The development of modern industries calls for the robotic manipulators with high speed and accurate tracking performance. Many authors have paid attention to robust control of robotic manipulators; however, only few authors have also considered the control problem of manipulators with power limitation. In this dissertation, the robotic manipulator is modeled as an uncertain system, with such uncertainties as varying moments of inertia, damping and payloads during tracking. The resulting uncertain part of the system is norm-bounded by a known constant. The total control consists of a linear part with gain matrix K, and a nonlinear part Δv, typically used for control of uncertain dynamical systems. Saturation of the resulting controller is assumed, with bounds imposed by the power limitation of actuators. It is proved at the dissertation that such a system is globally uniformly practically stable. The distribution of the control power between two controllers is discussed. It is found that when small gain matrix K is used and Δv dominates the controller, the solution to the system can approach a smaller region with faster response; that is, higher tracking accuracy is obtained. Theoretical analysis is provided to support the proposed control scheme. A two-link robotic manipulator is simulated with the results confirming the prediction. / Graduation date: 1992

Cartesian space path planning involves generating the position and orientation trajectories for a manipulator end-effector. Currently, much of the literature in motion planning for robotics concentrates on topics such as obstacle avoidance, dynamic optimizations, or high-level task planning. The focus of this research is on operator-generated motions. This will involve analytically studying the effects of higher-order properties (such as curvature and torsion) on the shape of spatial Cartesian curves. A particular emphasis will be placed on developing physical meanings and graphical visualization for these properties to aid the operator in generating geometrically complex motions. This research begins with a brief introduction to the domain of robotics and manipulator motion planning. An overview of work in the area of manipulator motion planning will demonstrate a lack of research on generating geometrically complex spatial paths. To pursue this goal, this report will then provide a review of the theory of algrebraic curves and their higher-order properties. This involves an evaluation of several different representations for both planar and spatial curves. Then, a survey of interactive curve generation techniques will be performed, which will draw from fields outside of robotics such as Computer Graphics and Computer-Aided Design (CAD). In addition to the reviewed methods, a new method for describing and generating spatial curves is proposed and demonstrated. This method begins with the study of a finite set of local geometric motion shapes (circular arcs, cusps, helices, etc). The local geometric shapes are studied in terms of their geometric parameters (curvature and torsion), analyzed to give physical meaning to these parameters, and displayed graphically as a family of curves based on these controlling parameters. This leads to the development of path constraints with well-defined physical meaning. Then, a curve generation method is developed that can convert these geometric constraints into parametric constraints and blend between them to form a complete motion program (cycle) of smooth paths connecting several carefully developed local curve properties. Up to ten distinct local curve shapes were developed in detail and one curve cycle demonstrated how all this could be combined into a full path planning scenario. Finally, the developed methods are packaged together into existing software and applied to an example demonstration. / text

Visual servoing (VS) is an automatic control technique which uses vision feedback to control the robot motion. Eye-in-hand VS systems, with the vision sensor mounted directly on the robot end-effector have received significant attention, in particular for the task of steering the vision sensor (usually a camera) from the present position to the desired one identified by image features shown in advance. The servo uses the difference between the present and the desired views (shown a priori) of some objects to develop real-time driving signals. This approach is also known as “teach-by-showing” method. To accomplish such a task, many constraints and limits are required such as camera field of view (FOV), robot joint limits, collision and occlusion avoidance, and etc. Path-planning technologies, as one branch of high-level control strategies, are explored in this thesis to impose these constraints for VS tasks with respect to different types of cameras and objects. First, a VS path-planning strategy is proposed for a class of cameras that include conventional perspective cameras, fisheye cameras, and catadioptric systems. These cameras are described by adopting a unified mathematical model and the strategy consists of designing image trajectories that allow the camera to reach the desired position while satisfying the camera FOV limit and the end-effector collision avoidance. To this end, the proposed strategy introduces the projection of the available image features onto a virtual plane and the computation of a feasible camera trajectory through polynomial programming. The computed image trajectory is hence tracked by an image-based visual servoing (IBVS) controller. Experimental results with a fisheye camera mounted on a 6-degree-of-freedom (6-DoF) robot arm illustrate the proposed strategy. Second, this thesis proposes a path-planning strategy for visual servoing with image moments, in the case of which the observed features are not restrained to points. Image moments of some solid objects such as circle, sphere, and etc. are more intuitive features than the dominant feature points in VS applications. The problem consists of planning a trajectory in order to ensure the convergence of the robot end-effector to the desired position while satisfying workspace (Cartesian space) constraints of the robot end-effector and visibility constraints of these solid objects, in particular including collision and occlusion avoidance. A solution based on polynomial parametrization is proposed and validated by some simulation and experiment results. Third, constrained optimization is combined with robot teach-by-demonstration to address simultaneously visibility constraint, joint limits and whole-arm collisions for robust vision-based control of a robot manipulator. User demonstration data generates safe regions for robot motion with respect to joint limits and potential whole-arm collisions. Constrained optimization uses these safe regions to generate new feasible trajectories under visibility constraint that achieve the desired view of the target (e.g., a pre-grasping location) in new, undemonstrated locations. To fulfill these requirements, camera trajectories that traverse a set of selected control points are modeled and optimized using either quintic Hermite splines or polynomials with C2 continuity. Experiments with a 7-DoF articulated arm validate the proposed method. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

This thesis describes the design and implementation of an extended knowledge-based modeling and simulation environment, in which the management of continuous-time models in DEVS-SCHEME is realized to meet the requirements of the modeling and simulation of a robot-managed laboratory aboard the forthcoming Space Station Freedom. The modular hierarchical modeling scheme is preserved in the continuous models by using DYMOLA, a continuous modeling language, as a bridge between the abstracted DEVS models and the continuous simulation language code (in DESIRE). Through operations on the System Entity Structure (SES), a knowledge representation scheme, models at different granularity levels are generated. Time-windows can be obtained by manipulating a pruned SES. These time windows can be used to generate an equivalent discrete-event model at a coarser granularity. Therefore, an event-based intelligent control strategy can be realized in this knowledge-based multi-facetted modeling environment. Continuous-time and discrete-event modeling and simulation can be merged with AI techniques.

An autonomous navigational system for a mobile robot was developed based on a Laser-Range-Finder-based path planning and navigational algorithms. The system was enhanced by incorporating collision avoidance algorithms using data from a sonar sensor array, and further improved by establishing two virtual regions in front of the robot for obstacle detection and avoidance. Several virtual detector bands with varying dimensions were also added to the sides of the robot to check for rotational clearance safety and to determine the direction of rotation. The autonomous navigational system was tested extensively under indoor environment. Test results showed that the system performed satisfactorily in navigating the mobile robot in three structured mazes under indoor conditions. / An artificial landmark localization algorithm was also developed to continuously record the positions of the robot whilst it was moving. The algorithm was tested on a grid layout of 6 m x 6 m. The performance of the artificial landmark localization technique was compared with odometric and inertial measurements obtained using a dead-reckoning method and a gyroscope-corrected dead-reckoning method. The artificial landmark localization method resulted in much smaller root mean square error (0.033 m) of position estimates compared to the other two methods (0.175 m and 0.135 m respectively).

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