Y. Tang
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
5 records found
1
This thesis presents a comprehensive exploration of unwieldy object delivery using mobile robots, focusing on the challenges and advancements in Navigation Among Movable Objects (NAMO). The research addresses critical issues in robotic manipulation, particularly nonprehensile techniques such as pushing, which are essential for handling objects that are difficult to grasp.
The study is structured around four key research questions guiding the investigation into effective pushing manipulation strategies:
1. How to perform pushing manipulation with limited or inaccurate state estimation: We employ the concept of “stable pushing,” ensuring the robot can always “catch” the object during delivery. The stable pushing control problem is simplified as an optimization problem with a concise linear constraint. Experiments show that this approach outperforms reactive pushing strategies, reducing the robot’s traveled distance by 23.8% and time by 77.4%.
2. How to improve pushing manipulation when accurate state estimation is available: We enhance the stable pushing approach to a more maneuverable “free pushing” method, allowing contact point changes to improve manipulation mobility. This approach achieves an average success rate of 83% with an accuracy of 0.085m when pushing to selected goals, demonstrating improved agility and efficiency compared to stable pushing.
3. How to achieve efficient, real-time global trajectory optimization for contact-rich pushing: By investigating the differential flatness property of the pushing system, we simplify the pushing planning problem, significantly reducing computational complexity. This transformation allows for a simpler contact-implicit planning task that is easy to design, fast to solve, and robust to uncertainties.
4. How to perform robust state estimation through sensor fusion: We integrate data from multiple sensors and improve the robustness of the classic Kalman Filter using a deep reinforcement learning (DRL) algorithm. This novel DRL-based orientation estimation method guarantees bounded estimation errors without the need for hyperparameter tuning. Experiments demonstrate its superior performance compared to conventional methods, particularly in challenging scenarios with inaccurate initial state estimates, imprecise filter gains, and non-Gaussian noise environments.
The key contributions of this thesis include:
• A stable pushing approach that simplifies the optimization problem for nonholonomic mobile bases.
• A maneuverable free pushing method that enhances agility while maintaining contact.
• A reactive manipulation strategy leveraging differential flatness for efficient trajectory planning.
• A reinforcement learning-based approach to improve state estimation accuracy.
In conclusion, this research significantly advances the capabilities of mobile robots in handling unwieldy objects, bridging the gap between theoretical navigation planning and practical applications in complex environments. The findings pave the way for future research and broader applications of mobile robots in various domains, including logistics, search and rescue, and autonomous inspections. ...
The study is structured around four key research questions guiding the investigation into effective pushing manipulation strategies:
1. How to perform pushing manipulation with limited or inaccurate state estimation: We employ the concept of “stable pushing,” ensuring the robot can always “catch” the object during delivery. The stable pushing control problem is simplified as an optimization problem with a concise linear constraint. Experiments show that this approach outperforms reactive pushing strategies, reducing the robot’s traveled distance by 23.8% and time by 77.4%.
2. How to improve pushing manipulation when accurate state estimation is available: We enhance the stable pushing approach to a more maneuverable “free pushing” method, allowing contact point changes to improve manipulation mobility. This approach achieves an average success rate of 83% with an accuracy of 0.085m when pushing to selected goals, demonstrating improved agility and efficiency compared to stable pushing.
3. How to achieve efficient, real-time global trajectory optimization for contact-rich pushing: By investigating the differential flatness property of the pushing system, we simplify the pushing planning problem, significantly reducing computational complexity. This transformation allows for a simpler contact-implicit planning task that is easy to design, fast to solve, and robust to uncertainties.
4. How to perform robust state estimation through sensor fusion: We integrate data from multiple sensors and improve the robustness of the classic Kalman Filter using a deep reinforcement learning (DRL) algorithm. This novel DRL-based orientation estimation method guarantees bounded estimation errors without the need for hyperparameter tuning. Experiments demonstrate its superior performance compared to conventional methods, particularly in challenging scenarios with inaccurate initial state estimates, imprecise filter gains, and non-Gaussian noise environments.
The key contributions of this thesis include:
• A stable pushing approach that simplifies the optimization problem for nonholonomic mobile bases.
• A maneuverable free pushing method that enhances agility while maintaining contact.
• A reactive manipulation strategy leveraging differential flatness for efficient trajectory planning.
• A reinforcement learning-based approach to improve state estimation accuracy.
In conclusion, this research significantly advances the capabilities of mobile robots in handling unwieldy objects, bridging the gap between theoretical navigation planning and practical applications in complex environments. The findings pave the way for future research and broader applications of mobile robots in various domains, including logistics, search and rescue, and autonomous inspections. ...
This thesis presents a comprehensive exploration of unwieldy object delivery using mobile robots, focusing on the challenges and advancements in Navigation Among Movable Objects (NAMO). The research addresses critical issues in robotic manipulation, particularly nonprehensile techniques such as pushing, which are essential for handling objects that are difficult to grasp.
