranean Challenge. 2021. https://arxiv.org/abs/2110.05911. Accessed January 10, 2022.
    [20]
    Agha A, Otsu K, Morrell B, et al. NeBula: Quest for robotic autonomy in challenging environments; TEAM CoSTAR at the DARPA subterranean challenge. 2021. https://arxiv.org/abs/2103.11470. Accessed January 10, 2022.
    [21]
    Chen X, Zhang H, Lu H, et al. Robust SLAM system based on monocular vision and LiDAR for robotic urban search and rescue. In: 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR). Shanghai, China: IEEE, 2017: 41–47.
    [22]
    Rouček T, Pecka M, Čížek P, et al. DARPA subterranean challenge: Multi-robotic exploration of underground environments. In: Mazal J, Fagiolini A, Vasik P,editors. Modelling and Simulation for Autonomous Systems. Cham: Springer International Publishing, 2020: 274–290.
    [23]
    Tranzatto M, Mascarich F, Bernreiter L, et al. CERBERUS: Autonomous legged and aerial robotic exploration in the tunnel and urban circuits of the DARPA subterranean challenge. 2022. https://arxiv.org/abs/2201.07067. Accessed January 20, 2022.
    [24]
    Schwarz M, Rodehutskors T, Droeschel D, et al. NimbRo rescue: Solving disaster-response tasks with the mobile manipulation robot momaro. Journal of Field Robotics, 2017, 34 (2): 400–425. doi: 10.1002/rob.21677
    [25]
    Li Y, Li M, Zhu H, et al. Development and applications of rescue robots for explosion accidents in coal mines. Journal of Field Robotics, 2020, 37 (3): 466–489. doi: 10.1002/rob.21920
    [26]
    Lösch R, Grehl S, Donner M, et al. Design of an autonomous robot for mapping, navigation, and manipulation in underground mines. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, Spain: IEEE, 2018: 1407–1412.
    [27]
    Szrek J, Zimroz R, Wodecki J, et al. Application of the infrared thermography and unmanned ground vehicle for rescue action support in underground mine—The AMICOS project. Remote Sensing, 2021, 13 (1): 69. doi: 10.3390/rs13010069
    [28]
    Bhatia R, Li L. Throughput optimization of wireless mesh networks with MIMO links. In: IEEE INFOCOM 2007 26th IEEE International Conference on Computer Communications. Anchorage, USA: IEEE, 2007: 2326–2330.
    [29]
    Karl P. LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1901, 2 (11): 559–572. doi: 10.1080/14786440109462720
    [30]
    Murray R M, Li Z, Sastry S S. A Mathematical Introduction to Robotic Manipulation. Boca Raton: CRC Press, 2017.
    [31]
    Kriegel H P, Kröger P, Sander J, et al. Density-based clustering. WIREs Data Mining and Knowledge Discovery, 2011, 1 (3): 231–240. doi: 10.1002/widm.30
    [32]
    Zhang J, Singh S. LOAM: lidar odometry and mapping in real-time. In: Robotics: Science and Systems Conference. Berkeley, USA: IEEE, 2014: 1–9.
    [33]
    Besl P J, McKay N D. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14 (2): 239–256. doi: 10.1109/34.121791
    [34]
    Forster C, Carlone L, Dellaert F, et al. IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. In: Robotics: Science and Systems, 2015.
    [35]
    Simanek J, Reinstein M, Kubelka V. Evaluation of the EKF-based estimation architectures for data fusion in mobile robots. IEEE/ASME Transactions on Mechatronics, 2015, 20 (2): 985–990. doi: 10.1109/tmech.2014.2311416
    [36]
    Bircher A, Kamel M, Alexis K, et al. Receding horizon “next-best-view” planner for 3D exploration. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). Stockholm, Sweden: IEEE, 2016: 1462–1468.
    [37]
    The ROS Wiki. Move_base. 2020. http://wiki.ros.org/move_base. Accessed January 20, 2022.
    [38]
    Frana P L, Misa T J. An interview with Edsger W. Dijkstra. Communications of the ACM, 2010, 53 (8): 41–47. doi: 10.1145/1787234.1787249
    [39]
    A-TEC Official Website. 2020. https://atec.leaguer.com.cn/index/index/championshipsjj. Accessed January 20, 2022.
    [40]
    de Petris P, Nguyen H, Dharmadhikari M, et al. RMF-owl: A collision-tolerant flying robot for autonomous subterranean exploration. 2022. https://arxiv.org/abs/2202.11055. Accessed March 2, 2022.
    [41]
    Hudson N, Talbot F, Cox M, et al. Heterogeneous ground and air platforms, homogeneous sensing: Team CSIRO Data61’s approach to the DARPA subterranean challenge. 2021. https://arxiv.org/abs/2104.09053. Accessed March 2, 2022.
    [42]
    Otsu K, Tepsuporn S, Thakker R, et al. Supervised autonomy for communication-degraded subterranean exploration by a robot team. In: 2020 IEEE Aerospace Conference. Big Sky, USA: IEEE, 2020: 1–9.
    [43]
    Ohradzansky M T, Rush E R, Riley D G, et al. Multi-agent autonomy: Advancements and challenges in subterranean exploration. 2021. https://arxiv.org/abs/2110.04390. Accessed March 12, 2022.
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    |
    Get Citation
    PDF
    XML

