Ernest L. Hall

The desirability and challenge of developing a completely autonomous vehicle and the rising need for more efficient use of energy by automobiles motivate this research- a study for an optimum solution to computer control of energy efficient vehicles. The purpose of this paper is to compare three control methods - mechanical, hydraulic and electric that have been used to convert an experimental all terrain vehicle to drive by wire which would eventually act as a test bed for conducting research on various technologies for autonomous operation. Computer control of basic operations in a vehicle namely steering, braking and speed control have been implemented and will be described in this paper. The output from a 3 axis motion controller is used for this purpose. The motion controller is interfaced with a software program using WSDK (Windows Servo Design Kit) as an intermediate tuning layer for tuning and parameter settings in autonomous operation. The software program is developed in C++. The voltage signal sent to the motion controller can be varied through the control program for desired results in controlling the steering motor, activating the hydraulic brakes and varying the vehicle's speed. The vehicle has been tested for its basic functionality which includes testing of street legal operations and also a 1000 mile test while running in a hybrid mode. The vehicle has also been tested for control when it is interfaced with devices such as a keyboard, joystick and sensors under full autonomous operation. The vehicle is currently being tested in various safety studies and is being used as a test bed for experiments in control courses and research studies. The significance of this research is in providing a greater understanding of conventional driving controls and the possibility of improving automobile safety by removing human error in control of a motor vehicle.

The purpose of this paper is to discuss the challenge of engineering robust intelligent robots. Robust intelligent robots may be considered as ones that not only work in one environment but rather in all types of situations and conditions. Our past work has described sensors for intelligent robots that permit adaptation to changes in the environment. We have also described the combination of these sensors with a "creative controller" that permits adaptive critic, neural network learning, and a dynamic database that permits task selection and criteria adjustment. However, the emphasis of this paper is on engineering solutions which are designed for robust operations and worst case situations such as day night cameras or rain and snow solutions. This ideal model may be compared to various approaches that have been implemented on "production vehicles and equipment" using Ethernet, CAN Bus and JAUS architectures and to modern, embedded, mobile computing architectures. Many prototype intelligent robots have been developed and demonstrated in terms of scientific feasibility but few have reached the stage of a robust engineering solution. Continual innovation and improvement are still required. The significance of this comparison is that it provides some insights that may be useful in designing future robots for various manufacturing, medical, and defense applications where robust and reliable performance is essential.

A predictive object detection algorithm was developed to investigate the practicality of using advanced filtering on stereo vision object detection algorithms such as the X-H Map. Obstacle detection with stereo vision is inherently noisy and non linear. This paper describes the X-H Map algorithm and details a method of improving the accuracy with the Unscented Kalman Filter (UKF). The significance of this work is that it details a method of stereo vision object detection and concludes that the UKF is a relevant method of filtering that improves the robustness of obstacle detection given noisy inputs. This method of integrating the UKF for use in stereo vision is suitable for any standard stereo vision algorithm that is based on pixel matching (stereo correspondence) from disparity maps.

History shows that problems that cause human confusion often lead to inventions to solve the problems, which then leads to exploitation of the invention, creating a confusion-invention-exploitation cycle. Robotics, which started as a new type of universal machine implemented with a computer controlled mechanism in the 1960's, has progressed from an Age of Over-expectation, a Time of Nightmare, an Age of Realism, and is now entering the Age of Exploitation. The purpose of this paper is to propose architecture for the modern intelligent robot in which sensors permit adaptation to changes in the environment are combined with a "creative controller" that permits adaptive critic, neural network learning, and a dynamic database that permits task selection and criteria adjustment. This ideal model may be compared to various controllers that have been implemented using Ethernet, CAN Bus and JAUS architectures and to modern, embedded, mobile computing architectures. Several prototypes and simulations are considered in view of peta-computing. The significance of this comparison is that it provides some insights that may be useful in designing future robots for various manufacturing, medical, and defense applications.