The study is structured around four key research questions guiding the investigation into effective pushing manipulation strategies:
1. How to perform pushing manipulation with limited or inaccurate state estimation: We employ the concept of “stable pushing,” ensuring the robot can always “catch” the object during delivery. The stable pushing control problem is simplified as an optimization problem with a concise linear constraint. Experiments show that this approach outperforms reactive pushing strategies, reducing the robot’s traveled distance by 23.8% and time by 77.4%.
2. How to improve pushing manipulation when accurate state estimation is available: We enhance the stable pushing approach to a more maneuverable “free pushing” method, allowing contact point changes to improve manipulation mobility. This approach achieves an average success rate of 83% with an accuracy of 0.085m when pushing to selected goals, demonstrating improved agility and efficiency compared to stable pushing.
3. How to achieve efficient, real-time global trajectory optimization for contact-rich pushing: By investigating the differential flatness property of the pushing system, we simplify the pushing planning problem, significantly reducing computational complexity. This transformation allows for a simpler contact-implicit planning task that is easy to design, fast to solve, and robust to uncertainties.
4. How to perform robust state estimation through sensor fusion: We integrate data from multiple sensors and improve the robustness of the classic Kalman Filter using a deep reinforcement learning (DRL) algorithm. This novel DRL-based orientation estimation method guarantees bounded estimation errors without the need for hyperparameter tuning. Experiments demonstrate its superior performance compared to conventional methods, particularly in challenging scenarios with inaccurate initial state estimates, imprecise filter gains, and non-Gaussian noise environments.
The key contributions of this thesis include:
• A stable pushing approach that simplifies the optimization problem for nonholonomic mobile bases.
• A maneuverable free pushing method that enhances agility while maintaining contact.
• A reactive manipulation strategy leveraging differential flatness for efficient trajectory planning.
• A reinforcement learning-based approach to improve state estimation accuracy.
In conclusion, this research significantly advances the capabilities of mobile robots in handling unwieldy objects, bridging the gap between theoretical navigation planning and practical applications in complex environments. The findings pave the way for future research and broader applications of mobile robots in various domains, including logistics, search and rescue, and autonomous inspections.
The study is structured around four key research questions guiding the investigation into effective pushing manipulation strategies:
1. How to perform pushing manipulation with limited or inaccurate state estimation: We employ the concept of “stable pushing,” ensuring the robot can always “catch” the object during delivery. The stable pushing control problem is simplified as an optimization problem with a concise linear constraint. Experiments show that this approach outperforms reactive pushing strategies, reducing the robot’s traveled distance by 23.8% and time by 77.4%.
2. How to improve pushing manipulation when accurate state estimation is available: We enhance the stable pushing approach to a more maneuverable “free pushing” method, allowing contact point changes to improve manipulation mobility. This approach achieves an average success rate of 83% with an accuracy of 0.085m when pushing to selected goals, demonstrating improved agility and efficiency compared to stable pushing.
3. How to achieve efficient, real-time global trajectory optimization for contact-rich pushing: By investigating the differential flatness property of the pushing system, we simplify the pushing planning problem, significantly reducing computational complexity. This transformation allows for a simpler contact-implicit planning task that is easy to design, fast to solve, and robust to uncertainties.
4. How to perform robust state estimation through sensor fusion: We integrate data from multiple sensors and improve the robustness of the classic Kalman Filter using a deep reinforcement learning (DRL) algorithm. This novel DRL-based orientation estimation method guarantees bounded estimation errors without the need for hyperparameter tuning. Experiments demonstrate its superior performance compared to conventional methods, particularly in challenging scenarios with inaccurate initial state estimates, imprecise filter gains, and non-Gaussian noise environments.
The key contributions of this thesis include:
• A stable pushing approach that simplifies the optimization problem for nonholonomic mobile bases.
• A maneuverable free pushing method that enhances agility while maintaining contact.
• A reactive manipulation strategy leveraging differential flatness for efficient trajectory planning.
• A reinforcement learning-based approach to improve state estimation accuracy.
In conclusion, this research significantly advances the capabilities of mobile robots in handling unwieldy objects, bridging the gap between theoretical navigation planning and practical applications in complex environments. The findings pave the way for future research and broader applications of mobile robots in various domains, including logistics, search and rescue, and autonomous inspections.
Unwieldy Object Delivery with Nonholonomic Mobile Base
A Free Pushing Approach
This letter explores the problem of delivering unwieldy objects using nonholonomic mobile bases. We propose a new approach called free pushing to address this challenge. Unlike previous stable pushing methods which maintain a stiff robot-object contact, our approach allows the robot to maneuver around the object while pushing it. It aims to execute continuous pushes without losing contact for improved pushing maneuverability. Additionally, to ensure the feasibility of the planned pushes, a robot-object contact model is developed to account for the shape and kinematics of the robot in pushing modeling and planning. A Model Predictive Controller solves the pushing planning problem in real time. Experimental results show that the proposed method achieves an average success rate of 83% with an accuracy of 0.085 m when pushing to the selected goals. Compared to the baselines, this approach improves the agility and efficiency of mobile pushers. Furthermore, it is robust in achieving the task while tolerating modeling errors.
...
