Learning visual docking for non-holonomic autonomous vehicles

This paper presents a new method of learning visual docking skills for non-holonomic vehicles by direct interaction with the environment. The method is based on a reinforcement algorithm, which speeds up Q-learning by applying memorybased sweeping and enforcing the “adjoining property”, a filtering mechanism to only allow transitions between states that satisfy a fixed distance. The method overcomes some limitations of reinforcement learning techniques when they are employed in applications with continuous non-linear systems, such as car-like vehicles. In particular, a good approximation to the optimal behaviour is obtained by a small look-up table. The algorithm is tested within an image-based visual servoing framework on a docking task. The training time was less than 1 hour on the real vehicle. In experiments, we show the satisfactory performance of the algorithm

Locomotion Optimization of Photoresponsive Small-scale Robot: A Deep Reinforcement Learning Approach

Soft robots comprise of elastic and flexible structures, and actuatable soft materials are often used to provide stimuli-responses, remotely controlled with different kinds of external stimuli, which is beneficial for designing small-scale devices. Among different stimuli-responsive materials, liquid crystal networks (LCNs) have gained a significant amount of attention for soft small-scale robots in the past decade being stimulated and actuated by light, which is clean energy, able to transduce energy remotely, easily available and accessible to sophisticated control. One of the persistent challenges in photoresponsive robotics is to produce controllable autonomous locomotion behavior. In this Thesis, different types of photoresponsive soft robots were used to realize light-powered locomotion, and an artificial intelligence-based approach was developed for controlling the movement. A robot tracking system, including an automatic laser steering function, was built for efficient robotic feature detection and steering the laser beam automatically to desired locations. Another robot prototype, a swimmer robot, driven by the automatically steered laser beam, showed directional movements including some degree of uncertainty and randomness in their locomotion behavior. A novel approach is developed to deal with the challenges related to the locomotion of photoresponsive swimmer robots. Machine learning, particularly deep reinforcement learning method, was applied to develop a control policy for autonomous locomotion behavior. This method can learn from its experiences by interacting with the robot and its environment without explicit knowledge of the robot structure, constituent material, and robotic mechanics. Due to the requirement of a large number of experiences to correlate the goodness of behavior control, a simulator was developed, which mimicked the uncertain and random movement behavior of the swimmer robots. This approach effectively adapted the random movement behaviors and developed an optimal control policy to reach different destination points autonomously within a simulated environment. This work has successfully taken a step towards the autonomous locomotion control of soft photoresponsive robots

Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAV's autopilot that determined its flight trajectory. Our method requires neither perfect single-view tracking nor appearance matching across views. For robustness, we allow the tracker to generate multiple detections per frame in each video. The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined during our bundle adjustment procedure. Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes demonstrates the effectiveness of our method

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Utilizing Reinforcement Learning and Computer Vision in a Pick-And-Place Operation for Sorting Objects in Motion

This master's thesis studies the implementation of advanced machine learning (ML) techniques in industrial automation systems, focusing on applying machine learning to enable and evolve autonomous sorting capabilities in robotic manipulators. In particular, Inverse Kinematics (IK) and Reinforcement Learning (RL) are investigated as methods for controlling a UR10e robotic arm for pick-and-place of moving objects on a conveyor belt within a small-scale sorting facility. A camera-based computer vision system applying YOLOv8 is used for real-time object detection and instance segmentation. Perception data is utilized to ascertain optimal grip points, specifically through an implemented algorithm that outputs optimal grip position, angle, and width. As the implemented system includes testing and evaluation on a physical system, the intricacies of hardware control, specifically the reverse engineering of an OnRobot RG6 gripper is elaborated as part of this study. The system is implemented on the Robotic Operating System (ROS), and its design is in particular driven by high modularity and scalability in mind. The camera-based vision system serves as the primary input, while the robot control is the output. The implemented system design allows for the evaluation of motion control employing both IK and RL. Computation of IK is conducted via MoveIt2, while the RL model is trained and computed in NVIDIA Isaac Sim. The high-level control of the robotic manipulator was accomplished with use of Proximal Policy Optimization (PPO). The main result of the research is a novel reward function for the pick-and-place operation that takes into account distance and orientation from the target object. In addition, the provided system administers task control by independently initializing pick-and-place operation phases for each environment. The findings demonstrate that PPO was able to significantly enhance the velocity, accuracy, and adaptability of industrial automation. Our research shows that accurate control of the robot arm can be reached by training the PPO Model purely by applying a digital twin simulation