294 results on '"Autonomous exploration"'
Search Results
2. 面向RatSLAM的自主探索算法.
- Author
-
武 皓, 武 彤, 张志慧, and 唐凤珍
- Abstract
Copyright of Journal of Shenyang Ligong University is the property of Journal of Shenyang Ligong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
- Full Text
- View/download PDF
3. 基于均匀扫描和专注引导策略的自主探索算法.
- Author
-
申伟霖, 陈荟慧, 关柏良, 王爱国, and 杨健茂
- Subjects
- *
PRICE indexes , *PROBLEM solving , *ROBOTS , *ALGORITHMS , *EXPLORERS - Abstract
To solve the problem of difficulty for robots to quickly explore unknown environments with narrow entrances, as well as the decrease in exploration efficiency caused by wandering between targets with close exploration benefits, this paper proposed a self-exploration algorithm USAGE (uniformly scanning and attentive guidance explorer), which included uniform scanning and attentive guidance strategy. USAGE used the uniform scanning method to detect boundary points, and clustered the boundary points to obtain the points to be explored, and finally determined the optimal exploration target through an attentive guidance strategy. This strategy introduced the steering cost evaluation index into the traditional evaluation function containing information gain and path cost, and constrained the execution of the exploration task according to the robot's state, and guided the robot to focus on exploration. Through building a simulation environment in the robot operating system for verification, the experimental results show that compared with the exploration algorithm based on rapidly-exploring random tree (RRT), USAGE occupies less system memory by more than 11.34%, reduces the exploration time and exploration path level by 26. 90% and 31.94% respectively, which improves autonomous exploration efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces.
- Author
-
Lin, Xiaohu, Yang, Xin, Yao, Wanqiang, Wang, Xiqi, Ma, Xiongwei, and Ma, Bolin
- Subjects
- *
OPTICAL radar , *LIDAR , *STANDARD deviations , *UNDERGROUND areas , *SUBWAY tunnels - Abstract
Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. 基于强化学习的机器人自主探索与物体感知算法.
- Author
-
吴关, 夏熙, 曹合智, and 刘利刚
- Abstract
Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
- Full Text
- View/download PDF
6. Implementation of an autonomous exploration system in unknown environments based on transfer learning.
- Author
-
Zhang, Feng, Lin, Rui, Liu, Lianghao, MingJun, Zhao, and Wu, Qichao
- Subjects
DEEP reinforcement learning ,REINFORCEMENT learning ,AUTONOMOUS robots ,ROBOT motion ,HEURISTIC - Abstract
In this article, we propose an autonomous exploration system based on transfer learning for target point exploration in unknown environments. The system generates a series of clustering points based on the local perceptual information and selects suitable local exploration points through a heuristic method to guide the robot towards the global target direction. This approach alleviates the problem of local optima to a significant extent. To reduce the time cost of deep reinforcement learning in the initial stages, we employ transfer learning by training a model capable of avoiding static obstacles in a simulated environment to accomplish local dynamic navigation tasks. By combining the locally learned dynamic navigation policy with global motion planning, we achieve autonomous exploration for the robot. During the fully autonomous navigation process, we record the robot's movement trajectory and the generated map. Experimental results demonstrate that compared to similar exploration methods, this approach exhibits advantages in complex dynamic environments, even with the sole use of two-dimensional laser, without the need for maps or excessive information. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. Combining spatial clustering and tour planning for efficient full area exploration.
- Author
-
Bao, Jiatong, Mamun, Sultan, Bao, Jiawei, Zhang, Wenbing, Yang, Yuequan, and Song, Aiguo
- Subjects
- *
TRAVELING salesman problem , *VIDEO coding , *REINFORCEMENT (Psychology) , *TOURS , *REINFORCEMENT learning , *MOBILE robots - Abstract
Autonomous exploration in unknown environments has become a critical capability of mobile robots. Many methods often suffer from problems such as exploration goal selection based solely on information gain and inefficient tour optimization. Recent reinforcement learning-based methods do not consider full area coverage and the performance of transferring learned policy to new environments cannot be guaranteed. To address these issues, a dual-stage exploration method has been proposed, which combines spatial clustering of possible exploration goals and Traveling Salesman Problem (TSP) based tour planning on both local and global scales, aiming for efficient full-area exploration in highly convoluted environments. Our method involves two stages: exploration and relocation. During the exploration stage, we introduce to generate local navigation goal candidates straight from clusters of all possible local exploration goals. The local navigation goal is determined through tour planning, utilizing the TSP framework. Moreover, during the relocation stage, we suggest clustering all possible global exploration goals and applying TSP-based tour planning to efficiently direct the robot toward previously detected but yet-to-be-explored areas. The proposed method is validated in various challenging simulated and real-world environments. Experimental results demonstrate its effectiveness and efficiency. Videos and code are available at https://github.com/JiatongBao/exploration. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
