Motion Planning of Uncertain Ordinary Differential Equation Systems

This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems. Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs. The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space

Designing a dexterous reconfigurable packaging system for flexible automation

This paper presents a design for a reconfigurable packaging system that can handle cartons of different shape and sizes and is amenable to ever changing demands of packaging industries for perfumery and cosmetic products. The system takes structure of a multi-fingered robot hand, which can provide fine motions, and dexterous manipulation capability that may be required in a typical packaging-assembly line. The paper outlines advanced modeling and simulation undertaken to design the packaging system and discusses the experimental work carried out. The new packaging system is based on the principle of reconfigurability, that shows adaptability to simple as well as complex carton geometry. The rationale of developing such a system is presented with description of its human equivalent. The hardware and software implementations are also discussed together with directions for future research

Learning robotic milling strategies based on passive variable operational space interaction control

This paper addresses the problem of robotic cutting during disassembly of products for materials separation and recycling. Waste handling applications differ from milling in manufacturing processes, as they engender considerable variety and uncertainty in the parameters (e.g. hardness) of materials which the robot must cut. To address this challenge, we propose a learning-based approach incorporating elements of interaction control, in which the robot can adapt key parameters, such as feed rate, depth of cut, and mechanical compliance during task execution. We show how a mathematical model of cutting mechanics, embedded in a simulation environment, can be used to rapidly train the system without needing large amounts of data from physical cutting trials. The simulation approach was validated on a real robot setup based on four case study materials with varying structural and mechanical properties. We demonstrate the proposed method minimises process force and path deviations to a level similar to offline optimal planning methods, while the average time to complete a cutting task is within 25% of the optimum, at the expense of reduced volume of material removed per pass. A key advantage of our approach over similar works is that no prior knowledge about the material is required.Comment: 15 pages, 14 figures, accepted for publication in IEEE Transactions on Automation Science and Engineering (T-ASE

Extrusion-based additive manufacturing of concrete products. Revolutionizing and remodeling the construction industry

Additive manufacturing is one of the main topics of the fourth industrial revolution; defined as Industry 4.0. This technology offers several advantages related to the construction and architectural sectors; such as economic; environmental; social; and engineering benefits. The usage of concrete in additive technologies allows the development of innovative applications and complexity design in the world of construction such as buildings; housing modules; bridges; and urban and domestic furniture elements. The aim of this review was to show in detail a general panoramic of extrusion-based additive processes in the construction sector; the main advantages of using additive manufacturing with the respect to traditional manufacturing; the fundamental requirements of 3D printable material (fresh and hardened properties), and state-of-the-art aesthetic and architectural projects with functional properties