This letter explores the problem of delivering unwieldy objects using nonholonomic mobile bases. We propose a new approach called free pushing to address this challenge. Unlike previous stable pushing methods which maintain a stiff robot-object contact, our approach allows the robot to maneuver around the object while pushing it. It aims to execute continuous pushes without losing contact for improved pushing maneuverability. Additionally, to ensure the feasibility of the planned pushes, a robot-object contact model is developed to account for the shape and kinematics of the robot in pushing modeling and planning. A Model Predictive Controller solves the pushing planning problem in real time. Experimental results show that the proposed method achieves an average success rate of 83% with an accuracy of 0.085 m when pushing to the selected goals. Compared to the baselines, this approach improves the agility and efficiency of mobile pushers. Furthermore, it is robust in achieving the task while tolerating modeling errors.
Unwieldy Object Delivery With Nonholonomic Mobile Base
A Stable Pushing Approach
This letter addresses the problem of pushing manipulation with nonholonomic mobile robots. Pushing is a fundamental skill that enables robots to move unwieldy objects that cannot be grasped. We propose a stable pushing method that maintains stiff contact between the robot and the object to avoid consuming repositioning actions. We prove that a line contact, rather than a single point contact, is necessary for nonholonomic robots to achieve stable pushing. We also show that the stable pushing constraint and the nonholonomic constraint of the robot can be simplified as a concise linear motion constraint. Then the pushing planning problem can be formulated as a constrained optimization problem using nonlinear model predictive control (NMPC). According to the experiments, our NMPC-based planner outperforms a reactive pushing strategy in terms of efficiency, reducing the robot's traveled distance by 23.8% and time by 77.4%. Furthermore, our method requires four fewer hyperparameters and decision variables than the Linear Time-Varying (LTV) MPC approach, making it easier to implement. Real-world experiments are carried out to validate the proposed method with two differential-drive robots, Husky and Boxer, under different friction conditions.
...
This letter addresses the problem of pushing manipulation with nonholonomic mobile robots. Pushing is a fundamental skill that enables robots to move unwieldy objects that cannot be grasped. We propose a stable pushing method that maintains stiff contact between the robot and the object to avoid consuming repositioning actions. We prove that a line contact, rather than a single point contact, is necessary for nonholonomic robots to achieve stable pushing. We also show that the stable pushing constraint and the nonholonomic constraint of the robot can be simplified as a concise linear motion constraint. Then the pushing planning problem can be formulated as a constrained optimization problem using nonlinear model predictive control (NMPC). According to the experiments, our NMPC-based planner outperforms a reactive pushing strategy in terms of efficiency, reducing the robot's traveled distance by 23.8% and time by 77.4%. Furthermore, our method requires four fewer hyperparameters and decision variables than the Linear Time-Varying (LTV) MPC approach, making it easier to implement. Real-world experiments are carried out to validate the proposed method with two differential-drive robots, Husky and Boxer, under different friction conditions.
This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. Lyapunov’s method in control theory is employed to prove the convergence of orientation estimation errors. The estimator gains and a Lyapunov function are parametrised by deep neural networks and learned from samples based on the theoretical results. The DRL estimator is compared with three well-known orientation estimation methods on both numerical simulations and real dataset collected from commercially available sensors. The results show that the proposed algorithm is superior for arbitrary estimation initialisation and can adapt to a drastic angular velocity profile for which other algorithms can be hardly applicable. To the best of our knowledge, this is the first DRL-based orientation estimation method with an estimation error boundedness guarantee.
...
This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. Lyapunov’s method in control theory is employed to prove the convergence of orientation estimation errors. The estimator gains and a Lyapunov function are parametrised by deep neural networks and learned from samples based on the theoretical results. The DRL estimator is compared with three well-known orientation estimation methods on both numerical simulations and real dataset collected from commercially available sensors. The results show that the proposed algorithm is superior for arbitrary estimation initialisation and can adapt to a drastic angular velocity profile for which other algorithms can be hardly applicable. To the best of our knowledge, this is the first DRL-based orientation estimation method with an estimation error boundedness guarantee.
Inertial measurement units are widely used in different fields to estimate the attitude. Many algorithms have been proposed to improve estimation performance. However, most of them still suffer from 1) inaccurate initial estimation, 2) inaccurate initial filter gain, and 3) non-Gaussian process and/or measurement noise. This paper will leverage reinforcement learning to compensate for the classical extended Kalman filter estimation, i.e., to learn the filter gain from the sensor measurements. We also analyse the convergence of the estimate error. The effectiveness of the proposed algorithm is validated on both simulated data and real data.
...
Inertial measurement units are widely used in different fields to estimate the attitude. Many algorithms have been proposed to improve estimation performance. However, most of them still suffer from 1) inaccurate initial estimation, 2) inaccurate initial filter gain, and 3) non-Gaussian process and/or measurement noise. This paper will leverage reinforcement learning to compensate for the classical extended Kalman filter estimation, i.e., to learn the filter gain from the sensor measurements. We also analyse the convergence of the estimate error. The effectiveness of the proposed algorithm is validated on both simulated data and real data.