8. Bio-inspired mobile robot design and autonomous exploration strategy for underground special space
- Author
-
Wang, Minghao, Cong, Ming, Du, Yu, Zhong, Huageng, and Liu, Dong
- Published
- 2024
- Full Text
- View/download PDF
9. Autonomous Exploration Method of Unmanned Ground Vehicles Based on an Incremental B-Spline Probability Roadmap.
- Author
-
Feng, Xingyang, Cong, Hua, Zhang, Yu, Qiu, Mianhao, and Hu, Xuesong
- Subjects
- *
AUTONOMOUS vehicles , *REMOTELY piloted vehicles , *NONHOLONOMIC constraints , *TRAVELING salesman problem - Abstract
Autonomous exploration in unknown environments is a fundamental problem for the practical application of unmanned ground vehicles (UGVs). However, existing exploration methods face difficulties when directly applied to UGVs due to limited sensory coverage, conservative exploration strategies, inappropriate decision frequencies, and the non-holonomic constraints of wheeled vehicles. In this paper, we present IB-PRM, a hierarchical planning method that combines Incremental B-splines with a probabilistic roadmap, which can support rapid exploration by a UGV in complex unknown environments. We define a new frontier structure that includes both information-gain guidance and a B-spline curve segment with different arrival orientations to satisfy the non-holonomic constraint characteristics of UGVs. We construct and maintain local and global graphs to generate and store filtered frontiers. By jointly solving the Traveling Salesman Problem (TSP) using these frontiers, we obtain the optimal global path traversing feasible frontiers. Finally, we optimize the global path based on the Time Elastic Band (TEB) algorithm to obtain a smooth, continuous, and feasible local trajectory. We conducted comparative experiments with existing advanced exploration methods in simulation environments of different scenarios, and the experimental results demonstrate that our method can effectively improve the efficiency of UGV exploration. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
10. A Low-Cost 3D SLAM System Integration of Autonomous Exploration Based on Fast-ICP Enhanced LiDAR-Inertial Odometry.
- Author
-
Pang, Conglin, Zhou, Liqing, and Huang, Xianfeng
- Subjects
- *
ROBOTICS , *NAUTICAL charts , *NATURAL gas prospecting , *OPEN access publishing , *SYSTEMS design - Abstract
Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system's capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
11. Autonomous Exploration Path Planning Method for Unmanned Aerial Vehicle Based on Flight Safety Constraint
- Author
-
Xie, Yuying, Lyu, Pin, Lai, Jizhou, Xing, Li, Liu, Wei, Li, Zhimin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Li, Xiaoduo, editor, Song, Xun, editor, and Zhou, Yingjiang, editor
- Published
- 2024
- Full Text
- View/download PDF
12. Fast Autonomous Exploration with Sparse Topological Graphs in Large-Scale Environments
- Author
-
Wu, Jianbin, Jiang, Shuang, Wei, Changyun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor
- Published
- 2024
- Full Text
- View/download PDF
13. A Submodular-Based Autonomous Exploration for Multi-Room Indoor Scenes Reconstruction
- Author
-
Miao, Yongwei, Wang, Haipeng, Fan, Ran, Liu, Fuchang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor
- Published
- 2024
- Full Text
- View/download PDF
14. Autonomous exploration for radioactive sources localization based on radiation field reconstruction
- Author
-
Xulin Hu, Junling Wang, Jianwen Huo, Ying Zhou, Yunlei Guo, and Li Hu
- Subjects
Radioactive source localization ,Radiation field reconstruction ,Autonomous exploration ,Unmanned ground vehicles ,Gaussian process regression ,Nuclear engineering. Atomic power ,TK9001-9401 - Abstract
In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.
- Published
- 2024
- Full Text
- View/download PDF
15. A Multi-Robot Collaborative Exploration Method Based on Deep Reinforcement Learning and Knowledge Distillation
- Author
-
Rui Wang, Ming Lyu, and Jie Zhang
- Subjects
multi-robot ,autonomous exploration ,deep reinforcement learning ,Mathematics ,QA1-939 - Abstract
Multi-robot collaborative autonomous exploration in communication-constrained scenarios is essential in areas such as search and rescue. During the exploration process, the robot teams must minimize the occurrence of redundant scanning of the environment. To this end, we propose to view the robot team as an agent and obtain a policy network that can be centrally executed by training with an improved SAC deep reinforcement learning algorithm. In addition, we transform the obtained policy network into distributed networks that can be adapted to communication-constrained scenarios using knowledge distillation. Our proposed method offers an innovative solution to the decision-making problem for multiple robots. We conducted experiments on our proposed method within simulated environments. The experimental results show the adaptability of our proposed method to various sizes of environments and its superior performance compared to the current mainstream methods.
- Published
- 2025
- Full Text
- View/download PDF
16. 室内测绘机器人自主定位与三维建图研究.