    Figure  1.  Overview of the rescue robot Earthshaker.

    Figure  2.  Demonstration of the swing arm—dozer blade structure. The structure can be folded or extended based on needs.

    Figure  3.  Electrical schematics of Earthshaker.

    Figure  4.  Flow charts of the control algorithms for Earthshaker.

    Figure  5.  Overview of the sessions of A-TEC championships.

    Figure  6.  Snapshots of Earthshaker in Session 1. (a)–(c) Earthshaker was passing through a pool filled with water of 500 mm in depth. (d) Earthshaker was traversing muddy terrains. (e) Earthshaker was crossing a trench of 600 mm in width.

    Figure  7.  Snapshots of Earthshaker in Session 2 & 3. (a)–(b) Earthshaker was clearing a light obstacle on the left and a heavy one on the right. (c)–(e) Earthshaker was opening a unifold door with a spherical door handle. (f)–(h) Earthshaker was climbing up and down the stairs.

    Figure  8.  The map built for the maze from the competition. The red slim line represents the path that the robot followed. The gray area indicates the accessible part of the map, while the cyan areas with dark red boundaries indicate the inaccessible parts. The three sub figures are the built maps, in turn, (a) at the beginning, (b) in the middle, and (c) at the end of the autonomous navigation through the maze.

    Figure  9.  Snapshots of Earthshaker in Session 5. (a) The infrared thermal image of the smoky environment. (b)–(e) The scene and the strategy used to rescue the dummy.

