Off Road Autonomous Vehicle Modeling and Repeatability Using Real World Telemetry via Simulation

One approach to autonomous control of high mobility ground vehicle platforms operating on challenging terrain is with the use of predictive simulation. Using a simulated or virtual world, an autonomous system can optimize use of its control systems by predicting interaction between the vehicle and ground as well as the vehicle actuator state. Such a simulation allows the platform to assess multiple possible scenarios before attempting to execute a path. Physically realistic simulations covering all of these domains are currently computationally expensive, and are unable to provide fast execution times when assessing each individual scenario due to the use of high simulation frequencies (\u3e 1000Hz). This work evaluates using an Unreal Engine 4 vehicle model and virtual environment, leveraging its underlying PhysX library to build a simple unmanned vehicle platform. The simulation is demonstrated to run at low simulation frequencies (\u3c 1000Hz) when performing multiple off road driving maneuvers. Real world path telemetry is used as input to drive the unmanned vehicle\u27s integrated Pure Pursuit and PID autonomous driving control algorithms within the simulation. Cross-track-error and vehicle heading error between the simulation and real world telemetry is then observed after each maneuver\u27s execution. It is concluded after running multiple different vehicle maneuvers in real time at low simulation frequencies, a lower threshold frequency of 190Hz was shown to reliably control the virtual vehicle model with minimal average cross-track-error and heading angle deviation. Higher simulation frequencies approaching 400Hz, the recorded sampling frequency of the real world telemetry for each maneuver, had little change in system performance. Setting the simulation to execute at lower frequencies \u3c 190Hz resulted in a point of exponential increase in both the overall average cross-track-error and heading error. Additional simulation failures were also observed when setting the AV to travel at higher velocities with set simulation frequencies \u3c 190Hz

Autonomous Mobile Robot Motion Control for Hospital Disinfection

As our society develops, robotic systems become indispensable. In producing automation can be used for scanning the impure areas in hospital.  In this article MATLAB's "Robotics System Toolbox," to simulate robot navigation. This essay attempts to demonstrate the efficacy of two path planning techniques: pure-pursuit and the probabilistic roadmap (PRM). To compare the performances of four maps, whose difficulty was gradually increased. For PRM, the number of nodes was first set after the map had been loaded. Initial and final positions were then established. Following that, the program built a possible network of links between the nodes at the start and goal locations. Finally, the algorithm analyzed this network of connected nodes to return a collision-free path. In pure-pursuit, the algorithm's main goal is to select a suitable look-ahead distance. In its most basic form, The Pure Pursuit algorithm examines the difference in heading between the current vehicle and the objective point along the course. It is a proportional controller. The effectiveness of the Pure Pursuit algorithm implementations was tested in a variety of situations. The algorithm used in all of the tests followed a straight path between high level waypoints. It's necessary to keep in mind that PRM path position was the only navigation sensor employed in these studies when analyzing their results

Team MIT Urban Challenge Technical Report

This technical report describes Team MITs approach to theDARPA Urban Challenge. We have developed a novel strategy forusing many inexpensive sensors, mounted on the vehicle periphery,and calibrated with a new cross-­modal calibrationtechnique. Lidar, camera, and radar data streams are processedusing an innovative, locally smooth state representation thatprovides robust perception for real­ time autonomous control. Aresilient planning and control architecture has been developedfor driving in traffic, comprised of an innovative combination ofwell­proven algorithms for mission planning, situationalplanning, situational interpretation, and trajectory control. These innovations are being incorporated in two new roboticvehicles equipped for autonomous driving in urban environments,with extensive testing on a DARPA site visit course. Experimentalresults demonstrate all basic navigation and some basic trafficbehaviors, including unoccupied autonomous driving, lanefollowing using pure-­pursuit control and our local frameperception strategy, obstacle avoidance using kino-­dynamic RRTpath planning, U-­turns, and precedence evaluation amongst othercars at intersections using our situational interpreter. We areworking to extend these approaches to advanced navigation andtraffic scenarios

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Mode Switching Control Using Lane Keeping Assist and Waypoints Tracking for Autonomous Driving in a City Environment

This paper proposes a mode switching supervisory controller for autonomous vehicles. The supervisory controller selects the most appropriate controller based on safety constraints and on the vehicle location with respect to junctions. Autonomous steering, throttle and deceleration control inputs are used to perform variable speed lane keeping assist, standard or emergency braking and to manage junctions, including roundabouts. Adaptive model predictive control with lane keeping assist is performed on the main roads and a linear pure pursuit inspired controller is applied using waypoints at road junctions where lane keeping assist sensors present a safety risk. A multi-stage rule based autonomous braking algorithm performs stop, restart and emergency braking maneuvers. The controllers are implemented in MATLAB® and Simulink™ and are demonstrated using the Automatic Driving Toolbox™ environment. Numerical simulations of autonomous driving scenarios demonstrate the efficiency of the lane keeping assist mode on roads with curvature and the ability to accurately track waypoints at cross intersections and roundabouts using a simpler pure pursuit inspired mode. The ego vehicle also autonomously stops in time at signaled intersections or to avoid collision with other road users

Data-driven System Identification and Optimal Control Framework for Grand-Prix Style Autonomous Racing

For the past 30 years, autonomous driving has witnessed a tremendous improvements thanks to the surge of computing power. Not only did we witness the autonomous vehicle navigate itself safely in the urban area, stories about more diverse autonomous driving applications, such as off-road rally-style navigation, are also commonly mentioned. Just until recently, the exponential increase in GPU and high-performance computing technology has motivated the research on autonomous driving under extreme situations such as autonomous racing or drifting.[25] The motivation for this thesis is to offer a brief overview about the main challenge of autonomous driving control and planning in racing scenario along with the potential solutions. The first contribution is using koopmam operator and deep neural network to perform data-driven system identification. We then design optimal model-based control which is based on the learned dynamics alone. Based on our new system identification algorithm, we can approximate an accurate, explainable, and linearized system representation in a high-dimensional latent space, without any prior knowledge of the system. In this case, the learned vehicle dynamic automatically involves the information that is normally difficult to obtain, including cornering stiffness, tire slip, transmission parameters, etc. Our result shows that our koopman data-driven optimal control approach is able to deliver better tracking accuracy at high speed compared to the state-of-art vehicle controllers. The second contribution is an iterative learning and sampling algorithm that can perform minimum-time optimization of the global racing trajectory(aka racing line) within the limit of tire friction. This trajectory optimization algorithm is not only proven to be computationally efficient, but also safe enough for the onboard RC vehicle’s test. The research achievements we made for the last two years not only enables the F1TENTH racing team of Clemson University Mechanical Engineering Department to finish top 5 in both virtual autonomous racing hosted by IFAC and IROS congress, but also offer us the opportunity to join ICRA 2021 Autonomous racing workshop to present our work and being awarded the joint best paper. More importantly, these contributions proved to be functional and effective in the on-board testing of the real F1TENTH robot’s autonomous navigation in the Flour Danial basement. Finally, this thesis will also include discussions of the potential research directions that can help improve the our current method so that it can better contribute to the autonomous driving industry