Recent industrial robotics covers a broad part of the manufacturing spectrum and other human everyday life applications; the performance of these devices has become increasingly important. ...Positioning accuracy and repeatability, as well as operating speed, are essential in any industrial robotics application. Robot positioning errors are complex due to the extensive combination of their sources and cannot be compensated for using conventional methods. Some robot positioning errors can be compensated for only using machine learning (ML) procedures. Reinforced machine learning increases the robot's positioning accuracy and expands its implementation capabilities. The provided methodology presents an easy and focused approach for industrial in situ robot position adjustment in real-time during production setup or readjustment cases. The scientific value of this approach is a methodology using an ML procedure without huge external datasets for the procedure and extensive computing facilities. This paper presents a deep q-learning algorithm applied to improve the positioning accuracy of an articulated KUKA youBot robot during operation. A significant improvement of the positioning accuracy was achieved approximately after 260 iterations in the online mode and initial simulation of the ML procedure.
Safety and resiliency are essential components of autonomous vehicles. In this research, we introduce ROSFI, the first robot operating system (ROS) resilience analysis methodology, to assess the ...effect of silent data corruption (SDC) on mission metrics. We use unmanned aerial vehicles (UAVs) as a case study to demonstrate that system-level parameters, such as flight time and success rate, are necessary for accurately measuring system resilience. We demonstrate that downstream ROS tasks such as planning and control are more susceptible to SDCs than the visual perception stage in the perception-planning-control (PPC) compute pipeline. This observation only becomes apparent when we consider the complete end-to-end system-level pipeline, as opposed to isolated compute kernels, as previous work does. To enhance the safety and robustness of robot systems bound by size, weight, and power (SWaP), we offer two low-overhead anomaly-based SDC detection and recovery algorithms based on Gaussian statistical models and autoencoder neural networks. Our anomaly error protection techniques are validated in numerous simulated environments. We demonstrate that the autoencoder-based technique can recover up to all failure cases in our studied scenarios with a computational overhead of no more than 0.0062%. Finally, our open-source methodology can be utilized to comprehensively test the robustness of other ROS-based applications. It is available for public download at https://github.com/harvard-edge/MAVBench/tree/mavfi .
This research delves into advancing an ultra-wideband (UWB) localization system through the integration of filtering technologies (moving average (MVG), Kalman filter (KF), extended Kalman filter ...(EKF)) with a low-pass filter (LPF). We investigated new approaches to enhance the precision and reduce noise of the current filtering methods-MVG, KF, and EKF. Using a TurtleBot robotic platform with a camera, our research thoroughly examines the UWB system in various trajectory situations (square, circular, and free paths with 2 m, 2.2 m, and 5 m distances). Particularly in the square path trajectory with the lowest root mean square error (RMSE) values (40.22 mm on the
axis, and 78.71 mm on the
axis), the extended Kalman filter with low-pass filter (EKF + LPF) shows notable accuracy. This filter stands out among the others. Furthermore, we find that integrated method using LPF outperforms MVG, KF, and EKF consistently, reducing the mean absolute error (MAE) to 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. This study highlights the effectiveness of EKF + LPF for accurate indoor localization for UWB systems.
From its internal representation of a building, or, its building world, a robot can retrieve a building's information for task planning and execution. However, a building is a complex construct ...composed of thousands of sub-elements, therefore manually programming the complete lifecycle information in a robot can be impractical. Our study presents the creation of a semantic building world from a building information model (BIM) that can be used for robot operations. To describe static and dynamic elements, we create a Universal Robot Description Format (URDF) building world using the Industry Foundation Classes (IFC) that a robot can directly query information for task planning and execution. As the case study demonstrates, our study bridges the gap between BIM's lifecycle information and robot operations. By allowing robots to acquire information about operating environments, the proposed methodologies provide the foundation for studies attempting task robotization in different types of facilities throughout their lifecycles.
•Presented a framework that connects BIM and robot task planning and execution.•Generated a semantic URDF-based building world representation for robots.•Achieved enhanced description of static and dynamic elements of a building.•A robot retrieved building element information for task planning and execution.•Proposed BIM-robot integration, essential for robotization in built environments.
In this article, a self-driving vehicle controller that optimizes the path a vehicle follows from its initial position to its destination is presented. The methods include clustering-based k-means, ...hierarchical, Gaussian matrix model, and self-organizing mapping. The real-time parallel implementation of the unsupervised machine learning algorithms could provide fast response times of under one microsecond during the lateral, longitudinal, and angular motion control of the autonomous vehicle. It was observed that a random selection of one of the machine learning methods may not always guarantee the optimality of the position and velocity variables as compared to the desired values. The proposed parallel implementation and optimization of the algorithms could have a significant contribution towards making transportation mobility more reliable and sustainable for future vehicular systems.
Automated Driving Systems (ADSs) require robust and scalable control systems in order to achieve a safe, efficient and comfortable driving experience. Most global planners for autonomous vehicles ...provide as output a sequence of waypoints to be followed. This paper proposes a modular and scalable waypoint tracking controller for Robot Operating System (ROS)-based autonomous guided vehicles. The proposed controller performs a smooth interpolation of the waypoints and uses optimal control techniques to ensure robust trajectory tracking even at high speeds in urban environments (up to 50 km/h). The delays in the localization system and actuators are compensated in the control loop to stabilize the system. Forward velocity is adapted to path characteristics using a velocity profiler. The controller has been implemented as an ROS package providing scalability and exportability to the system in order to be used with a wide variety of simulators and real vehicles. We show the results of this controller using the novel and hyper realistic CARLA Simulator and carrying out a comparison with other standard and state-of-art trajectory tracking controllers.
One of the major challenges in designing an autonomous agent system is to achieve the objective of recreating human-like cognition by exploiting the growing pragmatic architectures that act ...intelligently and intuitively in vital fields. Consequently, this research addresses the general problem of designing an agent-based autonomous flight control (AFC) architecture of a UAV to facilitate autonomous routing/navigation in uncharted and unascertained environments of organized foyer surroundings. The specific problem of this research is the indoor environment because of the perplexing characteristics of the required flight mechanics. We design the AFC agent architecture to consist of data acquisition, perception, localization, mapping, control, and planning modules. The AFC agent performs search and survey missions that entail commanding the UAV while performing object classifications and recognition tasks. The agent implements several image handling algorithms to detect and identify objects from their colors and shapes. It captures the video images acquired from a solitary onboard, front-facing camera which are handled off-board on a computer. We conduct tests on the AFC agent, and the results show that the agent successfully controls the UAV in three performed test cases and a total of nine implemented missions. The AFC agent detects and identifies all the assigned objects with a recall score of 1.00, a precision score of 0.9563, an accuracy score of 0.9573, an F1 score of 0.9776, an efficiency score of 0.5239, a detection total time score of 225.5 s, and an identification total time of 275 s and outperforms a human operator.
The communication system is a critical part of the system design for the autonomous Unmanned Aerial Vehicle (UAV). It has to address different considerations, including efficiency, reliability and ...mobility of the UAV. In addition, a multi-UAV system requires a communication system to assist information sharing, task allocation and collaboration in a team of UAVs. In this paper, we review communication solutions for supporting a team of UAVs while considering an application in the power line inspection industry. We provide a review of candidate wireless communication technologies for supporting communication in UAV applications. Performance measurements and UAV-related channel modeling of those candidate technologies are reviewed. A discussion of current technologies for building UAV mesh networks is presented. We then analyze the structure, interface and performance of robotic communication middleware, ROS and ROS2. Based on our review, the features and dependencies of candidate solutions in each layer of the communication system are presented.
An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A ...collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively.
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