- Author
-
周宏毅, 章国宝, and 朱宏伟
- Abstract
Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
- Full Text
- View/download PDF
17. A heuristic autonomous exploration method based on environmental information gain during quadrotor flight.
- Author
-
Zhang, Tong, Yu, Jiajie, Li, Jiaqi, and Pang, Minghui
- Subjects
HEURISTIC ,ACCESS to information ,AUTONOMOUS vehicles - Abstract
Autonomous exploration is a widely studied fundamental application in the field of quadrotor, which requires them to automatically explore unknown space to obtain complete information about the environment. The frontier-based method, one of the representative works on autonomous exploration, drives autonomous determination by the definition of frontier information so that complete information about the environment is available to the quadrotor. However, existing frontier-based methods are able to accomplish the task but still suffer from inefficient exploration, and how to improve the efficiency of autonomous exploration is the focus of research nowadays. Slow frontier generation affecting real-time viewpoint determination and insufficient determination methods affecting the quality of viewpoints are typical of these problems. Therefore, to overcome the aforementioned problems, this article proposes a two-level viewpoint determination method for frontier-based autonomous exploration. First, a sampling-based frontier detection method is presented for faster frontier generation, improving the immediacy of environmental representation compared to traditional traversal-based methods. Second, the access to environmental information during flight is considered for the first time, and an innovative heuristic evaluation function is designed to decide on high-quality viewpoint as the next local navigation target in each exploration iteration. Extensive benchmark and real-world tests are conducted to validate our method. The results confirm that our method optimizes the frontier search time by 85%, the exploration time by around 20%–30%, and the exploration path by 25%–35%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
18. PR-SLAM: Parallel Real-Time Dynamic SLAM Method Based on Semantic Segmentation
- Author
-
Hongyu Zhang, Jiansheng Peng, and Qing Yang
- Subjects
Semantic SLAM ,semantic segmentation ,real-time ,indoor dynamic scene ,autonomous exploration ,robustness and accuracy ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
SLAM (Simultaneous Localization and Mapping) is the core technology enabling autonomous exploration by mobile robots in unknown environments. While there have been numerous impressive SLAM systems developed, many of them are primarily based on the assumption of static environments, limiting their applicability in real-world settings. In order to enhance the robustness and accuracy of systems in dynamic real-world scenarios, we have introduced a parallelized real-time SLAM system called PR-SLAM, building upon the foundation of ORB-SLAM3. This algorithm introduces a dynamic probability update strategy within the semantic segmentation thread, completely decoupling the semantic segmentation thread from the tracking thread. Theoretically, the processing time per frame is solely dependent on the runtime of the tracking thread. Furthermore, we employ a geometric approach based on reprojection error to compensate for semantic gaps generated during semantic segmentation model inference. We have also designed a semantic optimization thread based on the dynamic probability of map points to optimize camera poses during semantic gaps. Finally, to reduce semantic gaps, we have performed lightweight modifications to SOLOV2. Comparative experiments were conducted against the state-of-the-art SLAM systems using the TUM dataset. The results indicate significant improvements in both accuracy and real-time performance for PR-SLAM. When compared to ORB-SLAM3, PR-SLAM achieved a remarkable 97.83% improvement in absolute trajectory accuracy and demonstrated an impressive 86.71% increase in runtime speed compared to DynaSLAM.
- Published
- 2024
- Full Text
- View/download PDF
19. Autonomous exploration through deep reinforcement learning
- Author
-
Yan, Xiangda, Huang, Jie, He, Keyan, Hong, Huajie, and Xu, Dasheng
- Published
- 2023
- Full Text
- View/download PDF
20. Efficient UAV Exploration for Large-Scale 3D Environments Using Low-Memory Map
- Author
-
Junlong Huang, Zhengping Fan, Zhewen Yan, Peiming Duan, Ruidong Mei, and Hui Cheng
- Subjects
unmanned aerial vehicles ,autonomous exploration ,path planning ,Motor vehicles. Aeronautics. Astronautics ,TL1-4050 - Abstract
Autonomous exploration of unknown environments is a challenging problem in robotic applications, especially in large-scale environments. As the size of the environment increases, the limited onboard resources of the robot hardly satisfy the memory overhead and computational requirements. As a result, it is challenging to respond quickly to the received sensor data, resulting in inefficient exploration planning. And it is difficult to comprehensively utilize the gathered environmental information for planning, leading to low-quality exploration paths. In this paper, a systematic framework tailored for unmanned aerial vehicles is proposed to autonomously explore large-scale unknown environments. To reduce memory consumption, a novel low-memory environmental representation is introduced that only maintains the information necessary for exploration. Moreover, a hierarchical exploration approach based on the proposed environmental representation is developed to allow for fast planning and efficient exploration. Extensive simulation tests demonstrate the superiority of the proposed method over current state-of-the-art methods in terms of memory consumption, computation time, and exploration efficiency. Furthermore, two real-world experiments conducted in different large-scale environments also validate the feasibility of our autonomous exploration system.
- Published
- 2024
- Full Text
- View/download PDF
21. EMExplorer: an episodic memory enhanced autonomous exploration strategy with Voronoi domain conversion and invalid action masking.
- Author
-
Chen, Bolei, Zhong, Ping, Cui, Yongzheng, Lu, Siyi, Liang, Yixiong, and Sheng, Yu
- Subjects
DEEP reinforcement learning ,EPISODIC memory ,MOBILE robots ,REINFORCEMENT learning ,VORONOI polygons ,ROBOTICS ,CONFIGURATION space ,SURGICAL robots ,SUCCESSIVE approximation analog-to-digital converters - Abstract
Autonomous exploration is a critical technology to realize robotic intelligence as it allows unsupervised preparation for future tasks and facilitates flexible deployment. In this paper, a novel Deep Reinforcement Learning (DRL) based autonomous exploration strategy is proposed to efficiently reduce the unknown area of the workspace and provide accurate 2D map construction for mobile robots. Different from existing human-designed exploration techniques that usually make strong assumptions about the scenarios and the tasks, we utilize a model-free method to directly learn an exploration strategy through trial-and-error interactions with complex environments. To be specific, the Generalized Voronoi Diagram (GVD) is first utilized for domain conversion to obtain a high-dimensional Topological Environmental Representation (TER). Then, the Generalized Voronoi Networks (GVN) with spatial awareness and episodic memory is designed to learn autonomous exploration policies interactively online. For complete and efficient exploration, Invalid Action Masking (IAM) is employed to reshape the configuration space of exploration tasks to cope with the explosion of action space and observation space caused by the expansion of the exploration range. Furthermore, a well-designed reward function is leveraged to guide the learning of policies. Extensive baseline tests and comparative simulations show that our strategy outperforms the state-of-the-art strategies in terms of map quality and exploration speed. Sufficient ablation studies and mobile robot experiments demonstrate the effectiveness and superiority of our strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
