Unwieldy Object Delivery with Mobile Robots

Abstract

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.