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Browsing by Author "Feng, Shumin"

Now showing 1 - 4 of 4
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    Autonomous Alignment and Docking Control for a Self-Reconfigurable Modular Mobile Robotic System
    Feng, Shumin; Liu, Yujiong; Pressgrove, Isaac; Ben-Tzvi, Pinhas (MDPI, 2024-05-20)
    This paper presents the path planning and motion control of a self-reconfigurable mobile robot system, focusing on module-to-module autonomous docking and alignment tasks. STORM, which stands for Self-configurable and Transformable Omni-Directional Robotic Modules, features a unique mode-switching ability and novel docking mechanism design. This enables the modules that make up STORM to dock with each other and form a variety configurations in or to perform a large array of tasks. The path planning and motion control presented here consists of two parallel schemes. A Lyapunov function-based precision controller is proposed to align the target docking mechanisms in a small range of the target position. Then, an optimization-based path planning algorithm is proposed to help find the fastest path and determine when to switch its locomotion mode in a much larger range. Both numerical simulations and real-world experiments were carried out to validate these proposed controllers.
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    A Collision Avoidance Method Based on Deep Reinforcement Learning
    Feng, Shumin; Sebastian, Bijo; Ben-Tzvi, Pinhas (MDPI, 2021-05-19)
    This paper set out to investigate the usefulness of solving collision avoidance problems with the help of deep reinforcement learning in an unknown environment, especially in compact spaces, such as a narrow corridor. This research aims to determine whether a deep reinforcement learning-based collision avoidance method is superior to the traditional methods, such as potential field-based methods and dynamic window approach. Besides, the proposed obstacle avoidance method was developed as one of the capabilities to enable each robot in a novel robotic system, namely the Self-reconfigurable and Transformable Omni-Directional Robotic Modules (STORM), to navigate intelligently and safely in an unknown environment. A well-conceived hardware and software architecture with features that enable further expansion and parallel development designed for the ongoing STORM projects is also presented in this work. A virtual STORM module with skid-steer kinematics was simulated in Gazebo to reduce the gap between the simulations and the real-world implementations. Moreover, comparisons among multiple training runs of the neural networks with different parameters related to balance the exploitation and exploration during the training process, as well as tests and experiments conducted in both simulation and real-world, are presented in detail. Directions for future research are also provided in the paper.
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    Design, Analysis, Planning, and Control of a Novel Modular Self-Reconfigurable Robotic System
    Feng, Shumin (Virginia Tech, 2022-01-11)
    This dissertation describes the design, analysis, planning, and control of a self-reconfigurable modular robotic system. The proposed robotic system mainly contains three major types of robotic modules: load carrier, manipulation module, and locomotion module. Each module is capable of navigation and interaction with the environment individually. In addition, the robotic system is proposed to reassemble autonomously into various configurations to perform complex tasks such as humanoid configuration to enable enhanced functionality to reconfigure into a configuration that would enable the system to cross over a ditch. A non-back drivable active docking mechanism with two Degrees of Freedom (DOFs) was designed to fit into the tracked units of the robot modules for achieveing the reconfiguration. The quantity and location of the docking mechanisms are customizable and selectable to satisfy various mission requirements and adapt to different environments. During the reconfiguration process, the target coupling mechanism of each module reconfigurable with each other autonomously. A Lyapunov function-based precision controller was developed to align the target docking mechanisms in a close range and high precision for assembling the robot modules autonomously into other configurations. Additionally, an trajectory optimization algorithm was developed to help the robot determine when to switch the locomotion modes and find the fastest path to the destination with the desired pose.
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    Mobile Robot Obstacle Avoidance based on Deep Reinforcement Learning
    Feng, Shumin (Virginia Tech, 2018)
    Obstacle avoidance is one of the core problems in the field of autonomous navigation. An obstacle avoidance approach is developed for the navigation task of a reconfigurable multi-robot system named STORM, which stands for Self-configurable and Transformable Omni-Directional Robotic Modules. Various mathematical models have been developed in previous work in this field to avoid collision for such robots. In this work, the proposed collision avoidance algorithm is trained via Deep Reinforcement Learning, which enables the robot to learn by itself from its experiences, and then fit a mathematical model by updating the parameters of a neural network. The trained neural network architecture is capable of choosing an action directly based on the input sensor data using the trained neural network architecture. A virtual STORM locomotion module was trained to explore a Gazebo simulation environment without collision, using the proposed collision avoidance strategies based on DRL. The mathematical model of the avoidance algorithm was derived from the simulation and then applied to the prototype of the locomotion module and validated via experiments. Universal software architecture was also designed for the STORM modules. The software architecture has extensible and reusable features that improve the design efficiency and enable parallel development.