Research on autonomous obstacle avoidance of mountainous tractors based on semantic neural network and laser SLAM

Ningjie Chang; Xianghai Yan; Bingxin Chen; Yiwei Wu; Liyou Xu

doi:10.1371/journal.pone.0323631

Peer Review History

Original SubmissionFebruary 13, 2025
13 Feb 2025 Author Response https://doi.org/10.1371/journal.pone.0323631.r001
27 Feb 2025 Decision Letter - Lei Zhang, Editor PONE-D-25-03307Research on Autonomous Obstacle Avoidance of Mountainous Tractors Based on Semantic Neural Network and Laser SLAMPLOS ONE Dear Dr. Xu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. This paper proposes a research on autonomous obstacle avoidance of mountainous tractors based on semantic neural network and laser SLAM. But revisions are needed in terms of comparison studies, writing, table and figure formulation and literature review comprehensiveness. Please submit your revised manuscript by Apr 13 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . 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You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Restructure the methodology section to clarify technical details; Expand the experimental section (dynamic scenarios, multi-sensor comparisons ); Add feasibility analysis for practical deployment. Reviewer #2: 1. The experimental environment was mentioned in the paper, but the details were insufficient. Suggest providing a detailed description of the terrain features of the testing area, such as slope, soil type, vegetation density, etc., as well as the types and arrangement of obstacles. This will help other researchers replicate the experiment and validate the results. 2. Please carefully check and confirm whether the figures, tables, etc. in the article conform to the format 3. In addition to comparing with GET and FGM methods, it is recommended to add comparisons with other advanced methods (such as deep learning based obstacle avoidance methods) to more comprehensively evaluate the performance of the proposed method. 4. Some variables that appear for the first time in the formula have not been explained in the main text, such as formula 5. It is recommended to add explanations. 5. Increase the review of existing research, especially the latest research progress related to semantic neural networks and laser SLAM technology. This will help readers better understand the innovation and background of the proposed method. Recommended quotes include "Enhancing Vehicle Re identification by Pair flexible Pose Guided Vehicle Image Synthesis" and "Deep Transfer Learning for Intelligent Vehicle Perception: A Survey" 6. Please ensure that the charts in the paper are clear and easy to read, and that the captions are detailed and accurate. For complex charts, it is recommended to add necessary explanations and clarifications 7. It is recommended to add a comparative analysis with the proposed method when describing existing research methods, clearly pointing out the shortcomings of the existing methods and the areas for improvement of the proposed method. This will help highlight the research contributions and innovative points of the paper. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy . 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Attachments Attachment Submitted filename: Research on Autonomou.docx https://doi.org/10.1371/journal.pone.0323631.r002
Revision 1
7 Apr 2025 Author Response 1. The introduction does not clearly articulate the unique challenges of autonomous obstacle avoidance for mountain tractors. A detailed scenario analysis is needed to highlight the differences from traditional farmland environments. Response: Provide detailed scenarios following your recommendations to clarify the differences from traditional farmland environments, such as: In order to verify the effectiveness of the autonomous obstacle avoidance method proposed in this article for mountain tractors, a representative test area was selected in hilly and mountainous areas, which includes complex terrain such as slopes of different slopes, ravines of varying depths, and dense tree clusters. The slope changes frequently in hilly and mountainous areas, which poses higher requirements for the tractor's power system and suspension system. Compared with the flat terrain of traditional farmland, mountain tractors need to maintain stability and maneuverability on slopes of different slopes, and the autonomous obstacle avoidance system needs to accurately determine the impact of slope on the driving path. Gullies of varying depths and scattered stones and rocks are common obstacles in mountainous environments. These obstacles not only have irregular shapes, but their positions are also difficult to predict, increasing the complexity and difficulty of obstacle avoidance systems. In contrast, obstacles in traditional farmland are usually more regular and easy to identify. Comprehensively test the tractor's autonomous obstacle avoidance ability in different environments in this complex environment. In the testing area, simulate obstacles such as stones, wooden stakes, and simulated trees according to the experimental design. Selecting the John Deere 6125R hilly mountain tractor as the experimental object, 2. The mathematical derivation of plane feature matching in