Vikas Bahl

Monotonically convergent iterative learning control for linear discrete-time systems

In iterative learning control schemes for linear discrete time systems, conditions to guarantee the monotonic convergence of the tracking error norms are derived. By using the Markov parameters, it is shown in the time-domain that there exists a non-increasing function such that when the properly chosen constant learning gain is multiplied by this function, the convergence of the tracking error norms is monotonic, without resort to high-gain feedback.

Learning feedforward control using a Dilated B-spline network: frequency Domain Analysis and design

This paper presents a frequency-domain analysis and design approach for a learning feedforward controller (LFFC) using a dilated B-spline network. The LFFC acts as an add-on element to the existing feedback controller (FBC). The LFFC signal is updated iteratively based on the FBC signal of the previous iteration as the task repeats. Similar to proportional-integral-derivative controller tuning, there are only two parameters to adjust: The B-spline support width and the learning gain. The effect of dilation in the B-spline network is discussed. Detailed design formulae are given based on a stability analysis. As an illustration, simulation results on the path tracking control of a wheeled mobile robot are presented.

The scalar /spl epsiv/-controller: a spatial path tracking approach for ODV, Ackerman, and differentially-steered autonomous wheeled mobile robots

Path tracking algorithms for wheeled mobile robots (WMRs) are frequently parametric in the sense that they are time-based. This has the potential of introducing lag-related errors, and is not a direct approach. A spatial path tracking control algorithm, the /spl epsiv/-controller (C/sub /spl epsiv//), is developed in this paper. It is based solely on static path geometry with position feedback. The C/sub /spl epsiv//, is applied in simulation to three different WMR steering configurations to illustrate the performance and generality of this new approach. Actual results are found to parallel the simulated results.

Visual servoing of an omni-directional mobile robot for alignment with parking lot lines

ODIS is an omni-directional mobile robot designed to autonomously or semi-autonomously inspect automobiles in a parking lot. Periodically, its position and orientation references need to be reset. This paper considers visual servoing to parking lot lines as one possible approach. Analysis and simulations demonstrate that a surprisingly simple proportional controller in the image coordinates can accomplish position and orientation alignment with parking lot lines. Unlike previous work, no image Jacobian matrix is necessary. Knowledge of the camera focal length is not required, but the camera and vehicle axes are assumed to be aligned, and the vehicle is assumed to rotate about the camera frames y-axis.

Utah State University's T2 ODV mobility analysis

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.

Improved path following of USU ODIS by learning feedforward controller using dilated B-spline network

A learning feedforward controller (LFFC) using a dilated B-splines network (BSN) is proposed in this paper. The LFFC acts as an add-on element to the existing feedback controller (FBC) for control performance enhancement. The LFFC signal is updated iteratively based on the FBC signal of previous iteration as the task repeats. In the LFFC approach, there are two parameters to tune: the B-spline support width and the learning gain. A frequency domain design approach is presented with detailed design formulae for dilation 2. Simulation results are presented for the path following control of the USU ODIS robot (omnidirectional inspection systems), a new family member of the Utah State University (USU) ODVs (Omni Directional Vehicles).

Spatial integration for a nonlinear path tracking control law

A nonlinear spatial path tracking control law, called the /spl epsi/-controller (C/sub /spl epsi//), was developed for autonomous ground vehicles (AGVs) such as the USU T-series and ODIS (omni-directional inspection system) wheeled mobile robots (WMRs). It is essentially a SISO regulator operating on the normal spatial deviation, /spl epsi/, of the robot from the desired path. As our performance expectation of C/sub /spl epsi// is entirely spatial a logical choice of the regulator should avoid any reference to time. We present a spatial integrator PI (SI-PI) regulator for C/sub /spl epsi// which is devoid of any time references while its structure includes some of the robot dynamics. It is also shown that the control action taken by this regulator represents the work done in moving the robot through an error vector field. The regulator gains are designed using a validated nonlinear Simulink/spl trade//StateFlow model. The experimental results presented show the effectiveness of the proposed SI-PI regulation scheme in the path tracking control of the ODIS WMR.

Intelligent behavior generator for autonomous mobile robots using planning-based AI decision making and supervisory control logic

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 Commands (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 systems 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.

WIRELESS VISUAL SERVOING FOR ODIS – AN UNDER CAR INSPECTION MOBILE ROBOT

Abstract This paper presents a simple robot localization technique using the wireless visual servoing technique involved in an autonomous ground vehicle ODIS (omnidirectional inspection system) for under-car inspection tasks in standard parking lot environment. Based on the current architecture of ODIS and the dedicated scripting language, an iterative visual servoing scheme is proposed to align the yellow line of the parking lot. The iterative scheme tolerates the uncertain time delay due to the wireless connections without introducing stability problem due to time-varying delay in real-time visual servoing. Experimental results are presented to show that for our specific application, the wireless visual servoing technique presented in this paper is an efficient way for robot localization.

Iterative learning control technique for mobile robot path-tracking control

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.