    [1]
    de Greeff J, Mioch T, van Vught W, et al. Persistent robot-assisted disaster response. In: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. New York: ACM, 2018: 99–100.
    [2]
    Matsuno F, Sato N, Kon K, et al. Utilization of robot systems in disaster sites of the great eastern Japan earthquake. In: Yoshida K, Tadokoro S, editors. Field and Service Robotics. Berlin: Springer. 2013: 1–17.
    [3]
    Queralta J P, Taipalmaa J, Can Pullinen B, et al. Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision. IEEE Access, 2020, 8: 191617–191643. doi: 10.1109/access.2020.3030190
    [4]
    Nagatani K, Kiribayashi S, Okada Y, et al. Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots. Journal of Field Robotics, 2013, 30 (1): 44–63. doi: 10.1002/rob.21439
    [5]
    Delmerico J, Mintchev S, Giusti A, et al. The current state and future outlook of rescue robotics. Journal of Field Robotics, 2019, 36 (7): 1171–1191. doi: 10.1002/rob.21887
    [6]
    Atkeson C G, Babu B P W, Banerjee N, et al. No falls, no resets: Reliable humanoid behavior in the DARPA robotics challenge. In: 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids). Seoul, South Korea: IEEE, 2015: 623–630.
    [7]
    Feng S, Whitman E, Xinjilefu X, et al. Optimization-based full body control for the DARPA robotics challenge. Journal of Field Robotics, 2015, 32 (2): 293–312. doi: 10.1002/rob.21559
    [8]
    Spenko M, Buerger S, Iagnemma K. The DARPA robotics challenge finals: Humanoid robots to the rescue. Cham: Springer International Publishing, 2018.
    [9]
    Sheh R, Schwertfeger S, Visser A. 16 years of RoboCup rescue. KI―Künstliche Intelligenz, 2016, 30: 267–277. doi: 10.1007/s13218-016-0444-x
    [10]
    Karumanchi S, Edelberg K, Baldwin I, et al. Team RoboSimian: Semi-autonomous mobile manipulation at the 2015 DARPA robotics challenge finals. Journal of Field Robotics, 2017, 34 (2): 305–332. doi: 10.1002/rob.21676
    [11]
    Schwarz M, Beul M, Droeschel D, et al. DRC team NimbRo rescue: Perception and control for centaur-like mobile manipulation robot momaro. In: Spenko M, Buerger S, Iagnemma K, editors. The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue. Cham: Springer International Publishing, 2018: 145–190.
    [12]
    Stentz A, Herman H, Kelly A, et al. CHIMP, the CMU highly intelligent mobile platform. Journal of Field Robotics, 2015, 32 (2): 209–228. doi: 10.1002/rob.21569
    [13]
    Hutter M, Gehring C, Lauber A, et al. ANYmal—toward legged robots for harsh environments. Advanced Robotics, 2017, 31 (17): 918–931. doi: 10.1080/01691864.2017.1378591
    [14]
    Kruijff-Korbayová I, Freda L, Gianni M, et al. Deployment of ground and aerial robots in earthquake-struck Amatrice in Italy (brief report). In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). Lausanne, Switzerland: IEEE, 2016: 278–279.
    [15]
    Autonomous robot for gas and oil sites challenge. http://www.argos–challenge.com/en/challenge. Accessed January 10, 2022.
    [16]
    TAUROB—ARGOS winner. http://taurob.com/text–argos–gewinner/.Accessed January 10, 2022.
    [17]
    Endo D, Nagatani K. Assessment of a tracked vehicle’s ability to traverse stairs. ROBOMECH Journal, 2016, 3: 20. doi: 10.1186/s40648-016-0058-y
    [18]
    Yamauchi G, Nagatani K, Hashimoto T, et al. Slip-compensated odometry for tracked vehicle on loose and weak slope. ROBOMECH Journal, 2017, 4: 27. doi: 10.1186/s40648-017-0095-1
    [19]
    Rouček T, Pecka M, Čížek P, et al. System for multi-robotic exploration of underground environments CTU-CRAS-NORLAB in the DARPA Subterranean Challenge. 2021. https://arxiv.org/abs/2110.05911. Accessed January 10, 2022.
    [20]
    Agha A, Otsu K, Morrell B, et al. NeBula: Quest for robotic autonomy in challenging environments; TEAM CoSTAR at the DARPA subterranean challenge. 2021. https://arxiv.org/abs/2103.11470. Accessed January 10, 2022.
    [21]
    Chen X, Zhang H, Lu H, et al. Robust SLAM system based on monocular vision and LiDAR for robotic urban search and rescue. In: 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR). Shanghai, China: IEEE, 2017: 41–47.
    [22]
