In earlier research the Center for Self-Organizing and Intelligent Systems (CSOIS) at Utah State University (USU) was funded by the US Army Tank-Automotive and Armaments Command's (TACOM) Intelligent Mobility Program to develop and demonstrate enhanced mobility concepts for unmanned ground vehicles (UGVs). As part of our research, we presented the use of a grammar-based approach to enabling intelligent behaviors in autonomous robotic vehicles. With the growth of the number of available resources on the robot, the variety of the generated behaviors and the need for parallel execution of multiple behaviors to achieve reaction also grew. As continuation of our past efforts, in this paper, we discuss the parallel execution of behaviors and the management of utilized resources. In our approach, available resources are wrapped with a layer (termed services) that synchronizes and serializes access to the underlying resources. The controlling agents (called behavior generating agents) generate behaviors to be executed via these services. The agents are prioritized and then, based on their priority and the availability of requested services, the Control Supervisor decides on a winner for the grant of access to services. Though the architecture is applicable to a variety of autonomous vehicles, we discuss its application on T4, a mid-sized autonomous vehicle developed for security applications.
As an outgrowth of series of projects focused on mobility of unmanned ground vehicles (UGV), an omni-directional (ODV), multi-robot, autonomous mobile parking security system has been developed. The system has two types of robots: the low-profile Omni-Directional Inspection System (ODIS), which can be used for under-vehicle inspections, and the mid-sized T4 robot, which serves as a ``marsupial mothership'' for the ODIS vehicles and performs coarse resolution inspection. A key task for the T4 robot is license plate recognition (LPR). For a successful LPR task without compromising the recognition rate, the robot must be able to identify the bumper locations of vehicles in the parking area and then precisely position the LPR camera relative to the bumper. This paper describes a 2D-laser scanner based approach to bumper identification and laser servoing for the T4 robot. The system uses a gimbal-mounted scanning laser. As the T4 robot travels down a row of parking stalls, data is collected from the laser every 100ms. For each parking stall in the range of the laser during the scan, the data is matched to a ``bumper box'' corresponding to where a car bumper is expected, resulting in a point cloud of data corresponding to a vehicle bumper for each stall. Next, recursive line-fitting algorithms are used to determine a line for the data in each stall's ``bumper box.'' The fitting technique uses Hough based transforms, which are robust against segmentation problems and fast enough for real-time line fitting. Once a bumper line is fitted with an acceptable confidence, the bumper location is passed to the T4 motion controller, which moves to position the LPR camera properly relative to the bumper. The paper includes examples and results that show the effectiveness of the technique, including its ability to work in real-time.
In earlier research the Center for Self-Organizing and Intelligent Systems (CSOIS) at Utah State University (USU) have been funded by the US Army Tank-Automotive and Armaments Command's (TACOM) Intelligent Mobility Program to develop and demonstrate enhanced mobility concepts for unmanned ground vehicles (UGVs). One among the several out growths of this work has been the development of a grammar-based approach to intelligent behavior generation for commanding autonomous robotic vehicles. In this paper we describe the use of this grammar for enabling autonomous behaviors. A supervisory task controller (STC) sequences high-level action commands (taken from the grammar) to be executed by the robot. It takes as input a set of goals and a partial (static) map of the environment and produces, from the grammar, a flexible script (or sequence) of the high-level commands that are to be executed by the robot. The sequence is derived by a planning function that uses a graph-based heuristic search (A* -algorithm). Each action command has specific exit conditions that are evaluated by the STC following each task completion or interruption (in the case of disturbances or new operator requests). Depending on the system's state at task completion or interruption (including updated environmental and robot sensor information), the STC invokes a reactive response. This can include sequencing the pending tasks or initiating a re-planning event, if necessary. Though applicable to a wide variety of autonomous robots, an application of this approach is demonstrated via simulations of ODIS, an omni-directional inspection system developed for security applications.
In response to ultra-high maneuverability vehicle requirements, Utah State University (USU) has developed an autonomous vehicle with unique mobility and maneuverability capabilities. This paper describes a study of the mobility of the USU T2 Omni-Directional Vehicle (ODV). The T2 vehicle is a mid-scale (625 kg), second-generation ODV mobile robot with six independently driven and steered wheel assemblies. The six wheel, independent steering system is capable of unlimited steering rotation, presenting a unique solution to enhanced vehicle mobility requirements. This mobility study focuses on energy consumption in three basic experiments, comparing two modes of steering: Ackerman and ODV. The experiments are all performed on the same vehicle without any physical changes to the vehicle itself, providing a direct comparison these two steering methodologies. A computer simulation of the T2 mechanical and control system dynamics is described.
Iterative learning control (ILC) is a technique for using repetitive operation to derive the input commands needed to force a dynamical system to follow a prescribed trajectory. In this paper we describe ideas towards the use of ILC for path-tracking control of a mobile robot. The work is focused on a novel robotic platform, the Utah State University (USU) Omni-Directional Vehicle (ODV), which features six “smart wheels,” each of which has independent control of both speed and direction. Using a validated dynamic model of the ODV robot, it is shown that ILC can be used to learn the nominal input commands needed force the robot to track a prescribed path in inertial space.
The Center for Self-Organizing and Intelligent Systems has built several vehicles with ultra-maneuverable steering capability. Each drive wheel on the vehicle can be independently set at any angle with respect to the vehicle body and the vehicles can rotate or translate in any direction. The vehicles are expected to operate on a wide range of terrain surfaces and problems arise in effectively controlling changes in wheel steering angles as the vehicle transitions from one extreme running surface to another. Controllers developed for smooth surfaces may not perform well on rough or 'sticky' surfaces and vice versa. The approach presented involves the development of a model of the steering motor with the static and viscous friction of the steering motor load included. The model parameters are then identified through a series of environmental tests using a vehicle wheel assembly and the model thus obtained is used for control law development. Four different robust controllers were developed and evaluated through simulation and vehicle testing. The findings of this development will be presented.