22. Improved artificial fish swarm based optimize rapidly-exploring random trees multi-robot exploration algorithm.
- Author
-
Yao, Zhifeng, Liu, Quanze, and Ju, Yongzhi
- Subjects
- *
PARTICLE swarm optimization , *ALGORITHMS , *TREES , *MOBILE robots , *PROBLEM solving - Abstract
To solve the problems of high storage resource consumption and low efficiency of the RRT exploration algorithm in the late stage of exploration, this paper proposes an Improved Artificial Fish Swarm based Optimize Rapidly-exploring Random Trees multi-robot Exploration Algorithm. Firstly, the efficiency of a single robot's exploration of nearby unknown regions is improved by dynamically adjusting the step size of the RRT tree.Secondly, the improved artificial fish swarm algorithm is used to delete the redundant nodes in the RRT tree and optimize the node state in the RRT tree, which reduces the occupation of memory resources and improves the exploration efficiency of the RRT tree in the narrow environment.Results from comparative experiments in simulation environments with different degrees of openness show that the optimized exploration algorithm can save significant storage resources and show better exploration performance in narrow environments compared to the original RRT exploration algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
23. 3D Mapping Using Multi-agent Systems
- Author
-
Jogeshwar, Bhaavin K., HomChaudhuri, Baisravan, Kappagantula, Sivayazi, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Sharma, Sanjay, editor, Subudhi, Bidyadhar, editor, and Sahu, Umesh Kumar, editor
- Published
- 2023
- Full Text
- View/download PDF
24. A Navigation and Control Framework of Quadrupedal Robot for Autonomous Exploration in Cave Environments
- Author
-
Hu, Yong, Jiang, Lisong, Kang, Ke, Cao, Dandan, Zhou, Zhongda, Liu, Junfeng, Wang, Yong, Li, Maodeng, Hu, Haidong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Huayong, editor, Liu, Honghai, editor, Zou, Jun, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Yang, Geng, editor, Ouyang, Xiaoping, editor, and Wang, Zhiyong, editor
- Published
- 2023
- Full Text
- View/download PDF
25. Region Clustering for Mobile Robot Autonomous Exploration in Unknown Environment
- Author
-
Zheng, Haoping, Zhang, Liwei, Chen, Meng, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Fuchun, editor, Cangelosi, Angelo, editor, Zhang, Jianwei, editor, Yu, Yuanlong, editor, Liu, Huaping, editor, and Fang, Bin, editor
- Published
- 2023
- Full Text
- View/download PDF
26. Exploration Method Considering Received Signal Strength of Communication
- Author
-
Li, Xuanang, Lai, Jizhou, Lyu, Pin, Fang, Wei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Deng, Yimin, editor
- Published
- 2023
- Full Text
- View/download PDF
27. Autonomous Exploration for 3D Mapping Using a Mobile Manipulator Robot with an RGB-D Camera
- Author
-
Uomi, Ryota, Yorozu, Ayanori, Ohya, Akihisa, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Petrovic, Ivan, editor, Menegatti, Emanuele, editor, and Marković, Ivan, editor
- Published
- 2023
- Full Text
- View/download PDF
28. ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology
- Author
-
Placed, Julio A., Rodríguez, Juan J. Gómez, Tardós, Juan D., Castellanos, José A., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tardioli, Danilo, editor, Matellán, Vicente, editor, Heredia, Guillermo, editor, Silva, Manuel F., editor, and Marques, Lino, editor
- Published
- 2023
- Full Text
- View/download PDF
29. Autonomous robotic exploration with region-biased sampling and consistent decision making
- Author
-
Jin Wang, Huan Yu, Zhi Zheng, Guodong Lu, Kewen Zhang, Tao Zheng, and Cong Fang
- Subjects
Autonomous exploration ,RRT ,Mean shift ,FSOTSP ,Decision making ,Electronic computers. Computer science ,QA75.5-76.95 ,Information technology ,T58.5-58.64 - Abstract
Abstract In this paper, we propose a scheme for autonomous exploration in unknown environments using a mobile robot. To reduce the storage consumption and speed up the search of frontiers, we propose a wave-features-based rapidly exploring random tree method, which can inhibit or promote the growth of sampling trees regionally. Then, we prune the frontiers with mean shift algorithm and use the pruned frontiers for decision-making. To avoid the repeated exploration, we develop a decision making method with consistency assessment, in which the status of the robot and frontiers are explicitly encoded and modeled as a fixed start open traveling salesman problem (FSOTSP). Furthermore, a re-decision mechanism is build to reduce the extra computing cost. Simulations and real-world experiments show the significant improvement of the proposed scheme.
