Initial Analysis and Development of an Automated Maintenance System for Agrivoltaics Plants

Agrivoltaics power generation is the simultaneous use of agricultural land and photovoltaic panels. In such cases, the panels are placed more sparsely, compared to conventional photovoltaic plants. Furthermore, the panels might be mounted at higher heights, to provide the required space for the farm. Due to this arrangement of the panels in wide areas with crops on the farm, maintenance of the panels is a hard task. The major part of maintenance is to clean the panels, since the accumulation of farm dust and bird excrement can reduce the efficiency of the panels. In Agrivoltaics plants, the panels can be scattered, thus it is not economical for each panel to have a dedicated cleaning robot. This paper presents the initial design and analysis of an automated system for cleaning such power plants. This system consists of a robotic AGV, which transfers a panel cleaner between the photovoltaic panels mounted over the farms. The initial design is presented. Then, to anticipate the required power and control system, the carrier kinetics and kinematics are modeled and validated through simulations. Then, position and trajectory controllers are designed and simulations are presented. The simulations show desired performance for the introduced controlled system

Grasp and stress analysis of an underactuated finger for proprioceptive tactile sensing

This paper presents the design and evaluation of a new sensorized underactuated self-adaptive finger. The design incorporates a two-degrees-of-freedom link-driven underactuated mechanism with an embedded load cell for contact force measurement and a trimmer potentiometer for acquiring joint variables. The utilization of proprioceptive (internal) sensors results in tactile-like sensations in the finger without compromising the size and complexity of the proposed design. To obtain an optimum finger design, the placement of the load cell is analyzed using finite element method. The design of the finger features a particular rounded shape of the distal phalanx and specific size ratio between the phalanxes to enable both precision and power grasps. A quantitative evaluation of the grasp efficiency by constructing a grasp wrench space is provided. The effectiveness of the proposed design is verified through experimental results that demonstrate the grasp external wrench tolerance, shape adaptability, and tactile capability. All CAD files and ROS package for the proposed underactuated design can be found on https://github.com/mahyaret

Trajectory tracking control of an aerial manipulator in presence of disturbances and modeling uncertainties

Development and dynamic validation of control techniques for trajectory tracking of a robotic manipulator mounted on a UAV. Tracking performances are evaluated in a context of simulated dynamic disturbance on manipulator base

Model learning for trajectory tracking of robot manipulators

Abstract Model based controllers have drastically improved robot performance, increasing task accuracy while reducing control effort. Nevertheless, all this was realized with a very strong assumption: the exact knowledge of the physical properties of both the robot and the environment that surrounds it. This assertion is often misleading: in fact modern robots are modeled in a very approximate way and, more important, the environment is almost never static and completely known. Also for systems very simple, such as robot manipulators, these assumptions are still too strong and must be relaxed. Many methods were developed which, exploiting previous experiences, are able to refine the nominal model: from classic identification techniques to more modern machine learning based approaches. Indeed, the topic of this thesis is the investigation of these data driven techniques in the context of robot control for trajectory tracking. In the first two chapters, preliminary knowledge is provided on both model based controllers, used in robotics to assure precise trajectory tracking, and model learning techniques. In the following three chapters, are presented the novelties introduced by the author in this context with respect to the state of the art: three works with the same premise (an inaccurate system modeling), an identical goal (accurate trajectory tracking control) but with small differences according to the specific platform of application (fully actuated, underactuated, redundant robots). In all the considered architectures, an online learning scheme has been introduced to correct the nominal feedback linearization control law. Indeed, the method has been primarily introduced in the literature to cope with fully actuated systems, showing its efficacy in the accurate tracking of joint space trajectories also with an inaccurate dynamic model. The main novelty of the technique was the use of only kinematics information, instead of torque measurements (in general very noisy), to online retrieve and compensate the dynamic mismatches. After that the method has been extended to underactuated robots. This new architecture was composed by an online learning correction of the controller, acting on the actuated part of the system (the nominal partial feedback linearization), and an offline planning phase, required to realize a dynamically feasible trajectory also for the zero dynamics of the system. The scheme was iterative: after each trial, according to the collected information, both the phases were improved and then repeated until the task achievement. Also in this case the method showed its capability, both in numerical simulations and on real experiments on a robotics platform. Eventually the method has been applied to redundant systems: differently from before, in this context the task consisted in the accurate tracking of a Cartesian end effector trajectory. In principle very similar to the fully actuated case, the presence of redundancy slowed down drastically the learning machinery convergence, worsening the performance. In order to cope with this, a redundancy resolution was proposed that, exploiting an approximation of the learning algorithm (Gaussian process regression), allowed to locally maximize the information and so select the most convenient self motion for the system; moreover, all of this was realized with just the resolution of a quadratic programming problem. Also in this case the method showed its performance, realizing an accurate online tracking while reducing both the control effort and the joints velocity, obtaining so a natural behaviour. The thesis concludes with summary considerations on the proposed approach and with possible future directions of research

Numerical methods for the inverse dynamics simulation of underactuated mechanical systems

