[1]GAO Yu,HUO Jing,LI Wenbin,et al.Object goal navigation based on path planning characteristics[J].CAAI Transactions on Intelligent Systems,2024,19(1):217-227.[doi:10.11992/tis.202309001]
Copy

Object goal navigation based on path planning characteristics

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 1 Page number: 217-227 Column: 人工智能院长论坛 Public date: 2024-01-05
Title:
Object goal navigation based on path planning characteristics
Author(s):
GAO Yu1;  HUO Jing1;  LI Wenbin1;  WU Jing2;  LAI Yukun2;  GAO Yang1
1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China;
2. School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3XQ , England
Keywords:
artificial intelligence; visual navigation; object goal navigation; semantic perception; semantic exploration; path planning; machine learning; semantic map
CLC:
TP399
DOI:
10.11992/tis.202309001
Abstract:
To solve the problems of low exploration efficiency and imprecise depth in object goal navigation, this article constructs a framework to solve object goal navigation. Depth map edge processing and map error correction mechanisms were introduced in the semantic map construction module; a coverage maximization algorithm was introduced in the exploration module; alternative point mechanisms was introduced in the path planning module. This article conducted experiments in a 3D simulation environment. The experimental results show that the new solution proposed in this article significantly improves the performance of object goal navigation. In addition, the method proposed in this article was successfully applied to quadruped robots, thereby verifying its generalization in real-world scenarios.
References:
[1] ANDERSON P, CHANG A, CHAPLOT D S, et al. On evaluation of embodied navigation agents[EB/OL]. (2018?01?18)[2023?09?01]. https://arxiv.org/abs/1807.06757.pdf.
[2] 王作为, 徐征, 张汝波, 等. 记忆神经网络在机器人导航领域的应用与研究进展[J]. 智能系统学报, 2020, 15(5): 835–846
WANG Zuowei, XU Zheng, ZHANG Rubo, et al. Research progress and application of memory neural network in robot navigation[J]. CAAI transactions on intelligent systems, 2020, 15(5): 835–846
[3] YADAV K, RAMRAKHYA R, RAMAKRISHNAN S K, et al. Habitat-matterport 3D semantics dataset[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 4927?4936.
[4] RAMAKRISHNAN S K, GOKASLAN A, WIJMANS E, et al. Habitat-matterport 3D dataset (HM3D): 1000 large-scale 3D environments for embodied AI[EB/OL]. (2021?09?16)[2023?09?01]. https://arxiv.org/abs/2109.08238.pdf.
[5] ZHANG Ji, SINGH S. LOAM: lidar odometry and mapping in real-time[C]//Robotics: Science and Systems Conference.Calidornia: Science and Systems Foundation, 2014: 1?9.
[6] VIZZO I, GUADAGNINO T, MERSCH B, et al. KISS-ICP: in defense of point-to-point ICP–simple, accurate, and robust registration if done the right way[J]. IEEE robotics and automation letters, 2023, 8(2): 1029–1036.
[7] 沈斯杰, 田昕, 魏国亮, 等. 基于2D激光雷达的SLAM算法研究综述[J]. 计算机技术与发展, 2022, 32(1): 13–18,46
SHEN Sijie, TIAN Xin, WEI Guoliang, et al. Review of SLAM algorithm based on 2D lidar[J]. Computer technology and development, 2022, 32(1): 13–18,46
[8] MUR-ARTAL R, TARDóS J D. ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras[J]. IEEE transactions on robotics, 2017, 33(5): 1255–1262.
[9] CVI?I? I, ?ESI? J, MARKOVI? I, et al. SOFT-SLAM: Computationally efficient stereo visual simultaneous localization and mapping for autonomous unmanned aerial vehicles[J]. Journal of field robotics, 2018, 35(4): 578–595.
[10] 张毅, 陈起, 罗元. 室内环境下移动机器人三维视觉SLAM[J]. 智能系统学报, 2015, 10(4): 615–619
ZHANG Yi, CHEN Qi, LUO Yuan. Three dimensional visual SLAM for mobile robots in indoor environments[J]. CAAI transactions on intelligent systems, 2015, 10(4): 615–619
[11] 王霞, 左一凡. 视觉slam研究进展[J]. 智能系统学报, 2020, 15(5): 825–834
WANG Xia, ZUO Yifan. Advances in visual SLAM research[J]. CAAI transactions on intelligent systems, 2020, 15(5): 825–834
[12] HART P E, NILSSON N J, RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE transactions on systems science and cybernetics, 1968, 4(2): 100–107.
[13] KAVRAKI L E, SVESTKA P, LATOMBE J C, et al. Probabilistic roadmaps for path planning in high-dimensional configuration spaces[J]. IEEE transactions on robotics and automation, 1996, 12(4): 566–580.
[14] 李娟, 张韵, 陈涛. 改进RRT算法在未知三维环境下AUV目标搜索中的应用[J]. 智能系统学报, 2022, 17(2): 368–375
LI Juan, ZHANG Yun, CHEN Tao. Application of the improved RRT algorithm to AUV target search in an unknown 3D environment[J]. CAAI transactions on intelligent systems, 2022, 17(2): 368–375
[15] WARREN C W. Global path planning using artificial potential fields[C]//Proceedings, 1989 International Conference on Robotics and Automation. Piscataway: IEEE, 2002: 316?321.