Equations (8)-(10) is overly simplified, with no explanation of how noise in point cloud registration is addressed. It is recommended to refer to the GEITS paper "A non-uniform quadtree map building method including dead-end semantics extraction" (DOI:10.1016/j.geits.2023.100071) for map construction methods. Response: Following your suggestion, we have added a feature matching process for the non-uniform quadtree map construction method, such as: Firstly, remove outliers in the point cloud that are too far from the mean. , Among them, is the -th point in the point cloud, and is the value of the point cloud. If (threshold), it is removed and changed to, and the data is denoised according to the above point cloud filtering. After pose estimation, the point cloud data is optimized through non-uniform quadtree to reduce redundant points and improve the resolution and accuracy of the map. Divide the map into multiple quadtree nodes, with each node representing an area. Dynamically adjust the size of nodes based on point cloud density and terrain complexity. , where is the edge length of the node, is the maximum edge length of the node, and is the number of layers the node is divided into. After each LiDAR scan, insert the newly acquired point cloud data into the quadtree map. Dynamically adjust the resolution of quadtree nodes based on point cloud distribution and terrain features. �16� Among them, is the number of points within the th layer node, and is the point density threshold. Based on the semantic information of the obstacles mentioned above, associate the semantic labels of the target obstacles obtained in section 2.2 with the quadtree nodes. �17� Among them, is the semantic label of node n, and is the semantic label of point . And using the constraint relationship between points and surfaces mentioned above, estimate the pose changes of the tractor. �18� Combine the point cloud data of each frame with the sub map data generated in the previous frame to generate a single sub map. Namely: �19� Among them, is the sub map of frame , and is the point cloud data of frame . All sub maps are iteratively updated to generate a global point cloud map, . Through the above steps and mathematical expressions, combined with the optimization processing steps of non-uniform quadtree maps, high-precision global point cloud maps can be generated. 3. Only GET and FGM algorithms are compared, without including mainstream methods like RRT* or deep reinforcement learning. It is recommended to reference the baseline methods in the GEITS paper "A review on reinforcement learning-based highway autonomous vehicle control" (DOI:10.1016/j.geits.2024.100156). Response: We have supplemented the above references as per your suggestion and conducted comparative analysis with them, such as: Irshayyid A et al. first reviewed the different traffic scenarios discussed in the literature [9], and then conducted a comprehensive review of DRL technology, such as the state representation method for capturing the interactive dynamics required for safe and efficient merging, and the reward formula for managing key indicators such as safety, efficiency, comfort, and adaptability. The insights from this review can guide future research towards realizing the potential of DRL in complex traffic automation under uncertainty. A dynamic adaptive trajectory planning method based on Bezier curve is proposed. To further validate the applicability of the design method and test the power consumption and delay tolerance in different distance environments, a comparative test analysis was conducted between the reinforcement learning based vehicle control algorithm in reference [9] and the Bezier curve based vehicle control algorithm in reference [10]. The results are shown below. Table 4 Results of comparing power consumption and minimum obstacle avoidance distance at different distances distance /m A vehicle control algorithm based on reinforcement learning Vehicle control algorithm based on Bezer curve Proposed method Power consumption/kWh Delay tolerance/s Power consumption/kWh Delay tolerance/s Power consumption/kWh Delay tolerance/s 500 0.86 1.5 0.77 1.4 0.2 0.12 1000 0.99 1.9 0.86 1.9 0.39 0.24 1500 1.20 2.1 0.92 2.1 0.61 0.33 2000 1.35 2.5 1.32 2.3 0.77 0.41 2500 1.41 5.9 1.50 2.5 0.95 0.52 According to the data in Table 4, the power consumption of the Proposed method is significantly lower than the other two algorithms at a distance of 500 meters. The power consumption of the Proposed method is 0.2 kWh, while the algorithms based on reinforcement learning and Bezier curve are 0.86 kWh and 0.77 kWh, respectively. This indicates that the proposed method performs the best in terms of power consumption and has higher energy efficiency. And the delay tolerance of the Proposed method * * is significantly lower than the other two algorithms. At a distance of 500 meters, the delay tolerance of the Proposed method is 0.12 seconds, while the reinforcement learning based algorithm and the Bezier curve based algorithm are 1.5 seconds and 1.4 seconds, respectively. This indicates that the proposed method performs the best in terms of delay tolerance, with higher real-time performance and response speed. 