- Published
- 2023
- Full Text
- View/download PDF
30. EMExplorer: an episodic memory enhanced autonomous exploration strategy with Voronoi domain conversion and invalid action masking
- Author
-
Bolei Chen, Ping Zhong, Yongzheng Cui, Siyi Lu, Yixiong Liang, and Yu Sheng
- Subjects
Autonomous exploration ,Episodic memory ,Deep reinforcement learning ,Generalized Voronoi diagram ,Invalid action masking ,Electronic computers. Computer science ,QA75.5-76.95 ,Information technology ,T58.5-58.64 - Abstract
Abstract Autonomous exploration is a critical technology to realize robotic intelligence as it allows unsupervised preparation for future tasks and facilitates flexible deployment. In this paper, a novel Deep Reinforcement Learning (DRL) based autonomous exploration strategy is proposed to efficiently reduce the unknown area of the workspace and provide accurate 2D map construction for mobile robots. Different from existing human-designed exploration techniques that usually make strong assumptions about the scenarios and the tasks, we utilize a model-free method to directly learn an exploration strategy through trial-and-error interactions with complex environments. To be specific, the Generalized Voronoi Diagram (GVD) is first utilized for domain conversion to obtain a high-dimensional Topological Environmental Representation (TER). Then, the Generalized Voronoi Networks (GVN) with spatial awareness and episodic memory is designed to learn autonomous exploration policies interactively online. For complete and efficient exploration, Invalid Action Masking (IAM) is employed to reshape the configuration space of exploration tasks to cope with the explosion of action space and observation space caused by the expansion of the exploration range. Furthermore, a well-designed reward function is leveraged to guide the learning of policies. Extensive baseline tests and comparative simulations show that our strategy outperforms the state-of-the-art strategies in terms of map quality and exploration speed. Sufficient ablation studies and mobile robot experiments demonstrate the effectiveness and superiority of our strategy.
- Published
- 2023
- Full Text
- View/download PDF
31. Autonomous robotic exploration with region-biased sampling and consistent decision making.
- Author
-
Wang, Jin, Yu, Huan, Zheng, Zhi, Lu, Guodong, Zhang, Kewen, Zheng, Tao, and Fang, Cong
- Subjects
MOBILE robots ,DECISION making ,TRAVELING salesman problem ,TREE growth ,ROBOTICS - Abstract
In this paper, we propose a scheme for autonomous exploration in unknown environments using a mobile robot. To reduce the storage consumption and speed up the search of frontiers, we propose a wave-features-based rapidly exploring random tree method, which can inhibit or promote the growth of sampling trees regionally. Then, we prune the frontiers with mean shift algorithm and use the pruned frontiers for decision-making. To avoid the repeated exploration, we develop a decision making method with consistency assessment, in which the status of the robot and frontiers are explicitly encoded and modeled as a fixed start open traveling salesman problem (FSOTSP). Furthermore, a re-decision mechanism is build to reduce the extra computing cost. Simulations and real-world experiments show the significant improvement of the proposed scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
32. 基于多重信息增益的移动机器人探索策略.
- Author
-
阮晓钢, 陈 晓, and 朱晓庆
- Subjects
POINT set theory ,PROBLEM solving ,MOBILE robots ,ENTROPY ,BLINDNESS ,ROBOTS - Abstract
Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
33. Fast autonomous exploration with sparse topological graphs in large-scale environments
- Author
-
Wei, Changyun, Wu, Jianbin, Xia, Yu, and Ji, Ze
- Published
- 2024
- Full Text
- View/download PDF
34. ASEP: An Autonomous Semantic Exploration Planner With Object Labeling
- Author
-
Ana Milas, Antun Ivanovic, and Tamara Petrovic
- Subjects
Autonomous exploration ,semantic segmentation ,UAV ,path planning ,object labeling ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
In this paper, we present a novel 3D autonomous exploration planner called the Autonomous Semantic Exploration Planner (ASEP), designed for GPS-denied indoor environments. ASEP combines real-time mapping, exploration, navigation, object detection, and object labeling onboard an Unmanned Aerial Vehicle (UAV) with limited resources. The planner is based on a frontier exploration strategy that utilizes semantic information about the environment in the exploration policy. The policy is extended to incorporate both geometric and semantic information provided by a deep convolutional neural network (DCNN) for semantic segmentation. This semantically-enhanced exploration algorithm directs the exploration toward the quick labeling of all objects of interest in the environment. An extended path planning algorithm continuously checks for path validity, enabling safe navigation in challenging environments. The overall system is designed to be modular and easily extended or replaced with custom modules. The proposed planner is evaluated and analyzed in both simulation and real-world environments using a UAV. Experimental studies demonstrate the effectiveness of the ASEP strategy compared to state-of-the-art methods. Results show that the objects in the environment are explored faster and total exploration time is reduced while the computational time remains consistent regardless of the semantic segmentation processing involved.
- Published
- 2023
- Full Text
- View/download PDF
35. Safe Autonomous Exploration and Adaptive Path Planning Strategy Using Signed Distance Field
- Author
-
Heying Wang, Yuan Lin, Wei Zhang, Wentao Ye, Mingming Zhang, and Xue Dong
- Subjects
Path planning ,autonomous exploration ,path safety ,signed distance field ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Autonomous exploration in unknown environment has remained challenging due to unexpected collisions, stuckness and slowdowns around obstacles. This paper reports a novel approach based on Signed Distance Field (SDF), to optimize path planning algorithms and autonomous exploration strategy for safe and adaptive navigation in search and rescue missions. A quantitative criterion is established for evaluating the safety of planned trajectories. Simulation results show that the proposed SDF-A* path planner outperforms traditional methods with a 30.10% increase in path safety (i.e. average distance from robot to obstacles) and a 64.11% reduction in time consumption; The proposed text SDF-based Safe Autonomous Exploration Strategy, combined with SDF-A* path planner, outperform traditional methods, leading to significant increases (47.06%) in path safety and reductions (44.75% and 15.32%) in exploration time and path length, respectively. The viability, efficiency, and safety of the proposed methods are further validated through text real-world experiments on a text three-wheeled differential steering robot equipped with Jetson Nano and RPLIDAR-A3 lidar. Results show that the proposed approach adapts to different indoor environments and map configurations without prior parameter settings.
