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Advanced driver-assistance systems (ADAS) play a significant role in reducing traffic fatalities and vehicular accidents. Among the many ADAS technologies is the blind spot alert monitor alerts you of a vehicle or an obstruction before you change lanes. This paper investigates extending the monitoring concept to detect if a car emerging from the blind spot is attempting to cut into your lane. In that respect, we would perceive the direction of the infringing vehicle. There are various means of sensing such a situation, including radar, lidar, camera, and sonar. We focus on using lidar and deep learning for this purpose in this paper. In particular, we present the experiments carried out to verify the concept, including details of the equipment, experimental data, and MATLAB Deep Learning for detection.

One of the major reasons of road related accidents is driver distraction. The aim of this study is to classify driver attention level which can be extended to improving driver warning systems by generating adaptive driver alert warning systems. In this paper logistic regression (LR) and support vector machine (SVM) classifiers are used to classify driver attention level. The performance of mentioned classifiers is illustrated and compared via the figures of predicted decision boundaries. Also, in comparison to LR, higher accuracy of SVM has been verified.

Traditional tractor ground leveling operation applies a manual process with no electronic assistance. Automated Ground Leveling (AGL) will increase quality of leveling and operator comfort. This paper outlines a machine learning approach using Artificial Neural Network (ANN). The proposed AGL uses tractor inclined angle and leveling error as inputs. The target output is the tractor scraper implement raise or lower command. The equations to run simulations are formulated and applied to the model and verified during simulations. The details can be found in section IV of this paper. John Deere StarFire 6000 GPS receiver is proposed to be the device to obtain latitude, longitude, altitude and an IMU device to obtain pitch/angling data of tractor. The proposed inputs and target output proved to be effective in producing a set of weights and biases that learns to control the scraper implement. Twenty (20) ANN trainings were conducted using the same set of training data. Out of the twenty trainings, three sets of trained weights and biases outperformed the training set. The best trained weights and biases produced an RMS error of 0.50449 compared to human training data RMS error of 0.593, which was about 14.9% improvement. The algorithm recognizes the goal of staying close to the ground reference line. This paper provides a brief review on ANN for clarity and applies it to the AGL.

A novel approach for predicting collision warning levels using a classifier that interprets a window of present and past relative positions between the ego vehicle and front object is considered. The premise assumes that the ego vehicle is equipped with a LiDAR, radar or camera that measures the relative distance. A machine learning approach is presented where the feature is the time-to-collision (TTC) which can be determined from Kalman filter estimate of distance, speed and acceleration. The classifier produces classes of warning labels that combines alert levels with watch levels. First part of the classifier called predictor classifier 1, produces the alert levels by evaluating the TTC value of current instance; however, the second part of the classifier called predictor classifier 2, produces the watch labels by applying a novel mathematical algorithm that interprets past values of TTC in a given window. Alert and watch labels are merged together by warning labels generator to produce the final labels for the dataset. This paper presents the formulation and simulation results for the machine learning approach that utilizes neural networks. A future extension of the result will address a deep learning approach where the Kalman filtering & TTC features will be eliminated.

An agile process is a frequent redesign technique that is suitable for quick adaptation of various tasks, in contrast to a more time-consuming high-level design. In this paper, we describe the concept of an agile development process to convert a by-wire electric vehicle to meet Intelligent Ground Vehicle Competition (IGVC) Self-Drive Challenges. We converted a commercial electric vehicle into a basic self-driving by-wire electric vehicle within a limited development time to satisfy the rule. A key factor for enabling agile development is the Robot Operating Systems (ROS) environment for integrating multiple microcontrollers with sensor and actuator modules. The approach allows the rapid installation of hardware components implemented as nodes, and rapid debugging of software programs implemented as topics. Employment of ROS allows frequent reassessments that enable rapid troubleshooting and debugging. This paper illustrates how a team of eight students with three advisors were able to successfully integrate hardware and software to meet specific autonomous driving challenges in a matter of one and a half months. The advantages of the agile development process were confirmed in the team meeting the IGVC self-driving challenges.

