广东工业大学 计算机学院,广东,广州,510006
刘瑞雪(1994–),女,硕士研究生,主要研究方向为智能机器人和人工智能
曾碧(1963–),女,教授,博士,主要研究方向为智能机器人和智能信息处理,E-mail:zb9215@gdut.edu.cn
网络首发:2020-09-17,
纸质出版:2020
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刘瑞雪, 曾碧, 汪明慧, 卢智亮. 一种基于高效边界探索的机器人自主建图方法[J]. 广东工业大学学报, 2020, 37(5): 38-45.
Liu Rui-xue, Zeng Bi, Wang Ming-hui, et al. An Autonomous Mapping Method for Robot Based on Efficient Frontier Exploration[J]. 2020, 37(5): 38-45.
刘瑞雪, 曾碧, 汪明慧, 卢智亮. 一种基于高效边界探索的机器人自主建图方法[J]. 广东工业大学学报, 2020, 37(5): 38-45. DOI: 10.12052/gdutxb.200030.
Liu Rui-xue, Zeng Bi, Wang Ming-hui, et al. An Autonomous Mapping Method for Robot Based on Efficient Frontier Exploration[J]. 2020, 37(5): 38-45. DOI: 10.12052/gdutxb.200030.
针对目前自主移动机器人对室内未知环境的自主探索建图效率低、通用性差以及由于探索区域狭窄导致地图构建不完整的问题
提出一种基于曲线拟合和目标探索点邻域规划的边界探索自主建图方法。该方法对已构建的初始地图边界以曲线拟合方式筛选安全目标探索区
并针对机器人不可达目标探索区
采取滑动窗口邻域规划方法建立新目标探索点
引导机器人自主导航至该目标探索点
同时利用同步定位与建图技术
完成机器人对未知环境的自主探索和构图。实验表明
与现有方法比较
本方法能够以较少的探索次数、更短的探索路径和更高的探索效率完成对未知室内复杂场景的自主探索建图。
An improved autonomous frontier exploration method is presented to solve the problems of autonomous mobile robot autonomously exploring indoor unknown environments with low efficiency
poor versatility
and incomplete map construction due to the narrow exploration area. The curve fitting method is used to screen the safety target exploration area for the constructed initial map frontier and for the robot unreachable target exploration area
adopting a sliding window neighborhood planning method to establish a new target exploration point
guiding the robot to autonomously navigate to the target exploration point
while using simultaneous localization and mapping technology to complete the robot's autonomous exploration and mapping of unknown environments. The experimental results show that the method can better solve the problems of narrow target area and unreachable target points in the traditional algorithm. And it can complete autonomous exploration and mapping of unknown indoor complex scenes with fewer exploration times
shorter exploration paths and higher exploration efficiency.
GAO F, LI Y R. Analysis of patents of autonomous mobile service robotic technology [J]. China Invention and Patent, 2019, 16(3): 52-59.
OH S, HAHN M, KIM J. Simultaneous localization and mapping for mobile robots in dynamic environments[C]//International Conference on Information Science and Applications. Pattaya: IEEE, 2013: 1-4.
ZHU F L, ZENG B, CAO J. Parallel optimization and implementation of SLAM algorithm based on particle filter [J]. Journal of Guangdong University of Technology, 2017, 34(2): 92-96.
BAILEY T, DURRANT-WHYTE H. Simultaneous localization and mapping (SLAM): part Ⅱ [J]. IEEE Robotics Automation Magazine, 2006, 13(3): 108-117.
陈赢峰. 大规模复杂场景下室内服务机器人导航的研究[D]. 合肥: 中国科学技术大学, 2017.
CHEN M J, LIN W, ZENG B. Path planning for robot autonomous map building based on rolling window [J]. Computer Engineering, 2017, 43(2): 286-292.
KHAWALDAH M A, NÜCHTER A. Enhanced frontier-based exploration for indoor environment with multiple robots [J]. Advanced Robotics, 2015, 29(10): 657-669.
JADIDI M G, MIRO J V, DISSANAYAKE G. Gaussian processes autonomous mapping and exploration for range-sensing mobile robots [J]. Autonomous Robots, 2018, 42(2): 273-290.
YAMAUCHI B. A frontier-based approach for autonomous exploration[C]//Computational Intelligence in Robotics and Automation, IEEE International Symposium. Washington, DC: IEEE Computer Society, 1997: 146-151.
HOLZ D, BASILICO N, AMIGONI F, et al. Evaluating the efficiency of frontier-based exploration strategies[C]//41st International Symposium on Robotice and 6th German Conference on Robotics. Munich: VDE, 2010: 36-43.
VALENCIA R, VALLS MIRO J. DISSANAYAKE G, et al. Active pose SLAM[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura: IEEE, 2012: 1885-1891.
VALLVE J, ANDRADE-CETTO J. Potential information fields for mobile robot exploration[C]//Robotics and Autonomous Systems. [S.l.]: Elsevier. 2015: 68-79.
GAO H Y, DENG G Q, ZHANG L, et al . Unknown region exploration method based on frontier-based boundary exploration and searching-tree [J ] . Journal of Computer Applications, 2017, 37(z2): 120-126.
LI X Z, QIU H, JIA S M, et al . Mobile robot autonomous exploration based on dynamically simplified hybrid map [J ] . Control and Decision, 2017, 32(5): 817-822.
MORAVEC H, ELFES A. High Resolution maps from wide angle sonar[C]//IEEE International Conference on Robotics Automation. St Louis: IEEE, 1985: 116-121.
LIU L M, WU M L. Least square curve fitting based on matlab [J]. Journal of Fujian Computer, 2019, 35(8): 9-12.
SANTANA A, AIRES K, VERAS R, et al . An approach for 2d visual occupancy grid map using monocular vision [J ] . Electronic Notes in Theoretical Computer Science, 2004, 281: 175-191.
汤晓. 基于激光测距仪的移动机器人同时定位和地图创建[D]. 济南: 山东大学, 2007.
GRISETTI G, STACHNISS C, BURGARD W. Improved techniques for grid mapping with rao-blackwellized particle filters [J]. IEEE Transactions on Robotics, 2007, 23(1): 34-46.
LIANG J J, ZENG B, HE Y L. Research on path planning algorithm for cleaning robot based on improved potential field grid method [J]. Journal of Guangdong University of Technology, 2016, 33(4): 30-36.
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