Jiahui Sun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jiahui Sun has a total of 21 co-authors. Here are the top co-authors by number of shared publications:
- Qingchun Feng
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
4 shared publications - Mengfei Ru
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2 shared publications - Yajun Li
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2 shared publications - Changyuan Zhai
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
1 shared publication - Chao Ji
Mechanical Equipment Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China
1 shared publication - Chenchen Gu
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
1 shared publication
Latest Publications
- Open Access
Compliant Motion Planning Integrating Human Skill for Robotic Arm Collecting Tomato Bunch Based on Improved DDPG
Dexterous manipulation and gradual placement are crucial for preserving fruit integrity during harvesting. Addressing the limitations of conventional path planning methods in learning manual compliant skills, we propose a novel method for tomato bunch collection that integrates human-robot skill transfer with Deep Deterministic Policy Gradient (DDPG). In our method, a demonstrator manually guided the robotic arm using an existing tomato collection mechanism, with spatial trajectories recorded as demonstration paths. We then developed an enhanced DDPG-Z model that incorporates human skill replay for pre-training, expert reward regression loss to stabilize pre-training, and dynamic step-length returns to balance short- and long-term rewards. Subsequently, the agent was trained to minimize the deviations of key points between the demonstration paths and actual paths, effectively approximating human operations. In a highly realistic simulation environment, our method achieved a 25% improvement in convergence speed, a 10.3% increase in post-convergence reward, and a 51.3% boost in destination accuracy compared to the case without the demonstrations, whereas classical models such as DDPG, SAC (Soft Actor-Critic), and TD3 (Twin Delayed Deep Deterministic Policy Gradient) failed to converge within the prescribed episodes. This work provides valuable insights for enhancing the compliant operational performance of agricultural robots.
Open AccessCompliant Motion Planning Integrating Human Skill for Robotic Arm Collecting Tomato Bunch Based on Improved DDPG
Dexterous manipulation and gradual placement are crucial for preserving fruit integrity during harvesting. Addressing the limitations of conventional path planning methods in learning manual compliant skills, we propose a novel method for tomato bunch collection that integrates human-robot skill transfer with Deep Deterministic Policy Gradient (DDPG). In our method, a demonstrator manually guided the robotic arm using an existing tomato collection mechanism, with spatial trajectories recorded as demonstration paths. We then developed an enhanced DDPG-Z model that incorporates human skill replay for pre-training, expert reward regression loss to stabilize pre-training, and dynamic step-length returns to balance short- and long-term rewards. Subsequently, the agent was trained to minimize the deviations of key points between the demonstration paths and actual paths, effectively approximating human operations. In a highly realistic simulation environment, our method achieved a 25% improvement in convergence speed, a 10.3% increase in post-convergence reward, and a 51.3% boost in destination accuracy compared to the case without the demonstrations, whereas classical models such as DDPG, SAC (Soft Actor-Critic), and TD3 (Twin Delayed Deep Deterministic Policy Gradient) failed to converge within the prescribed episodes. This work provides valuable insights for enhancing the compliant operational performance of agricultural robots.
- Open Access
Deposition Characteristics of Air-Assisted Sprayer Based on Canopy Volume and Leaf Area of Orchard Trees
Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for precise regulation of spray wind and pesticide dosage. The study addresses the knowledge gap by utilizing laser detection and ranging (LiDAR) to measure the thickness and leaf area of orchard tree canopies. The spray experiments were conducted on canopies of different regions, using an air-assisted sprayer with varying fan speeds of 1381 r/min, 1502 r/min, and 1676 r/min. The deposition effects were analyzed using water-sensitive papers. The inlet air speed within the canopy did not increase proportionally when the spray fan speed increased, and it showed a significant variation in locations with sparse foliage. Furthermore, droplets exhibited abnormal median volume diameters of the canopy regions with lower wind loss rates and smaller leaf areas. The influences were in the order of canopy thickness, leaf area, and inlet air speed on the cumulative deposition of droplets on both sides of the water-sensitive papers, as well as the ratio of deposition between the two sides, from big to small, are inlet air speed, leaf area, and canopy thickness. The study provides a scientific foundation for air control in precision pesticide application in apple orchards and contributes to the rapid development of precision spraying technologies.
Open AccessDeposition Characteristics of Air-Assisted Sprayer Based on Canopy Volume and Leaf Area of Orchard Trees
Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for precise regulation of spray wind and pesticide dosage. The study addresses the knowledge gap by utilizing laser detection and ranging (LiDAR) to measure the thickness and leaf area of orchard tree canopies. The spray experiments were conducted on canopies of different regions, using an air-assisted sprayer with varying fan speeds of 1381 r/min, 1502 r/min, and 1676 r/min. The deposition effects were analyzed using water-sensitive papers. The inlet air speed within the canopy did not increase proportionally when the spray fan speed increased, and it showed a significant variation in locations with sparse foliage. Furthermore, droplets exhibited abnormal median volume diameters of the canopy regions with lower wind loss rates and smaller leaf areas. The influences were in the order of canopy thickness, leaf area, and inlet air speed on the cumulative deposition of droplets on both sides of the water-sensitive papers, as well as the ratio of deposition between the two sides, from big to small, are inlet air speed, leaf area, and canopy thickness. The study provides a scientific foundation for air control in precision pesticide application in apple orchards and contributes to the rapid development of precision spraying technologies.
