This paper explores a novel system architecture of heterogeneous collaborative system for powerline inspection using human-robotic teaming. The research uses Quanser’s Autonomous QCar and QDrone to demonstrate the results. This heterogeneous powerline inspection system consists of three separately controlled elements that work collaboratively within a centralized controlled space and those individual elements are as follows: 1) A control system operated by the human that takes cares of the Autonomous car and drone functionality, 2) An autonomous unmanned ground vehicle system that will take tasks from the human control system to drive through the path determined, and 3) An autonomous unmanned aerial vehicle system that conducts the powerline search and inspection within a specified range. All three systems operate together as one heterogeneous system, which demonstrates the application of human-robotic teaming. This paper will demonstrate how the proposed system architecture is constructed and the results obtained from it in an indoor environment is discussed.
Adaptive Trajectory Tracking for Car-like Vehicles with Input Constraints
This paper proposes an adaptive trajectory tracking control scheme for low-speed car-like vehicles with less efforts in tuning of the control gains. An interesting way of integrating adaptive control gains with consideration of steering saturation by using the backstepping technique is designed to enhance trajectory tracking while ensuring the commanded inputs within the input boundaries. The design of such adaptive control gains is also based on enhancing the convergence rate of tracking errors, especially for lateral deviation from the reference trajectory. It is further theoretically proven that, even under the influence of steering saturation, the proposed controller can make the closed-loop system approximately globally asymptotically stable at zero errors. Comparative MATLAB/ Simulink simulations and experimental tests based on Quanser latest self-driving car (QCar) have been conducted to verify the effectiveness of the proposed control scheme in accurate tracking without violating the input constraints.
Navigation of a Self-Driving Vehicle Using One Fiducial Marker
Navigation using only one marker, which contains four artificial features, is a challenging task since camera pose estimation using only four coplanar points suffers from the rotational ambiguity problem in a real-world application. This paper presents a framework of vision-based navigation for a self-driving vehicle equipped with multiple cameras and a wheel odometer. A multiple camera setup is presented for the camera cluster which has 360-degree vision such that our framework solely requires one planar marker. A Kalman-Filter-based fusion method is introduced for the multiple-camera and wheel odometry. Furthermore, an algorithm is proposed to resolve the rotational ambiguity problem using the prediction of the Kalman Filter as additional information. Finally, the lateral and longitudinal controllers are provided. Experiments are conducted to illustrate the effectiveness of the theory.
The importance of equation η = μn2 in dimensional analysis and scaled vehicle experiments in vehicle dynamics
Dimensional analysis has been very helpful in experimentation of very large or small-scale engineering systems. A good example would be experimentation on the aircrafts and ships, which was made cost-effective and simple by dimensional analysis. The history of dimensional analysis mentioned in the introduction section of the present document includes many of such applications. Automotive industry, however, never felt the need as the price or the size of land vehicles did not make experimentation so far-fetched; therefore, there are many crash tests which every new vehicle has to go through before mass production This changed with the imminent introduction of autonomous vehicles, which brought all the risks involved in experimenting with them. Many cost-effective experimental platforms are introduced, such as QCar https://www.quanser.com/products/qcar/ or laboratories, such as Scaled Autonomous Vehicles Indoor (SAVI) https://cast.tamu.edu/research/technology-demonstrator-platforms/scaled-autonomous-vehicles-indoor-tdp/. The present study will enable the results taken from such platforms to be translated to real-sized vehicles, enabling researchers to study dynamics of various vehicles. Classical vehicle equations of motion including constant velocity, accelerating bicycle model and roll model have been made dimensionless. The case of steady-state responses is also calculated in a dimensionless form. Some practical numerical examples are also mentioned as a proof of theory.
Sorry, no results match your search criteria.
Request A Quote
Request A Demo
Find A Distributor
Request Instructor Resources
QUARC Trial License Request_2
QUARC Trial License Request