Fang Nan

Last Name

Nan

First Name

Fang

ORCID

0000-0003-4119-0886

Organisational unit

09570 - Hutter, Marco / Hutter, Marco

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Publications 1 - 7 of 7

Dynamic Throwing with Robotic Material Handling Machines
Werner, Lennart; Nan, Fang; Eyschen, Pol; et al. (2024)
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Automation of hydraulic material handling machinery is currently limited to semi-static pick-and-place cycles. Dynamic throwing motions which utilize the passive joints, can greatly improve time efficiency as well as increase the dumping workspace. In this work, we use Reinforcement Learning (RL) to design dynamic controllers for material handlers with underactuated arms as commonly used in logistics. The controllers are tested both in simulation and in real-world experiments on a 12-ton test platform. The method is able to exploit the passive joints of the gripper to perform dynamic throwing motions. With the proposed controllers, the machine is able to throw individual objects to targets outside the static reachability zone with good accuracy for its practical applications. The work demonstrates the possibility of using RL to perform highly dynamic tasks with heavy machinery, suggesting a potential for improving the efficiency and precision of autonomous material handling tasks.
Large Scale Robotic Material Handling: Learning, Planning, and Control
Spinelli, Filippo A.; Zhai, Yifan; Nan, Fang; et al. (2025)
arXiv
Bulk material handling involves the efficient and precise moving of large quantities of materials, a core operation in many industries, including cargo ship unloading, waste sorting, construction, and demolition. These repetitive, labor-intensive, and safety-critical operations are typically performed using large hydraulic material handlers equipped with underactuated grippers. In this work, we present a comprehensive framework for the autonomous execution of large-scale material handling tasks. The system integrates specialized modules for environment perception, pile attack point selection, path planning, and motion control. The main contributions of this work are two reinforcement learning-based modules: an attack point planner that selects optimal grasping locations on the material pile to maximize removal efficiency and minimize the number of scoops, and a robust trajectory following controller that addresses the precision and safety challenges associated with underactuated grippers in movement, while utilizing their free-swinging nature to release material through dynamic throwing. We validate our framework through real-world experiments on a 40 t material handler in a representative worksite, focusing on two key tasks: high-throughput bulk pile management and high-precision truck loading. Comparative evaluations against human operators demonstrate the system's effectiveness in terms of precision, repeatability, and operational safety. To the best of our knowledge, this is the first complete automation of material handling tasks on a full scale.
A reconfigurable leg for walking robots
Nan, Fang; Kolvenbach, Hendrik; Hutter, Marco (2022)
IEEE Robotics and Automation Letters
We present the design of a robotic leg that can seamlessly switch between a spring-suspended-, and unsuspended configuration. Switching is realized by a mechanism that exploits the alternative configuration of the two-link leg. The mechanism is lightweight, does not require additional actuation, and only relies on the leg movement for engagement. We validated the performance of the prototype leg on a single-leg testbed and investigated the power consumption during standing, crouching, and hopping in both configurations. The experiments showed that the efficiency of hopping and cyclic base height control is better with the spring-suspended configuration. However, it can undermine the leg's performance in position control and requires higher torque to maintain low base height, where the unsuspended configuration has advantages. Overall, the switching ability allows for seamlessly selecting the optimal mode for a specific locomotion task.
Motion Primitives Planning For Center-Articulated Vehicles
Hu, Jiangpeng; Yang, Fan; Nan, Fang; et al. (2024)