- Open Access
Dynamic Compressive Stress Relaxation Model of Tomato Fruit Based on Long Short-Term Memory Model
Tomatoes are prone to mechanical damage due to improper gripping forces during automated harvest and postharvest processes. To reduce this damage, a dynamic viscoelastic model based on long short-term memory (LSTM) is proposed to fit the dynamic compression stress relaxation characteristics of the individual fruit. Furthermore, the classical stress relaxation models involved, the triple-element Maxwell and Caputo fractional derivative models, are compared with the LSTM model to validate its performance. Meanwhile, the LSTM and classical stress relaxation models are used to predict the stress relaxation characteristics of tomato fruit with different fruit sizes and compression positions. The results for the whole test dataset show that the LSTM model achieves a RMSE of Mpa and a MAPE of 0.228%. It significantly outperforms the Caputo fractional derivative model by demonstrating a substantial enhancement with a 37% decrease in RMSE and a 36% reduction in MAPE. Further analysis of individual tomato fruit reveals the LSTM model’s performance, with the minimum RMSE recorded at the septum position being Mpa, 31% higher than the maximum RMSE at the locule position. Similarly, the lowest MAPE at the septum stands at 0.375%, outperforming the highest MAPE at the locule position by a significant margin of 90%. Moreover, the LSTM model consistently reports the smallest discrepancies between the predicted and observed values compared to classical stress relaxation models. This accuracy suggests that the LSTM model could effectively supplant classical stress relaxation models for predicting stress relaxation changes in individual tomato fruit.Open AccessDynamic Compressive Stress Relaxation Model of Tomato Fruit Based on Long Short-Term Memory Model
Tomatoes are prone to mechanical damage due to improper gripping forces during automated harvest and postharvest processes. To reduce this damage, a dynamic viscoelastic model based on long short-term memory (LSTM) is proposed to fit the dynamic compression stress relaxation characteristics of the individual fruit. Furthermore, the classical stress relaxation models involved, the triple-element Maxwell and Caputo fractional derivative models, are compared with the LSTM model to validate its performance. Meanwhile, the LSTM and classical stress relaxation models are used to predict the stress relaxation characteristics of tomato fruit with different fruit sizes and compression positions. The results for the whole test dataset show that the LSTM model achieves a RMSE of Mpa and a MAPE of 0.228%. It significantly outperforms the Caputo fractional derivative model by demonstrating a substantial enhancement with a 37% decrease in RMSE and a 36% reduction in MAPE. Further analysis of individual tomato fruit reveals the LSTM model’s performance, with the minimum RMSE recorded at the septum position being Mpa, 31% higher than the maximum RMSE at the locule position. Similarly, the lowest MAPE at the septum stands at 0.375%, outperforming the highest MAPE at the locule position by a significant margin of 90%. Moreover, the LSTM model consistently reports the smallest discrepancies between the predicted and observed values compared to classical stress relaxation models. This accuracy suggests that the LSTM model could effectively supplant classical stress relaxation models for predicting stress relaxation changes in individual tomato fruit. - Open Access
- 11 April 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (8) , 3231
GNSS and LiDAR Integrated Navigation Method in Orchards with Intermittent GNSS Dropout
Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m.
- 11 April 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (8) , 3231
Open AccessGNSS and LiDAR Integrated Navigation Method in Orchards with Intermittent GNSS Dropout
Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m.
- Open Access
Design of Shiitake Mushroom Robotic Picking Grasper: Considering Stipe Compressive Stress Relaxation
In order to realize the automatic picking of shiitake mushrooms and reduce the risk of damage to shiitake mushrooms in the picking process, this paper designed a shiitake mushroom picking grasper. First, this paper carries out mechanical tests of compression and stress relaxation on sections of shiitake mushroom stipes, and establishes the component stress relaxation equations of shiitake mushroom stipes. The compression mechanical characteristics of the entire mushroom stipe are then analyzed using finite element analysis, with a mean square error of less than 5% compared to actual results. Second, based on the actual picking experience, this paper proposes an “L”-shaped three-finger picking grasper, and analyzes the mechanical relationship between the grasper’s gripping force, twisting separation torque, and servo output torque. Furthermore, according to the mechanical constitutive model of mushroom stipes, the optimal twisting separation torque and corresponding servo motor output torque for the grasper are determined. The picking grasper designed in this paper was tested for picking mushrooms of different growth periods, and the test results show that the picking grasper designed in this paper is able to grasp and separate the mushrooms quickly and without damage.
Open AccessDesign of Shiitake Mushroom Robotic Picking Grasper: Considering Stipe Compressive Stress Relaxation
In order to realize the automatic picking of shiitake mushrooms and reduce the risk of damage to shiitake mushrooms in the picking process, this paper designed a shiitake mushroom picking grasper. First, this paper carries out mechanical tests of compression and stress relaxation on sections of shiitake mushroom stipes, and establishes the component stress relaxation equations of shiitake mushroom stipes. The compression mechanical characteristics of the entire mushroom stipe are then analyzed using finite element analysis, with a mean square error of less than 5% compared to actual results. Second, based on the actual picking experience, this paper proposes an “L”-shaped three-finger picking grasper, and analyzes the mechanical relationship between the grasper’s gripping force, twisting separation torque, and servo output torque. Furthermore, according to the mechanical constitutive model of mushroom stipes, the optimal twisting separation torque and corresponding servo motor output torque for the grasper are determined. The picking grasper designed in this paper was tested for picking mushrooms of different growth periods, and the test results show that the picking grasper designed in this paper is able to grasp and separate the mushrooms quickly and without damage.