Semi-Autonomous Navigation Based on Local Semantic Map for
Mobile Robot

doi:10.1007/s12204-023-2678-8

Abstract

Abstract: Mobile robots represented by smart wheelchairs can assist elderly people with mobility difficulties. This paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots, which can assist users to implement accurate navigation (e.g., docking) in the environment without prior maps. In order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms, this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic goals. At last, comparative experiments were carried out in the real environment. Results show that our method is superior in terms of safety, comfort and docking accuracy.

Key words: semi-autonomous navigation, mobile robot, semantic map, voice interaction

摘要： 以智能轮椅为代表的移动机器人可以帮助行动不便的老年人。本文提出了基于局部语义地图的移动机器人半自主导航系统，该系统可以帮助用户在无先验地图的环境中执行精确导航（例如对接）。为了克服传统局部路径规划算法在对接时出现反复震荡的问题，本文采取模式切换方法，在靠近语义目标的时候系统切换为反馈控制完成对接。最后，在真实的环境中进行了对比实验。结果表明：我们的方法在安全，舒适和对接精度上具有优越性。

关键词: 半自主导航，移动机器人，语义地图，语音交互

CLC Number:

ZHAO Yanfei^1，2，3(赵艳飞), XIAO Peng⁴ (肖鹏), WANG Jingchuan^1，2，3* (王景川), GUO Rui^4*(郭锐）. Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 27-33.

References

[1] LEAMAN J, LA H M. A comprehensive review of smart wheelchairs: Past, present, and future [J]. IEEE Transactions on Human-Machine Systems, 2017, 47(4): 486-499.
[2] CASADO F E, DEMIRIS Y. Federated learning from demonstration for active assistance to smart wheelchair users [C]//2022 IEEE/RSJ International Conference on Intelligent Robots and Systems. Kyoto: IEEE, 2022: 9326-9331.
[3] MAZO M. An integral system for assisted mobility [automated wheelchair [J]. IEEE Robotics & Automation Magazine, 2001, 8(1): 46-56.
[4] PRASSLER E, SCHOLZ J, FIORINI P. A robotics wheelchair for crowded public environment [J]. IEEE Robotics & Automation Magazine, 2001, 8(1): 38-45.
[5] MATSUMOTO O, KOMORIYA K, HATASE T, et al. Intelligent wheelchair robot “TAO aicle” [M]//Service robot applications. Rijeka: InTech, 2008: 55-70.
[6] YOKOZUKA M, SUZUKI Y, HASHIMOTO N, et al. Robotic wheelchair with autonomous traveling capability for transportation assistance in an urban environment [C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. VilamouraAlgarve: IEEE, 2012: 2234-2241.
[7] JOSHI R P, TARAPURE J P, SHIBATA T. Electric wheelchair-humanoid robot collaboration for clothing assistance of the elderly [C]//2020 13th International Conference on Human System Interaction. Tokyo: IEEE, 2020: 300-306.
[8] ZHANG R, LI Y Q, YAN Y Y, et al. Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(1): 128-139.
[9] ROSINOL A, VIOLETTE A, ABATE M, et al. Kimera: From SLAM to spatial perception with 3D dynamic scene graphs [J]. The International Journal of Robotics Research, 2021, 40(12/13/14): 1510-1546.
[10] WANG Z L, TIAN G H. Hybrid offline and online task planning for service robot using object-level semantic map and probabilistic inference [J]. Information Sciences, 2022, 593: 78-98.
[11] WEI Z X, CHEN W D, WANG J C, et al. Semantic mapping for safe and comfortable navigation of a brain-controlled wheelchair [M]//Intelligent robotics and applications. Berlin: Springer, 2013: 307-317.
[12] WEI Z X, CHEN W D, WANG J C, et al. Semantic topological map-based smart wheelchair navigation system for low throughput interface [M]//Intelligent autonomous systems 13. Cham: Springer, 2016: 109-120.
[13] WEI Z X, CHEN W D, WANG J C. Semantic mapping for smart wheelchairs using RGB-D camera [J]. Journal of Medical Imaging and Health Informatics, 2013, 3(1): 94-100.
[14] LU D V, HERSHBERGER D, SMART W D. Layered costmaps for context-sensitive navigation [C]//2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago: IEEE, 2014: 709-715.
[15] GRINVALD M, FURRER F, NOVKOVIC T, et al. Volumetric instance-aware semantic mapping and 3D object discovery [J]. IEEE Robotics and Automation Letters, 2019, 4(3): 3037-3044.
[16] RODOMAGOULAKIS I, KARDARIS N, PITSIKALIS V, et al. Multimodal human action recognition in assistive human-robot interaction[C]//2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Shanghai: IEEE, 2016: 2702-2706.
[17] ZHANG J, SINGH S. LOAM: Lidar odometry and mapping in real-time [C]/ Robotics: Science and Systems X. Berkeley: UC Berkeley, 2014: 1-9.
[18] REDMON J, FARHADI A. YOLOv3: An incremental improvement [DB/OL]. (2018-04-08) [2023-03-16]. https://arxiv.org/abs/1804.02767
[19] LECROSNIER L, KHEMMAR R, RAGOT N, et al. Deep learning-based object detection, localisation and tracking for smart wheelchair healthcare mobility [J]. International Journal of Environmental Research and Public Health, 2020, 18(1): 91.
[20] GROSSBERG M D, NAYAR S K. A general imaging model and a method for finding its parameters [C]//Proceedings Eighth IEEE International Conference on Computer Vision. Vancouver: IEEE, 2001: 108-115.
[21] FURRER F, NOVKOVIC T, FEHR M, et al. Incremental object database: Building 3D models from multiple partial observations [C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems. Madrid: IEEE, 2018: 6835-6842.
[22] ROESMANN C, FEITEN W, WOESCH T, et al. Trajectory modification considering dynamic constraints of autonomous robots [C]//ROBOTIK 2012; 7th German Conference on Robotics. Munich: VDE, 2012: 1-6.
[23] SIEGWART R, NOURBAKHSH I R. Introduction to autonomous mobile robots [M]. Cambridge: MIT Press, 2004.
[24] QUIGLEY M, CONLEY K, GERKEV B, et al. ROS: an open-source robot operating system [C]//ICRA Workshop on Open Source Software. Kobe: IEEE, 2009: 1-6.
[25] LI Q N, CHEN W D, WANG J C. Dynamic shared control for human-wheelchair cooperation [C]//2011 IEEE International Conference on Robotics and Automation. Shanghai: IEEE, 2011: 4278-4283.

Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

基于局部语义地图的移动机器人半自主导航

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 7

Recommended Articles 0

Metrics

Comments

[1]	LI Shuyi (李舒逸), LI Minzhe (李旻哲), JING Zhongliang^∗ (敬忠良). Multi-Agent Path Planning Method Based on Improved Deep Q-Network in Dynamic Environments [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 601-612.
[2]	ZHAO Yingce(赵英策), ZHANG Guanghao(张广浩), XING Zhengyu(邢正宇), LI Jianxun(李建勋). Hierarchical Reinforcement Learning Adversarial Algorithm Against Opponent with Fixed Offensive Strategy [J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 471-479.
[3]	CAO Bingquan1,2,3 (曹炳全), HE Yuesheng1,2,3∗ (贺越生), ZHUANG Hanyang4 (庄瀚洋), YANG Ming1,2,3 (杨明). Infrastructure-Based Vehicle Localization System for Indoor Parking Lot Using RGB-D Cameras [J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 61-69.
[4]	MAO Tianyang (茅天阳), ZHAO Wentao (赵文韬), WANG Jingchuan∗ (王景川), CHEN Weidong (陈卫东). Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration [J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 70-76.
[5]	LÜ Qibing (吕其兵), LIU Tianyuan (刘天元), ZHANG Rong (张荣), JIANG Yanan (江亚南), XIAO Lei (肖雷), BAO Jingsong∗ (鲍劲松). Generation Approach of Human-Robot Cooperative Assembly Strategy Based on Transfer Learning [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(5): 602-613.
[6]	LIU Dasheng∗ (刘大生), YAN Guozheng (颜国正). Biomechanical Analysis of a Radial Expansion Mechanism of Intestinal Robot Coupling with Hyperelastic Intestinal Wall [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 552-560.
[7]	LI Yanbiao∗ (李研彪), CHEN Ke (陈科), SUN Peng (孙鹏), WANG Zesheng (王泽胜). Dynamic Modeling and Performance Evaluation of a Novel Humanoid Ankle Joint [J]. J Shanghai Jiaotong Univ Sci, 2022, 27(4): 570-578.