Path planning is a key factor that determines how well a robotic vehicle performs in executing automated formations and maneuvers as in multi-vehicle platooning and self- organizing leader following with safe and graceful movements. Many types of path- planning schemes have been employed in the autonomous robotics and driving systems. In this paper, we will focus on the application of a smooth path-planning (SPP) algorithm that produces simple-to-implement robotic maneuvers. The algorithm is derived from using a well-established Lyapunov stability criterion and a clever dynamical control synthesis. We show that the SPP can be adapted to many autonomous guidance scenarios. Simulations show that the SPP resulted in autonomous behaviors similar to that parallel those of human or animal actions. The paper presents results using Matlab simulations as well as Gazebo animation. The results will provide a foundation for an implementation of SPP on actual robotic vehicles.

Electronic Brake System (EBS) is considered as one of the most complicated systems whose performance depends on the subsystems parameters. Usually these parameters are difficult to predict. Based on the task to improve the EBS performance, this article presents a mathematical modeling approach based on neuro-fuzzy network method to model a subsystem of EBS. For the model parameters identification, a neuro-fuzzy network has been implemented based on Least Square Error (LSE) and Levenberg- Marquardt Algorithm (LMA) as the optimization algorithms. Finally, the performance of identified model has been evaluated.

In this paper, a systematic approach is presented and a physical experiment substantiated for a SISO wind tunnel adaptive control system. This approach includes a two-step adaptation scheme: The first comprises a triggered system identification process and the second an adapting controller system. To verify this integrated systems engineering approach, a physical wind tunnel was constructed with an axial fan and airspeed sensors. A real-time adapting system model, coupled with an adaptive control system is interfaced with the wind tunnel. The real-time data from the system in operation is analyzed and used to influence how the controller adapts through system identification techniques as well as PID tuning criteria.

Flocking is a term that describes the behavior of a group of birds (a “flock”) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems - ‘'Boids''- such as ground systems (robotic vehicles/ swarm robots). Each one of these systems collectively moves inside/outside of a building to reach a target. The flocking behavior is implemented on a server-based control, which processes each of the boids' properties e.g. position, speed & target. Subsequently, the server will assign the appropriate move to a specific boid. The calculated information will be used locally to control and direct the movements/flocking for each boid in the group. A simulation technique and detailed flow chart is presented. In addition to Reynolds three original rules for flocking, two other rules- targeting obstacle avoidance - are presented-. Our result shows that the obstacles' avoiding rule was utilized to ensure that the flock didn't collide with obstacles in each of the boids' paths.

This paper presents a novel approach to control the formation of a group of unmanned ground vehicles in an outdoor environment. Using Lyapunov stability analysis, a closed loop control law for a differential-drive vehicle is derived to maintain its target position in the formation. This control law is combined with an optimal control strategy to avoid obstacles. A simple fuzzy logic supervisor balances the weight each algorithm has on the output control signals by gradually allowing the obstacle avoidance to take over the steering as obstacles become close. Simulations have shown this supervised optimal control strategy to be an effective algorithm that seamlessly allows the group of vehicles to temporarily break formation to avoid obstacles.

Accurate knowledge on the true ground speed of a vehicle, if available, can be used to enhance advanced vehicle dynamics control systems such as anti-lock brake systems (ABS) and traction control systems (TCS) control schemes. This article describes a fuzzy rule-based Kalman filtering technique that employs an additional accelerometer to complement the wheel-based speed sensor, and produce a more accurate estimation of the true speed of a vehicle. Application of the fuzzy logic rule-based Kalman filter shows that better estimates of the true speed can be achieved even under significant braking skid and traction slip conditions.