- Published
- 2023
- Full Text
- View/download PDF
36. Efficient Autonomous Exploration and Mapping in Unknown Environments.
- Author
-
Feng, Ao, Xie, Yuyang, Sun, Yankang, Wang, Xuanzhi, Jiang, Bin, and Xiao, Jian
- Subjects
- *
KRIGING , *AUTONOMOUS robots , *REINFORCEMENT learning - Abstract
Autonomous exploration and mapping in unknown environments is a critical capability for robots. Existing exploration techniques (e.g., heuristic-based and learning-based methods) do not consider the regional legacy issues, i.e., the great impact of smaller unexplored regions on the whole exploration process, which results in a dramatic reduction in their later exploration efficiency. To this end, this paper proposes a Local-and-Global Strategy (LAGS) algorithm that combines a local exploration strategy with a global perception strategy, which considers and solves the regional legacy issues in the autonomous exploration process to improve exploration efficiency. Additionally, we further integrate Gaussian process regression (GPR), Bayesian optimization (BO) sampling, and deep reinforcement learning (DRL) models to efficiently explore unknown environments while ensuring the robot's safety. Extensive experiments show that the proposed method could explore unknown environments with shorter paths, higher efficiencies, and stronger adaptability on different unknown maps with different layouts and sizes. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Development of a search and rescue robot system for the underground building environment.
- Author
-
Wang, Gongcheng, Wang, Weidong, Ding, Pengchao, Liu, Yueming, Wang, Han, Fan, Zhenquan, Bai, Hua, Hongbiao, Zhu, and Du, Zhijiang
- Subjects
UNDERGROUND construction ,RESCUE work ,ROBOT hands ,GEOGRAPHICAL perception ,CONCRETE walls ,ROBOTS - Abstract
The underground building environment plays an increasingly important role in the construction of modern cities. To deal with possible fires, collapses, and so on, in underground building space, it is a general trend to use rescue robots to replace humans. This paper proposes a dual‐robot system solution for search and rescue in an underground building environment. To speed up rescue and search, the two robots focus on different tasks. However, the environmental perception information and location of them are shared. The primary robot is used to quickly explore the environment in a wide range, identify objects, cross difficult obstacles, and so on. The secondary robot is responsible for grabbing, carrying items, clearing obstacles, and so on. In response to the difficulty of rescue caused by unknown scenes, the Lidar, inertial measurement unit and multiview cameras are integrated for large‐scale 3D environment mapping. The depth camera detects the objects to be rescued and locate them on the map. A six‐degree‐of‐freedom manipulator with a two‐finger gripper is equipped to open doors and clear roadblocks during the rescue. To solve the problem of severe signal attenuation caused by reinforced concrete walls, corners and long‐distance transmission, a wireless multinode networking solution is adopted. In the case of a weak wireless signal, the primary robot uses autonomous exploration for environmental perception. Experimental results show the robots' system has high reliability in over‐the‐horizon maneuvering, teleoperation of the door opening and grasping, object searching, and environmental perception, and can be well applied to underground search and rescue. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
- Author
-
Ziyu Cao, Zhihui Du, and Jianhua Yang
- Subjects
autonomous exploration ,path planning ,PCATSP ,prior information ,topological map ,Motor vehicles. Aeronautics. Astronautics ,TL1-4050 - Abstract
Robot autonomous exploration is a challenging and valuable research field that has attracted widespread research interest in recent years. However, existing methods often encounter problems such as incomplete exploration, repeated exploration paths, and low exploration efficiency when facing large-scale scenes. Considering that many indoor and outdoor scenes usually have a prior topological map, such as road navigation maps, satellite road network maps, indoor computer-aided design (CAD) maps, etc., this paper incorporated this information into the autonomous exploration framework and proposed an innovative topological map-based autonomous exploration method for large-scale scenes. The key idea of the proposed method is to plan exploration paths with long-term benefits by tightly merging the information between robot-collected and prior topological maps. The exploration path follows a global exploration strategy but prioritizes exploring scenes outside the prior information, thereby preventing the robot from revisiting explored areas and avoiding the duplication of any effort. Furthermore, to improve the stability of exploration efficiency, the exploration path is further refined by assessing the cost and reward of each candidate viewpoint through a fast method. Simulation experimental results demonstrated that the proposed method outperforms state-of-the-art autonomous exploration methods in efficiency and stability and is more suitable for exploration in large-scale scenes. Real-world experimentation has also proven the effectiveness of our proposed method.
