Nearly all larger cases and the vast majority of commercial cases where our Indoor “GPS” is used are under NDAs, unfortunately. Thus, they are difficult to share. Below we list those cases that are public and we found them in the internet.
Abstract: Expert drivers have the skill to perform high side slip maneuvers, like drifting, during racing events to minimize lap time. Due to the complex dynamics of these maneuvers, transient drift is difficult to model and to control in autonomous vehicles.In this paper, the authors present a mixed open-loop and closed-loop control strategy to perform a transient drift-corning trajectory. The reference trajectory is generated from a ruled-based algorithm. We validate the planning and control techniques in simulation using Simulink/Carsim, and experimentally using an open source, low cost 1/10 scale RC car.
Keywords: Autonomous vehicles, Vehicle dynamics, Rule-based path planning, Drift control
Planning and Control of Drift Maneuvers with the Berkeley Autonomous Race Car
by Jon Matthew Gonzales
Doctor of Philosophy in Engineering – Mechanical Engineering
University of California, Berkeley
Professor Francesco Borrelli, Chair
In competitive sporting events, drivers operate vehicles at the limits of handling, with near full rear tire saturation. Expert drivers intentionally drift their vehicles around corners to turn the vehicle quickly. These drivers operate their vehicles in a way that is contrary to the way safety systems in automotive electronic control units are designed. By investigating and understanding the physics and operating principles of these maneuvers, it may be possible to enhance safety features in automotive control systems for collision avoidance, as well as enable sports cars to autonomously perform drift within the context of racing. The main focus of this dissertation is on planning and control of drift maneuvers, in particular, steady state drift, drift parking, and drift cornering.
Secondly, with the growth of research and engineering in the domain of autonomous vehicles, the dissertation also focuses on the design and development of a robotic platform called the Berkeley Autonomous Race Car (BARC). The platform is based on a 1/10-scaleremote control vehicle equipped with computing hardware and a suite of sensors that make it suitable for research and instruction. The project aims to provide a low-cost, open-source test bed option for researchers and instructors interested in autonomous vehicles. The methods and algorithms provided in the first part of the dissertation are experimentally validated on the BARC platform.
Marvelmind Robotics is a company that develops an off-the-shelf indoor GPS. Indoor localization is achieved with the use of ultrasound beacons. In this application, four stationary beacons are placed at a height of around 1 meter at all each corner of the robocup field. A fifth beacon is attached to the drone and connected to the drone’s on-board power circuit. This beacon is known as the hedgehog or hedge for short. The hedgehog is a mobile beacon whose position is tracked by the stationary beacons. Pose data of the four beacons and the hedgehog are communicated using the modem, which also provides data output via UART. In this application, the pose data for the hedgehog is accessed through the modem using a C program. This program accesses the data via UART, decrypts the pose data and broadcasts it to the MATLAB/Simulink file via UDP. All of this is executed on the Intel Nuc. The co-ordinates derived from the Marvelmind setup was mapped to the NED co-ordinate frame used by the Pixhawk.
This section explains how the on-board Pixhawk was used to fly the drone autonomously.
The actions taken by the Pixhawk are dictated by the messages it receives through the Mavlink message protocol. The Mavlink messages were generated in Matlab Simulink running on an off-board Intel NUC computer and were communicated to the Pixhawk via telemetry. The telemetry connection was established through connecting two telemetry modules, one to the telem2 port of the Pixhawk and the other to the Intel NUC computer via USB. One Mavlink message is sent and another is received in every iteration of the Matlab Simulink simulation. Each Mavlink message sent to the Pixhawk include the position estimations taken from the Marvelmind setup and a required set point. The setpoint represents the target location that the Pixhawk, and subsequently the drone, is required to move to. The Mavlink message received include information regarding the drone’s current pose and positions. This information was received to be used in planning the drone’s path by deciding upon the set points sent to the Pixhawk.
The article can found here.