Real-Time Simulation and Animation (RTSA) of dynamic control systems has been shown to significantly enhance the interpretation, understanding and evaluation of system response performance. Texas Instruments' TMS320C30 Digital Signal Processor is demonstrated to play a key role in accelerating the simulation speed and, in turn, increasing the graphics quality. The main feature of the approach we used to evaluate an active suspension system includes a multi-processor hardware structure (a PC and a DSP), software controlled interprocessor communication and shared memory, and the RTSA technique. These are discussed in the paper. In fact, the configuration outlined in this paper provides a platform for a high speed computing system. It is suitable for real-time applications such as signal processing, dynamic control and estimation, as well as graphical simulation.

A local positioning system (LPS) for tracking mobile robots using a newly developed ultra-wideband (UWB) ranging radio technology is presented in this paper. However, measured ranges from these radios often have uncertain biases and large sporadic errors due to multipath and attenuation effect. A fuzzy neighborhood tracking filtering technique was developed to deal with the range outlier problems. A progressive update trilateration filter technique is introduced. The paper then describes a UWB LPS based on triangulation or trilateration of the ranges, which is fused with other vehicle kinematics/dynamics sensors including a compass, rate gyros and wheel speed sensors. The UWB LPS was applied to navigation and guidance of an experimental autonomous mobile robot. Simulation theoretical and real-time experimental results were validated against the true results of the experiment recorded by video.

Legged Robots have tremendous mobility, but they can also be very inefficient. These inefficiencies can be due to suboptimal control schemes, among other things. If your goal is to get from point A to point B in the least amount of time, your control scheme will be different from if your goal is to get there using the least amount of energy. In this paper, we seek a balance between these extremes by looking at both efficiency and speed. We model a walking robot as a rimless wheel, and, using Pontryagin's Maximum Principle (PMP), we find an "on-off" control for the model, and describe the switching curve between these control extremes.

Using wide-angle or omnidirectional camera lenses to increase a mobile robot's field of view introduces nonlinearity in the image due to the 'fish-eye' effect. This complicates distance perception, and increases image processing overhead. Using multiple cameras avoids the fish-eye complications, but involves using more electrical and processing power to interface them to a computer. Being able to control the orientation of a single camera, both of these disadvantages are minimized while still allowing the robot to preview a wider area. In addition, controlling the orientation allows the robot to optimize its environment perception by only looking where the most useful information can be discovered. In this paper, a technique is presented that creates a two dimensional map of objects of interest surrounding a mobile robot equipped with a panning camera on a telescoping shaft. Before attempting to negotiate a difficult path planning situation, the robot takes snapshots at different camera heights and pan angles and then produces a single map of the surrounding area. Distance perception is performed by making calibration measurements of the camera and applying coordinate transformations to project the camera's findings into the vehicle's coordinate frame. To test the system, obstacles and lines were placed to form a chicane. Several snapshots were taken with different camera orientations, and the information from each were stitched together to yield a very useful map of the surrounding area for the robot to use to plan a path through the chicane.

Many robotics problems do not take the dynamics of the actuators into account in the formulation of the control solutions. The fallacy is in assuming that forces/torques can be instantaneously and accurately generated. In practice, actuator dynamics may be unknown. This paper presents a Model Reference Adaptive Controller (MRAC) for the actuators of a biped robot that mimics a human walking motion. The MRAC self-adjusts so that the actuators produce the desired torques. Lyapunov stability criterion and a rate of convergence analysis is provided. The control scheme for the biped robot is simulated on a sagittal plane to verify the MRAC scheme for the actuators. Next, the paper shows how a neural network (NN) can learn to generate its own walking gaits using successful runs from the adaptive control scheme. In this case, the NN learns to estimate and anticipate the reference commands for the gaits.

Real-time digital simulation and graphic animation limited band-width dynamic control systems can significantly enhance the interpretation and understanding of system responses. This paper outlines an efficient techniques for generating the graphics and the timing in real-time simulation and animation of the dynamic control systems using a high-speed high- performance PC-AT microcomputer. For illustration, the simulation and animation results for the adaptive computer controlled suspension of a combat vehicle and for the gun-turret stabilization and tracking control system for combat vehicle are presented.