- Published
- 2024
- Full Text
- View/download PDF
39. Target-Driven Autonomous Robot Exploration in Mappless Indoor Environments Through Deep Reinforcement Learning
- Author
-
Shuai, Wenxuan, Huang, Mengxing, Wu, Di, Cao, Gang, Feng, Zikai, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Yang, Shuo, editor, and Lu, Huimin, editor
- Published
- 2022
- Full Text
- View/download PDF
40. A Multi-robot Collaborative Exploration Technology Based on Instance Segmentation
- Author
-
Lin, Junqin, Chen, Zhihong, Wang, Yanbo, Huang, Kui, Chen, Yanjiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, Fu, Yongling, editor, and Zhao, Shoujun, editor
- Published
- 2022
- Full Text
- View/download PDF
41. Improved RRT Autonomous Exploration Method Based on Hybrid Clustering Algorithm
- Author
-
Yin, Yizhen, Ma, Hongjun, Liang, Xinkai, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wu, Meiping, editor, Niu, Yifeng, editor, Gu, Mancang, editor, and Cheng, Jin, editor
- Published
- 2022
- Full Text
- View/download PDF
42. Space-Heuristic Navigation and Occupancy Map Prediction for Robot Autonomous Exploration
- Author
-
Zhong, Ping, Chen, Bolei, Cui, Yongzheng, Song, Hanchen, Sheng, Yu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lai, Yongxuan, editor, Wang, Tian, editor, Jiang, Min, editor, Xu, Guangquan, editor, Liang, Wei, editor, and Castiglione, Aniello, editor
- Published
- 2022
- Full Text
- View/download PDF
43. Hierarchical framework for mobile robots to effectively and autonomously explore unknown environments.
- Author
-
Sun, Xuehao, Deng, Shuchao, Tong, Baohong, Wang, Shuang, Zhang, Chenyang, and Jiang, Yuxiang
- Subjects
MOBILE robots ,COST functions ,DEGREES of freedom ,ROBOTS - Abstract
Achieving efficient and safe autonomous exploration in unknown environments is an urgent challenge to be overcome in the field of robotics. Existing exploration methods based on random and greedy strategies cannot ensure that the robot moves to the unknown area as much as possible, and the exploration efficiency is not high. In addition, because the robot is located in an unknown environment, the robot cannot obtain enough information to process the surrounding environment and cannot guarantee absolute safety. To improve the efficiency and safety of exploring unknown environments, we propose an autonomous exploration motion planning framework that is divided into the exploration and obstacle avoidance levels. The two levels are independent and interconnected. The exploration level finds the optimal frontier target point in the global scope based on the forward filtering angle and cost function, attracting the robot to move to the unknown area as much as possible, and improving the exploration efficiency; the obstacle avoidance level establishes a scenario-speed conversion mechanism, and the target point and obstacle information are weighed to realise dynamic motion planning and completes obstacle avoidance control, and ensures the safety of exploration. Experiments in different simulation scenarios and real environments verify the superiority of the method. Results show that our method is superior to the existing methods. • An autonomous exploration method for a car-like mobile robot on the ground is designed. Even if the degree of freedom of exploration on the ground is limited and the process is more difficult. • The method is an interconnected and independent framework, including the exploration and the obstacle avoidance levels, both of which are dynamically expanded in the re-planning step. • The purpose of the exploration level is to provide the robot with a clear exploration direction, so that the robot can move to the unknown area as much as possible. • The obstacle avoidance level processes the robot's local environment in real time, it provides the robot with exploration power and ensures the safety of the exploration process. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. A Novel Informative Autonomous Exploration Strategy With Uniform Sampling for Quadrotors.
- Author
-
Zhang, Xuetao, Chu, Yubin, Liu, Yisha, Zhang, Xuebo, and Zhuang, Yan
- Subjects
- *
SPACE exploration , *SURFACE reconstruction , *TASK analysis - Abstract
In this article, a novel informative autonomous exploration strategy with uniform sampling is proposed to efficiently reduce the unknown volume of 3-D environments and provide an accurate truncated-signed-distance-function-based reconstruction for quadrotors. Different from existing methods, the proposed method locally samples candidate viewpoints uniformly and achieves a global coverage efficiently by backtracking history nodes when there is no unmapped space near the quadrotor. Specifically, by sampling viewpoints with a fixed radius and assigning priority, the best one is selected to which a collision-free path will be planned. Then, a history list is designed to store the executed nodes, which is backtracked to find the unmapped space, resulting in a complete exploration of the environment. In addition, a hierarchical information gain is proposed to balance the space exploration efficiency and reconstruction accuracy by considering the uncertainty of the surface and volume of the unknown space. Comparative experiments in simulation show that the proposed method outperforms the sampling-based state-of-the-art method in terms of both the exploration time and reconstruction accuracy. Moreover, the effectiveness of the proposed approach is further evaluated in a real-world experiment. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
45. Multi-Robot Hybrid Coverage Path Planning for 3D Reconstruction of Large Structures
- Author
-
Randa Almadhoun, Tarek Taha, Lakmal Seneviratne, and Yahya Zweiri
- Subjects
Autonomous exploration ,multi-robot ,coverage planning ,LSTM ,path prediction ,3D reconstruction ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Coverage Path Planning (CPP) is an essential capability for autonomous robots operating in various critical applications such as fire fighting, and inspection. Performing autonomous coverage using a single robot system consumes time and energy. In particular, 3D large structures might contain some complex and occluded areas that shall be scanned rapidly in certain application domains. In this paper, a new Hybrid Coverage Path Planning (HCPP) approach is proposed to explore and cover unknown 3D large structures using a decentralized multi-robot system. The HCPP approach combines a guided Next Best View (NBV) approach with a developed Long Short Term Memory (LSTM) waypoint prediction approach to decrease the CPP exploration time at each iteration and simultaneously achieve high coverage. The hybrid approach is the new ML paradigm which fosters intelligence by balancing between data efficiency and generality allowing the exchange of some CPP parts with a learned model. The HCPP uses a stateful LSTM network architecture which is trained based on collected paths that cover different 3D structures to predict the next viewpoint. This architecture captures the dynamic dependencies of adjacent viewpoints in the long term sequences like the coverage paths. The HCPP switches between these methods triggered by either the number of iterations or an entropy threshold. In the decentralized multi-robot system, the proposed HCPP is embedded in each robot where each one of them shares its global 3D map ensuring robustness. The results performed in a realistic Gazebo robotic simulator confirmed the advantage of the proposed HCPP approach by achieving high coverage on different 3D unknown structures in a shorter time compared to conventional NBV.