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Autonomous navigation across unstructured terrains, including forests and construction areas, faces unique challenges due to intricate obstacles and the element of the unknown. Lacking pre-existing maps, these scenarios necessitate a motion planning approach that combines agility with efficiency. Critically, it must also incorporate the robot's kinematic constraints to navigate more effectively through complex environments. This work introduces a novel planning method for center-articulated vehicles (CAV), leveraging motion primitives within a receding horizon planning framework using onboard sensing. The approach commences with the offline creation of motion primitives, generated through forward simulations that reflect the distinct kinematic model of center-articulated vehicles. These primitives undergo evaluation through a heuristic-based scoring function, facilitating the selection of the most suitable path for real-time navigation. To account for disturbances, we develop a pose-stabilizing controller, tailored to the kinematic specifications of center-articulated vehicles. During experiments, our method demonstrates a $67\%$ improvement in SPL (Success Rate weighted by Path Length) performance over existing strategies. Furthermore, its efficacy was validated through real-world experiments conducted with a tree harvester vehicle - SAHA.
Nonlinear MPC for Quadrotor Fault-Tolerant Control
Nan, Fang; Sun, Sihao; Foehn, Philipp; et al. (2022)
IEEE Robotics and Automation Letters
The mechanical simplicity, hover capabilities, and high agility of quadrotors lead to a fast adaption in the industry for inspection, exploration, and urban aerial mobility. On the other hand, the unstable and underactuated dynamics of quadrotors render them highly susceptible to system faults, especially rotor failures. In this work, we propose a fault-tolerant controller using nonlinear model predictive control (NMPC) to stabilize and control a quadrotor subjected to the complete failure of a single rotor. Differently from existing works, which either rely on linear assumptions or resort to cascaded structures neglecting input constraints in the outer-loop, our method leverages full nonlinear dynamics of the damaged quadrotor and considers the thrust constraint of each rotor. Hence, this method could effectively perform upset recovery from extreme initial conditions. Extensive simulations and real-world experiments are conducted for validation, which demonstrates that the proposed NMPC method can effectively recover the damaged quadrotor even if the failure occurs during aggressive maneuvers, such as flipping and tracking agile trajectories.
Learning Adaptive Controller for Hydraulic Machinery Automation
Nan, Fang; Hutter, Marco (2024)
IEEE Robotics and Automation Letters
The automation of hydraulic machinery has the potential to improve productivity and reduce human labor in many industries. However, the complex dynamics of hydraulic actuators, variability from machine to machine, and system degradation over time make it challenging to design controllers for hydraulic machine automation. Consequently, existing approaches rely on manual tuning and data collection. In this letter, we propose an approach to train an adaptive controller for this problem. The controller can be trained purely in simulation, and at the time of deployment, it can adapt to the dynamics of the real system within minutes. After the adaptation, precise motion control can be achieved. We validated the approach by testing a single controller trained with the proposed method on two hydraulic machines that are distinctly different in size, application, and age. The results show comparable control performance of our general approach compared to previous methods, which rely on machine-specific data and training.
Reinforcement Learning Control for Autonomous Hydraulic Material Handling Machines with Underactuated Tools
Spinelli, Filippo A.; Egli, Pascal; Nubert, Julian; et al. (2024)
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
The precise and safe control of heavy material handling machines presents numerous challenges due to the hard-to-model hydraulically actuated joints and the need for collision-free trajectory planning with a free-swinging end effector tool. In this work, we propose an RL-based controller that commands the cabin joint and the arm simultaneously. It is trained in a simulation combining data-driven modeling techniques with first-principles modeling. On the one hand, we employ a neural network model to capture the highly nonlinear dynamics of the upper carriage turn hydraulic motor, incorporating explicit pressure prediction to handle delays better. On the other hand, we model the arm as velocity-controllable and the free-swinging end-effector tool as a damped pendulum using first principles. This combined model enhances our simulation environment, enabling the training of RL controllers that can be directly transferred to the real machine. Designed to reach steady-state Cartesian targets, the RL controller learns to leverage the hydraulic dynamics to improve accuracy, maintain high speeds, and minimize end-effector tool oscillations. Our controller, tested on a mid-size prototype material handler, is more accurate than an inexperienced operator and causes fewer tool oscillations. It demonstrates competitive performance even compared to an experienced professional driver.

Publications 1 - 7 of 7

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