In this paper, the controllability of a Mecanum omnidirectional vehicle (ODV) is investigated. An adaptive drive controller is developed that guides the ODV over irregular and unpredictable driving surfaces. Using sensor fusion with appropriate filtering, the ODV gets an accurate perception of the conditions it encounters and then adapts to them to robustly control its motion. Current applications of Mecanum ODVs are designed for use on smooth, regular driving surfaces, and don't actively detect the characteristics of disturbances in the terrain. The intention of this work is to take advantage of the mobility of ODVs in environments where they weren't originally intended to be used. The methods proposed in this paper were implemented in hardware on an ODV. Experimental results did not perform as designed due to incorrect assumptions and over-simplification of the system model. Future work will concentrate on developing more robust control schemes to account for the unknown nonlinear dynamics inherent in the system.

The tuning process of Fuzzy Logic Controllers (FLCs) through trial-and-error is usually a tedious and time-consuming task. The significant amount of computation imposed by current techniques used in self organized FLCs coupled with the need for a shortened development cycle in today's fast manufacturing environment, make existing methods practically obsolete. This paper introduces and demonstrates a methodology to simplify the tuning process of self organized FLC. Through identifying what will be described as core membership functions MFs, limiting the tuning process to core MFs, utilizing knowledge from domain experts, and mathematically derive other important MFs from core MFs once the tuning is completed, it will be demonstrated in this paper that the tuning process of FLCs can be greatly simplified.

Today’s advanced microcomputers offer extremely fast processing speed and vibrant high-resolution color graphics capabilities at affordable cost. By using carefully developed courseware (software used for coursework), these technologies can be used to strengthen engineering course delivery and provide laboratory innovation. This paper describes an interactive computer visualization environment in which real-time simulation and animation (RTSA) is used to significantly enhance control engineering curricula. Presented is the potential impact of the proposed environment on control engineering education with illustrative examples. The roles of this new interactive computer learning experience in control engineering curriculum are two-fold: 1) To provide a new method for delivering classroom material whereby real-world control system engineering concepts are introduced via interative RTSA, and 2) To provide an opportunity innovative laboratory assignments where students can analyze, design and modify control engineering systems via interactive RTSA. Positive acceptance and reactions have been widely received from users and audiences of RTSA programs which were incorporated in research projects and control curricula at Oakland University.

This chapter offers approaches for gauging the intelligence of a mobile robot and case studies as illustrations. The meaning of “Intelligence” varies greatly depending on the context in which the word is used. A general discussion on various perspectives for intelligence is provided. A framework consisting of an agent, an environment and a goal is provided for viewing intelligent behaviors. Three possible means of gauging performance of intelligent mobile robots are suggested. The first is a qualitative perception that is based on an extension of the Turing test for perceiving an unsuspecting UVS behavior as that of a person. The second is a quantitative measure where taskspecific intelligence performance of a smart system is evaluated. The third is the comparative scale that gauges the difficulty of challenges against the intelligent skills of humans. Two case studies, one involving a commercially available robotic floor vacuum cleaner and the other autonomous competition mobile robots, are gauged using the suggested measures. Included in this chapter is a description of a key experiment which revealed that it requires the mind of at least a four-year-old child to successfully navigate an autonomous navigation course.

Fuzzy systems are modular by definition but lack the trainability of neural networks. We will introduce a modular, neural-fuzzy system called threshold fuzzy systems (TFS). A TFS has two unique features that distinguish it from traditional fuzzy logic and neural network systems; (1) the rulebase of a TFS contains only single antecedent, single consequence rules (called a behaviorist fuzzy rulebase (BFR)) and (2) the fuzzy inference mechanism is modified to incorporate a highly structured adaptive node network (called a rule dominance network - RDN). Each rule in the BFR is a direct mapping of an input sensor to a system output. Connection nodes in the DN occur when rules in the BFR are conflicting. The nodes of the DN contain functions that are used to suppress the output of other conflicting rules in the BFR. Several different approaches to tuning the unknown parameters of the dominance function can be used; including supervisory training methods, self-organizing and evolutionary-based exploration. For the supervisory training approach, a unique gradient-matrix error back-propagation algorithm (GMEBA) has been developed and will be discussed.

We present two methods for a localization system, defined as the "angle of arrival" scheme, which computes position and heading of an autonomous vehicle system (AVS) fusing both odometry data and the measurements of the relative azimuth angles of known landmarks (in this case, reflectors of a stabilized laser/reflector system). The first method involves a combination of a geometric transformation and a recursive least squares approach with forgetting factor. The second method presented is a direct approach using variants of the Unscented Kalman filter. Both methods are examined in simulation and the results presented.

Computer visualization is an extremely useful tool for conveying engineering concepts to technical as well as non-technical audiences. Real-Time Simulation and Animation (RTSA) is a software that has proven to be very effective in presenting concepts of dynamic control systems in instructional and research laboratories. The unique feature of this recently developed software is that it graphically animates the physical motion of the control system being evaluated, instead of merely performing numerical simulation and displaying plots of the simulated results. With suitable hardware and programming, these tasks can all be accomplished in real-time, giving the visual impact of happening "live". RTSA has been successfully developed and used at Oakland University to provide an interactive computer-based learning experience for control engineering students. It enhances the control curriculum in the following ways: It provides an effective method for introducing applications and concepts of control engineering in real-world systems, and it challenges students to analyze, design and modify the control systems using the interactive features of RTSA software. Instant gratification in seeing control theory successfully applied and better understanding of control concepts have led to the wide acceptance of the RTSA by students. This paper describes the details of how one such RTSA software, entitled "Auto-Tracking Control of a Heat-Seeking Missile," becomes a hit as a laboratory teachware among the students.

Today's advanced microcomputers offer extremely fast processing speed and vibrant high-resolution color graphics capabilities at affordable cost. By using carefully developed courseware (software used for coursework), these technologies can be used to strengthen engineering course delivery and provide laboratory innovation. This paper describes an interactive computer visualization environment in which real-time simulation and animation (RTSA) is used to significantly enhance control engineering curricula. Presented is the potential impact of the proposed environment on control engineering education with illustrative examples. The roles of this new interactive computer learning experience in control engineering curriculum are two-fold: 1) To provide a new method for delivering classroom material whereby real-world control system engineering concepts are introduced via interative RTSA, and 2) To provide an opportunity innovative laboratory assignments where students can analyze, design and modify control engineering systems via interactive RTSA. Positive acceptance and reactions have been widely received from users and audiences of RTSA programs which were incorporated in research projects and control curricula at Oakland University.

This paper presents a unified treatment to the problem of optimal feedback control of discrete-time linear shift-invariant systems using state feedback, output feedback, dynamical output feedback, and observer-controller feedback. A class of generalized optimal feedback controllers is proposed as a basis for obtaining existing results as well as for deriving new results such as the interesting min-max feedback controllers. The essence of the generalized results lies in the minimization of a class of generalized performance measures subject to a flexible controller configuration. The stability aspect of the generalized optimal feedback controlled system is addressed.

A class of general structured discrete-time deterministic observers is developed. The one-step predicting or Luenberger observers and the current-update observers may be obtained from this class of general structured observers. Interesting relationships and important properties among various full-order observers are established. Optimal discrete-time observer gains are then derived, and it is shown that the optimal observers can be structually and numerically equivalent to various forms of Kalman filters.

The purpose of this paper is to utilize a class of compensated parameter identifiers for generating bias-free parameter estimates, in the presence of noise disturbance which accounts for the uncertainties in the system. A forward feedback is used to minimize the effect of noise. The observer form of state variable modeling is used to synthesize a ladder circuit whereby the compensated parameter identifiers are asymptotically stable, with added rapid convergence. A new technique is applied to derive state feedback that assures the assignment of closed-loop eigenvalues in arbitrary sectors within the unit circle.