4. The coordination mechanism between A* and DWA is vaguely described, with no explanation of how global paths are dynamically updated to adapt to environmental changes. It is recommended to refer to the spatiotemporal constraint optimization methods in the GEITS paper "Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic" (DOI:10.1016/j.geits.2024.100159). Response: We have referred to relevant papers as per your suggestion, supplemented with spatiotemporal constraints and other conditions to adapt to environmental changes, such as: (3) Space time constraint setting In the cost function of the A * algorithm, add the terrain complexity factor : �22� Among them, is the slope at node , is the roughness at node , and and are weight coefficients. Add safety distance constraints to obstacles in the trajectory scoring of DWA algorithm: �23� Among them, (xt, yt) are points on the trajectory, and (xo, yo) are the positions of obstacles. (4) Trajectory rating: Based on the above constraint settings,trajectory scoring. The trajectories of each simulated tractor were scored to evaluate the merits. The scoring function considering deviations from the target heading , deviation from the target position and velocity deviations , the formula is expressed as follows: (24) 5. Key metrics such as inference speed (FPS) and memory usage of the lightweight network are missing, making it impossible to prove its suitability for embedded devices. Response: Complthe comparative analysis following your recommendations, such as: To verify whether the method proposed in this article is applicable to embedded devices, a memory usage analysis was conducted, and the results are shown in the following table: Table 4 Comparison results of memory occupancy at different times Time / day Vehicle control algorithm based on reinforcement learning /% Vehicle control algorithm based on Bezer curve/% Proposed method/% 5 11 10 8 10 15 15 9 15 18 16 10 20 19 17 10 25 21 19 12 30 22 20 14 The memory usage of the proposed method is significantly lower than the other two algorithms. At 5 days, the proposed method had a memory usage rate of 8%, while the reinforcement learning based algorithm and the Bezier curve based algorithm had memory usage rates of 11% and 10%, respectively. As time goes on, the memory usage of the proposed method grows relatively steadily. This indicates that the proposed method performs the best in terms of memory usage and has higher memory efficiency. Can effectively embed devices. 6. The data availability statement claims "data is within the manuscript," but no point cloud dataset or code links are provided, violating reproducibility principles. Data should be uploaded to a public platform. Response: Regarding the issue you mentioned that the point cloud dataset and code links were not provided, we would like to clarify that some of the data involved in this study do contain proprietary information, which cannot be fully uploaded to public platforms. The publicly available data has been provided with relevant information in the article to ensure the transparency and verifiability of the research. 7. The system's power consumption and its impact on tractor endurance, especially in mountainous areas without charging facilities, are not discussed. This issue is critical and should be addressed. Response: The power consumption test analysis of this system has been supplemented by following your recommendations, such as: To further validate the applicability of the design method and test the power consumption and delay tolerance in different distance environments, a comparative test analysis was conducted between the reinforcement learning based vehicle control algorithm in reference [9] and the Bezier curve based vehicle control algorithm in reference [10]. The results are shown below. Table 4 Results of comparing power consumption and minimum obstacle avoidance distance at different distances distance /m A vehicle control algorithm based on reinforcement learning Vehicle control algorithm based on Bezer curve Proposed method Power consumption/kWh Delay tolerance/s Power consumption/kWh Delay tolerance/s Power consumption/kWh Delay tolerance/s 500 0.86 1.5 0.77 1.4 0.2 0.12 1000 0.99 1.9 0.86 1.9 0.39 0.24 1500 1.20 2.1 0.92 2.1 0.61 0.33 2000 1.35 2.5 1.32 2.3 0.77 0.41 2500 1.41 5.9 1.50 2.5 0.95 0.52 According to the data in Table 4, the power consumption of the Proposed method is significantly lower than the other two algorithms at a distance of 500 meters. The power consumption of the Proposed method is 0.2 kWh, while the algorithms based on reinforcement learning and Bezier curve are 0.86 kWh and 0.77 kWh, respectively. This indicates that the proposed method performs the best in terms of power consumption and has higher energy efficiency. And the delay tolerance of the Proposed method * * is significantly lower than the other two algorithms. At a distance of 500 meters, the delay tolerance of the Proposed method is 0.12 seconds, while the reinforcement learning based algorithm and the Bezier curve based algorithm are 1.5 seconds and 1.4 seconds, respectively. This indicates that the proposed method performs the best in terms of delay tolerance, with higher real-time performance and response speed. 