[16] 朱少凯, 孟庆浩, 金晟, 等. 基于深度强化学习的室内视觉局部路径规划[J]. 智能系统学报, 2022, 17(5): 908–918
ZHU Shaokai, MENG Qinghao, JIN Sheng, et al. Indoor visual local path planning based on deep reinforcement learning[J]. CAAI transactions on intelligent systems, 2022, 17(5): 908–918
[17] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Playing atari with deep reinforcement learning[EB/OL]. (2013?12?19)[2023?09?01]. https://arxiv.org/abs/1312.5602.pdf.
[18] MNIH V, BADIA A P, MIRZA M, et al. Asynchronous methods for deep reinforcement learning[EB/OL]. (2016?01?16)[2023?09?01]. https://arxiv.org/abs/1602.01783.pdf.
[19] SCHULMAN J, WOLSKI F, DHARIWAL P, et al. Proximal policy optimization algorithms[EB/OL]. (2017?07?28)[2023?09?01]. https://arxiv.org/abs/1707.06347.pdf.
[20] LILLICRAP T P, HUNT J J, PRITZEL A, et al. Continuous control with deep reinforcement learning[EB/OL]. (2015?09?09)[2023?09?01]. https://arxiv.org/abs/1509.02971.pdf.
[21] 黄东晋, 蒋晨凤, 韩凯丽. 基于深度强化学习的三维路径规划算法[J]. 计算机工程与应用, 2020, 56(15): 30–36
HUANG Dongjin, JIANG Chenfeng, HAN Kaili. 3D path planning algorithm based on deep reinforcement learning[J]. Computer engineering and applications, 2020, 56(15): 30–36
[22] FAUST A, OSLUND K, RAMIREZ O, et al. PRM-RL: long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2018: 5113?5120.
[23] CHAPLOT D S, GANDHI D P, GUPTA A, et al. Object goal navigation using goal-oriented semantic exploration[J]. Advances in neural information processing systems, 2020, 33: 4247–4258.
[24] YANG Wei, WANG Xiaolong, FARHADI A, et al. Visual semantic navigation using scene priors[EB/OL]. (2018?10?15)[2023?09?01]. https://arxiv.org/abs/1810.06543.pdf.
[25] MAKSYMETS O, CARTILLIER V, GOKASLAN A, et al. THDA: treasure hunt data augmentation for semantic navigation[C]//2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2022: 15354?15363.
[26] MAYO B, HAZAN T, TAL A. Visual navigation with spatial attention[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2021: 16893?16902.
[27] CHAPLOT D S, GANDHI D, GUPTA S, et al. Learning to explore using active neural SLAM[EB/OL]. (2020?04?20)[2023?09?01]. https://arxiv.org/abs/2004.05155.pdf.
[28] HE Kaiming, GKIOXARI G, DOLL?R P, et al. Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision (ICCV). Piscataway: IEEE, 2017: 2980?2988.
[29] SETHIAN J A. A fast marching level set method for monotonically advancing fronts[J]. Proceedings of the national academy of sciences of the United States of America, 1996, 93(4): 1591–1595.
[30] WIJMANS E, KADIAN A, MORCOS A, et al. DD-PPO: learning near-perfect PointGoal navigators from 2.5 billion frames[EB/OL]. (2019?11?01)[2023?09?01]. https://arxiv.org/abs/1911.00357.pdf.
[31] RAMAKRISHNAN S K, JAYARAMAN D, GRAUMAN K. An exploration of embodied visual exploration[J]. International journal of computer vision, 2021, 129(5): 1–34.
[32] GEORGAKIS G, BUCHER B, SCHMECKPEPER K, et al. Learning to map for active semantic goal navigation[EB/OL]. (2021?06?29)[2023?09?01]. https://arxiv.org/abs/2106.15648.pdf.
[33] ZHU Minzhao, ZHAO Binglei, KONG Tao. Navigating to objects in unseen environments by distance prediction[C]//2022 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE, 2022: 10571?10578.
[34] LIANG Yiqing, CHEN Boyuan, SONG Shuran. SSCNav: confidence-aware semantic scene completion for visual semantic navigation[C]//2021 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2021: 13194?13200.
[35] SAVVA M, KADIAN A, MAKSYMETS O, et al. Habitat: a platform for embodied AI research[C]//2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2020: 9338?9346.
[36] SZOT A, CLEGG A, UNDERSANDER E, et al. Habitat 2.0: training home assistants to rearrange their habitat[EB/OL]. (2021?10?06)[2023?09?01]. https://arxiv.org/abs/2106.14405.pdf.
[37] YAMAUCHI B. A frontier-based approach for autonomous exploration[C]//Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA’97. ’Towards New Computational Principles for Robotics and Automation’. Piscataway: IEEE, 2002: 146?151.
[38] RAMRAKHYA R, UNDERSANDER E, BATRA D, et al. Habitat-web: learning embodied object-search strategies from human demonstrations at scale[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 5163?5173.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems