Adaptive Sliding Mode Control of Mobile Manipulators with Markovian Switching Joints

The hybrid joints of manipulators can be switched to either active (actuated) or passive (underactuated) mode as needed. Consider the property of hybrid joints, the system switches stochastically between active and passive systems, and the dynamics of the jump system cannot stay on each trajectory errors region of subsystems forever; therefore, it is difficult to determine whether the closed-loop system is stochastically stable. In this paper, we consider stochastic stability and sliding mode control for mobile manipulators using stochastic jumps switching joints. Adaptive parameter techniques are adopted to cope with the effect of Markovian switching and nonlinear dynamics uncertainty and follow the desired trajectory for wheeled mobile manipulators. The resulting closed-loop system is bounded in probability and the effect due to the external disturbance on the tracking errors can be attenuated to any preassigned level. It has been shown that the adaptive control problem for the Markovian jump nonlinear systems is solvable if a set of coupled linear matrix inequalities (LMIs) have solutions. Finally, a numerical example is given to show the potential of the proposed techniques

An optimization-based formalism for shared autonomy in dynamic environments

Teleoperation is an integral component of various industrial processes. For example, concrete spraying, assisted welding, plastering, inspection, and maintenance. Often these systems implement direct control that maps interface signals onto robot motions. Successful completion of tasks typically requires high levels of manual dexterity and cognitive load. In addition, the operator is often present nearby dangerous machinery. Consequently, safety is of critical importance and training is expensive and prolonged -- in some cases taking several months or even years. An autonomous robot replacement would be an ideal solution since the human could be removed from danger and training costs significantly reduced. However, this is currently not possible due to the complexity and unpredictability of the environments, and the levels of situational and contextual awareness required to successfully complete these tasks. In this thesis, the limitations of direct control are addressed by developing methods for shared autonomy. A shared autonomous approach combines human input with autonomy to generate optimal robot motions. The approach taken in this thesis is to formulate shared autonomy within an optimization framework that finds optimized states and controls by minimizing a cost function, modeling task objectives, given a set of (changing) physical and operational constraints. Online shared autonomy requires the human to be continuously interacting with the system via an interface (akin to direct control). The key challenges addressed in this thesis are: 1) ensuring computational feasibility (such a method should be able to find solutions fast enough to achieve a sampling frequency bound below by 40Hz), 2) being reactive to changes in the environment and operator intention, 3) knowing how to appropriately blend operator input and autonomy, and 4) allowing the operator to supply input in an intuitive manner that is conducive to high task performance. Various operator interfaces are investigated with regards to the control space, called a mode of teleoperation. Extensive evaluations were carried out to determine for which modes are most intuitive and lead to highest performance in target acquisition tasks (e.g. spraying/welding/etc). Our performance metrics quantified task difficulty based on Fitts' law, as well as a measure of how well constraints affecting the task performance were met. The experimental evaluations indicate that higher performance is achieved when humans submit commands in low-dimensional task spaces as opposed to joint space manipulations. In addition, our multivariate analysis indicated that those with regular exposure to computer games achieved higher performance. Shared autonomy aims to relieve human operators of the burden of precise motor control, tracking, and localization. An optimization-based representation for shared autonomy in dynamic environments was developed. Real-time tractability is ensured by modulating the human input with information of the changing environment within the same task space, instead of adding it to the optimization cost or constraints. The method was illustrated with two real world applications: grasping objects in cluttered environments and spraying tasks requiring sprayed linings with greater homogeneity. Maintaining motion patterns -- referred to as skills -- is often an integral part of teleoperation for various industrial processes (e.g. spraying, welding, plastering). We develop a novel model-based shared autonomous framework for incorporating the notion of skill assistance to aid operators to sustain these motion patterns whilst adhering to environment constraints. In order to achieve computational feasibility, we introduce a novel parameterization for state and control that combines skill and underlying trajectory models, leveraging a special type of curve known as Clothoids. This new parameterization allows for efficient computation of skill-based short term horizon plans, enabling the use of a model predictive control loop. Our hardware realization validates the effectiveness of our method to recognize a change of intended skill, and showing an improved quality of output motion, even under dynamically changing obstacles. In addition, extensions of the work to supervisory control are described. An exploratory study presents an approach that improves computational feasibility for complex tasks with minimal interactive effort on the part of the human. Adaptations are theorized which might allow such a method to be applicable and beneficial to high degree of freedom systems. Finally, a system developed in our lab is described that implements sliding autonomy and shown to complete multi-objective tasks in complex environments with minimal interaction from the human

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version