In this paper the use of support vector machines (SVM) for path planning has been investigated through a Player/Stage simulation for various case studies. SVMs are maximum margin classifiers that obtain a non-linear class boundary between the data sets. In order to apply SVM to the path planning problem, the entire obstacle course is divided in to two classes of data sets and a separating class boundary is obtained using SVM. This non-linear class boundary line determines the heading of the robot for a collision-free path. Complex obstacles and maps have been created in the simulation environment of Player/Stage. The effectiveness of SVM for path planning on unknown tracks has been studied and the results have been presented. For the classification of newly detected data points in the unknown environment, the k-nearest neighbors algorithm has been studied and implemented.

This paper describes an approach that uses support vector machines (SVM) for path planning of mobile robots. The algorithm generates a collision free path for mobile robots running between two tracks or moving towards a known way point. This approach can negotiate tracks and avoid obstacles which may be initially unknown but are later perceived by the robot, and hence is suitable for use with onboard sensors which provides local information. The approach involves dividing the whole terrain into two different classes, classifying any new point obtained from sensors into either of the classes, and generating a track between both the classes as a path of the robot. SVM generates a non-linear class boundary on the principle of maximizing the margin. The boundary generated by this method is smooth, free of obstacles, and safe for a robot to navigate. The paper presents various case studies and simulation results. Future possibility to integrate this technique with other path planning techniques is also discussed.

The purpose of this paper is to introduce a concept of eclecticism for the design, development, simulation and implementation of a real time controller for an intelligent, vision guided robots. The use of an eclectic perceptual, creative controller that can select its own tasks and perform autonomous operations is illustrated. This eclectic controller is a new paradigm for robot controllers and is an attempt to simplify the application of intelligent machines in general and robots in particular. The idea is to uses a task control center and dynamic programming approach. However, the information required for an optimal solution may only partially reside in a dynamic database so that some tasks are impossible to accomplish. So a decision must be made about the feasibility of a solution to a task before the task is attempted. Even when tasks are feasible, an iterative learning approach may be required. The learning could go on forever. The dynamic database stores both global environmental information and local information including the kinematic and dynamic models of the intelligent robot. The kinematic model is very useful for position control and simulations. However, models of the dynamics of the manipulators are needed for tracking control of the robot's motions. Such models are also necessary for sizing the actuators, tuning the controller, and achieving superior performance. Simulations of various control designs are shown. Much of the model has also been used for the actual prototype Bearcat Cub mobile robot. This vision guided robot was designed for the Intelligent Ground Vehicle Contest. A novel feature of the proposed approach lies in the fact that it is applicable to both robot arm manipulators and mobile robots such as wheeled mobile robots. This generality should encourage the development of more mobile robots with manipulator capability since both models can be easily stored in the dynamic database. The multi task controller also permits wide applications. The use of manipulators and mobile bases with a high-level control are potentially useful for space exploration, certain rescue robots, defense robots, medical robotics, and robots that aids older people in daily living activities.

This paper presents a modified virtual force based obstacle avoidance approach suited for laser range finder. The modified method takes advantage of the polar coordinate based data sent by the laser sensor by mapping the environment in a polar coordinate system. The method also utilizes a Gaussian function based certainty values to detect obstacle. The method successfully navigates through complex obstacles and reaches target GPS waypoints.

This paper describes a methodology for creative learning that applies to man and machines. Creative learning is a general approach used to solve optimal control problems. The creative controller for intelligent machines integrates a dynamic database and a task control center into the adaptive critic learning model. The task control center can function as a command center to decompose tasks into sub-tasks with different dynamic models and criteria functions, while the dynamic database can act as an information system. To illustrate the theory of creative control, several experimental simulations for robot arm manipulators and mobile wheeled vehicles were included. The simulation results showed that the best performance was obtained by using adaptive critic controller among all other controllers. By changing the paths of the robot arm manipulator in the simulation, it was demonstrated that the learning component of the creative controller was adapted to a new set of criteria. The Bearcat Cub robot was another experimental example used for testing the creative control learning. The significance of this research is to generalize the adaptive control theory in a direction toward highest level of human learning - imagination. In doing this it is hoped to better understand the adaptive learning theory and move forward to develop more human-intelligence-like components and capabilities into the intelligent robot. It is also hoped that a greater understanding of machine learning will motivate similar studies to improve human learning.

Authors: Souma Alhaj Ali, Masoud Ghaffari, Xiaoqun Liao and Ernest Hall