8. Safety is only measured by "comfort score," without introducing quantitative metrics from agricultural machinery safety standards (e.g., ISO 25119), such as minimum obstacle avoidance distance or emergency braking response time. Response: The minimum obstacle avoidance distance test analysis has been supplemented by following your recommendations, such as: To further verify the safety performance of the design method, a minimum obstacle avoidance distance test analysis was conducted, and the results are shown in the following figure. Figure 8 Minimum obstacle avoidance distance test results Based on the above results, it can be seen that the minimum obstacle avoidance distance of the algorithm in this paper is always around 8m and relatively stable, while the minimum obstacle avoidance distance of the vehicle control algorithm based on reinforcement learning fluctuates greatly between 1-5m and is unstable; The minimum obstacle avoidance distance of the vehicle control algorithm based on the Beizer curve hovers between 0.6 and 4 meters, which is too small and poses a certain degree of danger, and the fluctuation is greater than that of the vehicle control algorithm based on reinforcement learning. This indicates that the security performance of the algorithm in this article is relatively good. 9. In distributed control scenarios, communication delays between LiDAR and computing units may affect real-time performance. Delay tolerance testing should be added. Response: The delay tolerance test analysis has been supplemented by following your recommendations, such as: To further validate the applicability of the design method and test the power consumption and delay tolerance in different distance environments, a comparative test analysis was conducted between the reinforcement learning based vehicle control algorithm in reference [9] and the Bezier curve based vehicle control algorithm in reference [10]. The results are shown below. Table 4 Results of comparing power consumption and minimum obstacle avoidance distance at different distances distance /m A vehicle control algorithm based on reinforcement learning Vehicle control algorithm based on Bezer curve Proposed method Power consumption/kWh Delay tolerance/s Power consumption/kWh Delay tolerance/s Power consumption/kWh Delay tolerance/s 500 0.86 1.5 0.77 1.4 0.2 0.12 1000 0.99 1.9 0.86 1.9 0.39 0.24 1500 1.20 2.1 0.92 2.1 0.61 0.33 2000 1.35 2.5 1.32 2.3 0.77 0.41 2500 1.41 5.9 1.50 2.5 0.95 0.52 According to the data in Table 4, the power consumption of the Proposed method is significantly lower than the other two algorithms at a distance of 500 meters. The power consumption of the Proposed method is 0.2 kWh, while the algorithms based on reinforcement learning and Bezier curve are 0.86 kWh and 0.77 kWh, respectively. This indicates that the proposed method performs the best in terms of power consumption and has higher energy efficiency. And the delay tolerance of the Proposed method * * is significantly lower than the other two algorithms. At a distance of 500 meters, the delay tolerance of the Proposed method is 0.12 seconds, while the reinforcement learning based algorithm and the Bezier curve based algorithm are 1.5 seconds and 1.4 seconds, respectively. This indicates that the proposed method performs the best in terms of delay tolerance, with higher real-time performance and response speed. 10. Some references (e.g., [9] on laser SLAM) are from before 2023 and you can cite the latest GEITS paper "Deep transfer learning for intelligent vehicle perception: A survey" (DOI: Attachments Attachment Submitted filename: Research_on_Autonomou_auresp_1.docx https://doi.org/10.1371/journal.pone.0323631.r003
11 Apr 2025 Decision Letter - Lei Zhang, Editor Research on Autonomous Obstacle Avoidance of Mountainous Tractors Based on Semantic Neural Network and Laser SLAM PONE-D-25-03307R1 Dear Dr. Xu, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: (No Response) ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: (No Response) ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: (No Response) ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: (No Response) ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: (No Response) ******** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: This paper presents an autonomous obstacle avoidance method for mountainous tractors using semantic neural networks and laser SLAM. It integrates LiDAR-based environment scanning, a pruned YOLOv3 model for obstacle detection, and A* with DWA algorithms for path planning. Experimental results demonstrate improved accuracy, efficiency, and safety over traditional methods, with lower power consumption and memory usage. However, the study lacks comparisons with advanced algorithms like RRT* and detailed validation under dynamic conditions, necessitating further refinement for real-world deployment. Reviewer #2: (No Response) ******** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy . Reviewer #1: No Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0323631.r004
Formally Accepted
Acceptance Letter - Lei Zhang, Editor PONE-D-25-03307R1 PLOS ONE Dear Dr. Xu, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Lei Zhang Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0323631.r005

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