- Published
- 2022
- Full Text
- View/download PDF
46. Rmap+: Autonomous Path Planning for Exploration of Mobile Robot Based on Inner Pair of Outer Frontiers.
- Author
-
Buriboev, Abror, Hyun Kyu Kang, Jun Dong Lee, Oh, Ryumduck, and Heung Seok Jeon
- Subjects
MOBILE robots ,ROBOTIC path planning ,BASE pairs ,GRIDS (Cartography) - Abstract
Exploration of mobile robot without prior data about environments is a fundamental problem during the SLAM processes. In this work, we propose improved version of previous Rmap algorithm by modifying its Exploration submodule. Despite the previous Rmap's performance which significantly reduces the overhead of the grid map, its exploration module costs a lot because of its rectangle following algorithm. To prevent that, we propose a new Rmap+ algorithm for autonomous path planning of mobile robot to explore an unknown environment. The algorithm bases on paired frontiers. To navigate and extend an exploration area of mobile robot, the Rmap+ utilizes the inner and outer frontiers. In each exploration round, the mobile robot using the sensor range determines the frontiers. Then robot periodically changes the range of sensor and generates inner pairs of frontiers. After calculating the length of each frontiers' and its corresponding pairs, the Rmap+ selects the goal point to navigate the robot. The experimental results represent efficiency and applicability on exploration time and distance, i.e., to complete the whole exploration, the path distance decreased from 15% to 69%, as well as the robot decreased the time consumption from 12% to 86% than previous algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
47. From environmental exploration to clearance measurement – developing mobile robot systems for decommissioning of nuclear power plants: Von der Umgebungsexploration bis zur Freimessung – Entwicklung mobiler Robotersysteme für den Rückbau kerntechnischer Anlagen
- Author
-
Chen, Ziyuan, Gentes, Sascha, Hartmann, Dennis, Hein, Björn, Kazemi, Siavash, and Wernke, Alena
- Subjects
DECOMMISSIONING of nuclear power plants ,MOBILE robots ,BUILDING information modeling ,NUCLEAR power plants ,RADIATION exposure ,NUCLEAR facilities - Abstract
Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
- Full Text
- View/download PDF
48. Exploration via Progress-Driven Intrinsic Rewards
- Author
-
Bougie, Nicolas, Ichise, Ryutaro, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Farkaš, Igor, editor, Masulli, Paolo, editor, and Wermter, Stefan, editor
- Published
- 2020
- Full Text
- View/download PDF
49. Autonomous Exploration of Unknown Indoor Environments for High-Quality Mapping Using Feature-Based RGB-D SLAM.
- Author
-
Eldemiry, Amr, Zou, Yajing, Li, Yaxin, Wen, Chih-Yung, and Chen, Wu
- Subjects
- *
MOBILE robots , *AUTONOMOUS robots , *QUALITY factor - Abstract
Simultaneous localization and mapping (SLAM) system-based indoor mapping using autonomous mobile robots in unknown environments is crucial for many applications, such as rescue scenarios, utility tunnel monitoring, and indoor 3D modeling. Researchers have proposed various strategies to obtain full coverage while minimizing exploration time; however, mapping quality factors have not been considered. In fact, mapping quality plays a pivotal role in 3D modeling, especially when using low-cost sensors in challenging indoor scenarios. This study proposes a novel exploration algorithm to simultaneously optimize exploration time and mapping quality using a low-cost RGB-D camera. Feature-based RGB-D SLAM is utilized due to its various advantages, such as low computational cost and dense real-time reconstruction ability. Subsequently, our novel exploration strategies consider the mapping quality factors of the RGB-D SLAM system. Exploration time optimization factors are also considered to set a new optimum goal. Furthermore, a Voronoi path planner is adopted for reliable, maximal obstacle clearance and fixed paths. According to the texture level, three exploration strategies are evaluated in three real-world environments. We achieve a significant enhancement in mapping quality and exploration time using our proposed exploration strategies compared to the baseline frontier-based exploration, particularly in a low-texture environment. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
50. Upgraded trajectory planning method deployed in autonomous exploration for unmanned aerial vehicle.
- Author
-
Zhang, Tong, Yu, Jiajie, Li, Jiaqi, and Wei, Jianli
- Subjects
DRONE aircraft ,TRAJECTORY optimization ,UNDERWATER exploration ,FLIGHT planning (Aeronautics) - Abstract
Autonomous exploration is grounded on target decision and trajectory planning, which is widely deployed on unmanned aerial vehicles. However, existing methods generally only focus on the exploration effect of target decision but neglect the environment information gained with trajectory planning during flight, resulting in redundant exploration trajectories and low exploration efficiency. This article proposes an upgraded method of trajectory planning for autonomous exploration work. We design a fresh cost term considering the frontier information in the part of trajectory optimization. Besides, yaw angles are planned independently to catch more environment information during flight. We present extensive simulations and real-world tests. The results show that our proposed method reduces the exploration cost time by 10–15% compared with the previous one. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.