In der vorliegenden Dissertation wird die Simulation der inversen Dynamik unteraktuierter Mehrkörpersysteme behandelt. Insbesondere werden Steuerungsprobleme der Bahnverfolgung für differentiell flache unteraktuierte Systeme untersucht. Mit Hilfe von Servobindungen werden die Steuerungsprobleme der Bahnverfolgung für unteraktuierte Systeme formuliert. Die betrachteten Probleme werden unteraktuierte Servobindungsprobleme genannt. Minimalkoordinaten, abhängige oder redundante Koordinaten werden zur Formulierung unteraktuierter Servobindungsprobleme verwendet. Die Formulierung ergibt differential-algebraische Gleichungen mit hohem Index. Die diskrete Nullraum-Methode ermöglicht den Übergang von redundanten Koordinaten zu Minimalkoordinaten. Da die numerische Lösung der differential-algebraischen Gleichungen mit hohem Index anspruchsvoll ist und die flachheitsbasierte analytische Lösung für komplizierte unteraktuierte Systeme nicht praktikabel ist, werden Methoden zur Indexreduktion vor der direkten Zeitdiskretisierung eingesetzt. Eine spezielle Projektionsmethode wird angewendet, um den Index von fünf auf drei zu reduzieren. Die Methode erfordert die Berechnung von Projektionsmatrizen, die in der redundanten Koordinaten Formulierung konstant und in der Minimalkoordinaten Formulierung zeitabhängig sind. Eine neue Methode, Indexreduktion durch minimale Erweiterung genannt, wird in dieser Dissertation entwickelt und für Servobindungsprobleme unteraktuierter Systeme verwendet. Die beiden Methoden werden auf repräsentative numerische Beispiele angewandt. Insbesondere wird schon gezeigt, dass sich die neu entwickelte Indexreduktionsmethode zur Lösung involvierter Probleme eignet, die bislang mit der Projektionsmethode nicht gelöst werden konnten

Numerical methods for the inverse dynamics simulation of underactuated mechanical systems

The present work deals with the inverse dynamics simulation of underactuated multibody systems. In particular, the study focuses on solving trajectory tracking control problems of differentially flat underactuated systems. The use of servo constraints provides an approach to formulate trajectory tracking control problems of underacutated systems, which are also called underactuated servo constraint problems

Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges

Continuum soft robots are mechanical systems entirely made of continuously deformable elements. This design solution aims to bring robots closer to invertebrate animals and soft appendices of vertebrate animals (e.g., an elephant's trunk, a monkey's tail). This work aims to introduce the control theorist perspective to this novel development in robotics. We aim to remove the barriers to entry into this field by presenting existing results and future challenges using a unified language and within a coherent framework. Indeed, the main difficulty in entering this field is the wide variability of terminology and scientific backgrounds, making it quite hard to acquire a comprehensive view on the topic. Another limiting factor is that it is not obvious where to draw a clear line between the limitations imposed by the technology not being mature yet and the challenges intrinsic to this class of robots. In this work, we argue that the intrinsic effects are the continuum or multi-body dynamics, the presence of a non-negligible elastic potential field, and the variability in sensing and actuation strategies.Comment: 69 pages, 13 figure

Sample-based motion planning in high-dimensional and differentially-constrained systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 115-124).State of the art sample-based path planning algorithms, such as the Rapidly-exploring Random Tree (RRT), have proven to be effective in path planning for systems subject to complex kinematic and geometric constraints. The performance of these algorithms, however, degrade as the dimension of the system increases. Furthermore, sample-based planners rely on distance metrics which do not work well when the system has differential constraints. Such constraints are particularly challenging in systems with non-holonomic and underactuated dynamics. This thesis develops two intelligent sampling strategies to help guide the search process. To reduce sensitivity to dimension, sampling can be done in a low-dimensional task space rather than in the high-dimensional state space. Altering the sampling strategy in this way creates a Voronoi Bias in task space, which helps to guide the search, while the RRT continues to verify trajectory feasibility in the full state space. Fast path planning is demonstrated using this approach on a 1500-link manipulator. To enable task-space biasing for underactuated systems, a hierarchical task space controller is developed by utilizing partial feedback linearization. Another sampling strategy is also presented, where the local reachability of the tree is approximated, and used to bias the search, for systems subject to differential constraints. Reachability guidance is shown to improve search performance of the RRT by an order of magnitude when planning on a pendulum and non-holonomic car. The ideas of task-space biasing and reachability guidance are then combined for demonstration of a motion planning algorithm implemented on LittleDog, a quadruped robot. The motion planning algorithm successfully planned bounding trajectories over extremely rough terrain.by Alexander C. Shkolnik.Ph.D

Parallel Manipulators

In recent years, parallel kinematics mechanisms have attracted a lot of attention from the academic and industrial communities due to potential applications not only as robot manipulators but also as machine tools. Generally, the criteria used to compare the performance of traditional serial robots and parallel robots are the workspace, the ratio between the payload and the robot mass, accuracy, and dynamic behaviour. In addition to the reduced coupling effect between joints, parallel robots bring the benefits of much higher payload-robot mass ratios, superior accuracy and greater stiffness; qualities which lead to better dynamic performance. The main drawback with parallel robots is the relatively small workspace. A great deal of research on parallel robots has been carried out worldwide, and a large number of parallel mechanism systems have been built for various applications, such as remote handling, machine tools, medical robots, simulators, micro-robots, and humanoid robots. This book opens a window to exceptional research and development work on parallel mechanisms contributed by authors from around the world. Through this window the reader can get a good view of current parallel robot research and applications