    Rouček T, Pecka M, Čížek P, et al. DARPA subterranean challenge: Multi-robotic exploration of underground environments. In: Mazal J, Fagiolini A, Vasik P,editors. Modelling and Simulation for Autonomous Systems. Cham: Springer International Publishing, 2020: 274–290.
    [23]
    Tranzatto M, Mascarich F, Bernreiter L, et al. CERBERUS: Autonomous legged and aerial robotic exploration in the tunnel and urban circuits of the DARPA subterranean challenge. 2022. https://arxiv.org/abs/2201.07067. Accessed January 20, 2022.
    [24]
    Schwarz M, Rodehutskors T, Droeschel D, et al. NimbRo rescue: Solving disaster-response tasks with the mobile manipulation robot momaro. Journal of Field Robotics, 2017, 34 (2): 400–425. doi: 10.1002/rob.21677
    [25]
    Li Y, Li M, Zhu H, et al. Development and applications of rescue robots for explosion accidents in coal mines. Journal of Field Robotics, 2020, 37 (3): 466–489. doi: 10.1002/rob.21920
    [26]
    Lösch R, Grehl S, Donner M, et al. Design of an autonomous robot for mapping, navigation, and manipulation in underground mines. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, Spain: IEEE, 2018: 1407–1412.
    [27]
    Szrek J, Zimroz R, Wodecki J, et al. Application of the infrared thermography and unmanned ground vehicle for rescue action support in underground mine—The AMICOS project. Remote Sensing, 2021, 13 (1): 69. doi: 10.3390/rs13010069
    [28]
    Bhatia R, Li L. Throughput optimization of wireless mesh networks with MIMO links. In: IEEE INFOCOM 2007 26th IEEE International Conference on Computer Communications. Anchorage, USA: IEEE, 2007: 2326–2330.
    [29]
    Karl P. LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1901, 2 (11): 559–572. doi: 10.1080/14786440109462720
    [30]
    Murray R M, Li Z, Sastry S S. A Mathematical Introduction to Robotic Manipulation. Boca Raton: CRC Press, 2017.
    [31]
    Kriegel H P, Kröger P, Sander J, et al. Density-based clustering. WIREs Data Mining and Knowledge Discovery, 2011, 1 (3): 231–240. doi: 10.1002/widm.30
    [32]
    Zhang J, Singh S. LOAM: lidar odometry and mapping in real-time. In: Robotics: Science and Systems Conference. Berkeley, USA: IEEE, 2014: 1–9.
    [33]
    Besl P J, McKay N D. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14 (2): 239–256. doi: 10.1109/34.121791
    [34]
    Forster C, Carlone L, Dellaert F, et al. IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. In: Robotics: Science and Systems, 2015.
    [35]
    Simanek J, Reinstein M, Kubelka V. Evaluation of the EKF-based estimation architectures for data fusion in mobile robots. IEEE/ASME Transactions on Mechatronics, 2015, 20 (2): 985–990. doi: 10.1109/tmech.2014.2311416
    [36]
    Bircher A, Kamel M, Alexis K, et al. Receding horizon “next-best-view” planner for 3D exploration. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). Stockholm, Sweden: IEEE, 2016: 1462–1468.
    [37]
    The ROS Wiki. Move_base. 2020. http://wiki.ros.org/move_base. Accessed January 20, 2022.
    [38]
    Frana P L, Misa T J. An interview with Edsger W. Dijkstra. Communications of the ACM, 2010, 53 (8): 41–47. doi: 10.1145/1787234.1787249
    [39]
    A-TEC Official Website. 2020. https://atec.leaguer.com.cn/index/index/championshipsjj. Accessed January 20, 2022.
    [40]
    de Petris P, Nguyen H, Dharmadhikari M, et al. RMF-owl: A collision-tolerant flying robot for autonomous subterranean exploration. 2022. https://arxiv.org/abs/2202.11055. Accessed March 2, 2022.
    [41]
    Hudson N, Talbot F, Cox M, et al. Heterogeneous ground and air platforms, homogeneous sensing: Team CSIRO Data61’s approach to the DARPA subterranean challenge. 2021. https://arxiv.org/abs/2104.09053. Accessed March 2, 2022.
    [42]
    Otsu K, Tepsuporn S, Thakker R, et al. Supervised autonomy for communication-degraded subterranean exploration by a robot team. In: 2020 IEEE Aerospace Conference. Big Sky, USA: IEEE, 2020: 1–9.
    [43]
    Ohradzansky M T, Rush E R, Riley D G, et al. Multi-agent autonomy: Advancements and challenges in subterranean exploration. 2021. https://arxiv.org/abs/2110.04390. Accessed March 12, 2022.
    Recommended articles
    TrendMD
    Volume 53 Issue 1 page: 4
    Cover

    Article Metrics

    Article views (1582) PDF downloads(2705)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return