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Using Multivariate Statistics

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Prospective Cohort Study

This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an ...

Randomized kinodynamic planning

Measuring Health: A Guide to Rating Scales and Questionnaires

Illustrating the power, simplicity, and generality of the concept of flatness, this reference explains how to identify, utilize, and apply flatness in system planning and design. The book includes a large assortment of exercises and models that range from elementary to complex classes of systems. Leading students and professionals through a vast array of designs, simulations, and analytical studies on the traditional uses of flatness, Differentially Flat Systems contains an extensive amount of examples that showcase the value of flatness in system design, demonstrate how flatness can be assessed in the context of perturbed systems and apply static and dynamic feedback controller design techniques.

Differentially Flat Systems

Gait training of stroke survivors is crucial to facilitate neuromuscular plasticity needed for improvements in functional walking ability. Robot assisted gait training (RAGT) was developed for stroke survivors using active leg exoskeleton (ALEX) and a force-field controller, which uses assist-as-needed paradigm for rehabilitation. In this paradigm undesirable gait motion is resisted and assistance is provided towards desired motion. The force-field controller achieves this paradigm by effectively applying forces at the ankle of the subject through actuators on the hip and knee joints. Two stroke survivors participated in a 15-session gait training study each with ALEX. The results show that by the end of the training the gait pattern of the patients improved and became closer to a healthy subject's gait pattern. Improvement is seen as an increase in the size of the patients' gait pattern, increased knee and ankle joint excursions and increase in their walking speeds on the treadmill.

Robot Assisted Gait Training With Active Leg Exoskeleton (ALEX)

In this paper, the dynamic model of a wheeled inverted pendulum (eg, Segway, Quasimoro, and Joe) is analyzed from a controllability and feedback linearizability point of view First, a dynamic model of this underactuated system is derived with respect to the wheel motor torques as inputs while taking the nonholonomic no-slip constraints into considerations This model is compared with the previous models derived for similar systems The strong accessibility condition is checked and the maximum relative degree of the system is found Based on this result, a partial feedback linearization of the system is obtained and the internal dynamics equations are isolated The resulting equations are then used to design two novel controllers The first one is a two-level velocity controller for tracking vehicle orientation and heading speed set-points, while controlling the vehicle pitch (pendulum angle from the vertical) within a specified range The second controller is also a two-level controller which stabilizes the vehicle's position to the desired point, while again keeping the pitch bounded between specified limits Simulation results are provided to show the efficacy of the controllers using realistic data

Velocity and position control of a wheeled inverted pendulum by partial feedback linearization

Rehabilitation robots are, currently, being explored for training of neural impaired subjects or for assistance of those with weak limbs. Intensive training of neurally impaired subjects, with quantifiable outcomes, is the eventual goal of these robot exoskeletons. Conventional arm exoskeletons for rehabilitation are bulky and heavy. In recent years, the authors have proposed to make lightweight exoskeletons for rehabilitation by replacing the rigid links of the exoskeleton with lightweight cuffs fixed to the moving limb segments of the human arm. Cables are routed through these cuffs, which are driven by motors, to move the limb segments relative to each other. However, a scientific limitation of a cable-driven system is that each cable can only pull but not push. This paper is the first to demonstrate via experiments with cable-driven arm exoskeleton (CAREX) that it is possible to achieve desired forces on the hand, i.e., both pull and push, in any direction as required in neural training. In this research, an anthropomorphic arm was used to bench test the design and control concepts proposed in CAREX. As described in this paper, CAREX was attached to the limb segments of a five degree-of-freedom anthropomorphic arm instrumented with joint sensors. The cuffs of CAREX were designed to have adjustable cable routing points to optimize the “tensioned” workspace of the anthropomorphic arm. Simulation results of force field for training and rehabilitation of the arm are first presented. Experiments are conducted to show the performance of a CAREX force field controller when human subjects pull the end-effector of the anthropomorphic arm to travel on prescribed paths. The human-exoskeleton interface is also presented at the end of this paper to demonstrate the feasibility of CAREX on human arm.

Design of a Cable-Driven Arm Exoskeleton (CAREX) for Neural Rehabilitation

This paper describes the design and human machine interface of an Active Leg EXoskeleton (ALEX) for gait rehabilitation of patients with walking disabilities. The paper proposes force-field controller which can apply suitable forces on the leg to help it move on a desired trajectory. The interaction forces between the subject and the orthosis are designed to be 'assist-as-needed' for safe and effective gait training. Simulations and experimental results with the force-field controller are presented. Experiments have been performed with healthy subjects walking on a treadmill. It was shown that a healthy subject could be retrained in about 45 minutes with ALEX to walk on a treadmill with a significantly altered gait. In the coming months, this powered orthosis will be used for gait training of stroke patients.

Sunil K. Agrawal

Papers

Differentially Flat Systems

Robot Assisted Gait Training With Active Leg Exoskeleton (ALEX)

Velocity and position control of a wheeled inverted pendulum by partial feedback linearization

Design of a Cable-Driven Arm Exoskeleton (CAREX) for Neural Rehabilitation

Active Leg Exoskeleton (ALEX) for Gait Rehabilitation of Motor-Impaired Patients