Task-space dynamic control of underwater robots

This thesis is concerned with the control aspects for underwater tasks performed by marine robots. The mathematical models of an underwater vehicle and an underwater vehicle with an onboard manipulator are discussed together with their associated properties. The task-space regulation problem for an underwater vehicle is addressed where the desired target is commonly specified as a point. A new control technique is proposed where the multiple targets are defined as sub-regions. A fuzzy technique is used to handle these multiple sub-region criteria effectively. Due to the unknown gravitational and buoyancy forces, an adaptive term is adopted in the proposed controller. An extension to a region boundary-based control law is then proposed for an underwater vehicle to illustrate the flexibility of the region reaching concept. In this novel controller, a desired target is defined as a boundary instead of a point or region. For a mapping of the uncertain restoring forces, a least-squares estimation algorithm and the inverse Jacobian matrix are utilised in the adaptive control law. To realise a new tracking control concept for a kinematically redundant robot, subregion tracking control schemes with a sub-tasks objective are developed for a UVMS. In this concept, the desired objective is specified as a moving sub-region instead of a trajectory. In addition, due to the system being kinematically redundant, the controller also enables the use of self-motion of the system to perform sub-tasks (drag minimisation, obstacle avoidance, manipulability and avoidance of mechanical joint limits)

Implementation and testing of a CAM postprocessor for an industrial redundant workcell with evaluation of several fuzzified Redundancy Resolution Schemes

This paper describes the implementation of a postprocessor to adapt the toolpath generated by a CAM system (NXTM) to a complex workcell of eight joints (namely, a KUKA KR15/2 manipulator mounted on a linear track and synchronized with a rotary table), devoted to the rapid prototyping of 3D CAD-defined products. Previously, it evaluates several Redundancy Resolution Schemes at the joint-rate level for the configuration of the postprocessor, dealing not only with the additional joints but also with the redundancy due to the symmetry on the milling tool. The use of these redundancies is optimized by adjusting two performance criterion vectors related to both singularity avoidance and maintenance of a preferred reference posture, as secondary tasks to be done during the path tracking. In addition, two proper fuzzy inference engines actively adjust the weight of each joint in these tasks. The postprocessor is validated in a real prototyping of a Valencian Falla.This research is partially supported by the Technical University of Valencia (PAID-00-09), project PROMETEO 2009/063 of Generalitat Valenciana and research project DPI2009-14744-C03-01 of the Spanish Government.Andrés De La Esperanza, FJ.; Gracia Calandin, LI.; Tornero Montserrat, J. (2012). Implementation and testing of a CAM postprocessor for an industrial redundant workcell with evaluation of several fuzzified Redundancy Resolution Schemes. Robotics and Computer-Integrated Manufacturing. 28(2):265-274. https://doi.org/10.1016/j.rcim.2011.09.008S26527428

A heuristic approach for path planning for redundant robots

A new method to solve the trajectory generation for a redundant manipulator is proposed. It avoids traditional computationally intensive methods by relying on the human experience; The proposed method uses a fuzzy logic controller to generate the magnitude of the angles needed to move the end effector to the next target point. The inputs to the controller are the desired displacement of the end effector and the elements of the jacobian matrix that correspond to the considered joint, while the output is the angle magnitude of the joint needed to reach the target point; An algorithm is used to determine the sign of the output from the fuzzy logic controller. Inverse kinematics is used to bring the end-effector to the target point; Several fuzzy logic controllers combined with heuristic algorithms are used to avoid the obstacles in the workspace and to avoid self collision of the links

An hybridization of global-local methods for autonomous mobile robot navigation in partially-known environments

This paper deals with the navigation problem of an autonomous non-holonomic mobile robot in partially-known environment. In this proposed method, the entire process of navigation is divided into two phases: an off-line phase on which a distance-optimal reference trajectory enables the mobile robot to move from an initial position to a desired target which is planned using the B-spline method and the Dijkstra algorithm. In the online phase of the navigation process, the mobile robot follows the planned trajectory using a sliding mode controller with the ability of avoiding unexpected obstacles by the use of fuzzy logic controller. Also, the fuzzy logic and fuzzy wall-following controllers are used to accomplish the reactive navigation mission (path tracking and obstacle avoidance) for a comparative purpose. Simulation results prove that the proposed path planning method (B-spline) is simple and effective. Also, they attest that the sliding mode controller track more precisely the reference trajectory than the fuzzy logic controller (in terms of time elapsed to reach the target and stability of two wheels velocity) and this last gives best results than the wall-following controller in the avoidance of unexpected obstacles. Thus, the effectiveness of our proposed approach (B-spline method combined with sliding mode and fuzzy logic controllers) is proved compared to other techniques

Obstacle Evasion Algorithm Using Convolutional Neural Networks and Kinect-V1

The following paper presents the development of an algorithm for the evasion of static obstacles during the process of gripping the desired object, using an anthropomorphic robot, artificial intelligence, and machine vision systems. The algorithm has developed to detect a variable number of obstacles (between 1 and 15) and the grip desired element, using a robot with 3 degrees of freedom (DoF). A Kinect V1 was used to capture the RGB-D information of the environment and Convolutional Neural Networks for the detection and classification of each element. The capture of the three-dimensional information of the detected objects allows comparing the distance between the obstacles and the robot, to make decisions regarding the movement of the gripper to evade elements present in the path and hold the desired object without colliding. Obstacles of less than 18 cm in height were avoided, concerning the ground, with a probability of collision of 0% under specific environmental conditions, moving the robot since initial path in a straight line to the desired object, which is prone to changes according to the obstacles present in its. Function tests have been according to the manipulator's ability to evade possible obstacles of different heights located between the robot and the desired objec

Postprocesamiento CAM-ROBOTICA orientado al prototipado y mecanizado en células robotizadas complejas

The main interest of this thesis consists of the study and implementation of postprocessors to adapt the toolpath generated by a Computer Aided Manufacturing (CAM) system to a complex robotic workcell of eight joints, devoted to the rapid prototyping of 3D CAD-defined products. It consists of a 6R industrial manipulator mounted on a linear track and synchronized with a rotary table. To accomplish this main objective, previous work is required. Each task carried out entails a methodology, objective and partial results that complement each other, namely: - It is described the architecture of the workcell in depth, at both displacement and joint-rate levels, for both direct and inverse resolutions. The conditioning of the Jacobian matrix is described as kinetostatic performance index to evaluate the vicinity to singular postures. These ones are analysed from a geometric point of view. - Prior to any machining, the additional external joints require a calibration done in situ, usually in an industrial environment. A novel Non-contact Planar Constraint Calibration method is developed to estimate the external joints configuration parameters by means of a laser displacement sensor. - A first control is originally done by means of a fuzzy inference engine at the displacement level, which is integrated within the postprocessor of the CAM software. - Several Redundancy Resolution Schemes (RRS) at the joint-rate level are compared for the configuration of the postprocessor, dealing not only with the additional joints (intrinsic redundancy) but also with the redundancy due to the symmetry on the milling tool (functional redundancy). - The use of these schemes is optimized by adjusting two performance criterion vectors related to both singularity avoidance and maintenance of a preferred reference posture, as secondary tasks to be done during the path tracking. Two innovative fuzzy inference engines actively adjust the weight of each joint in these tasks.Andrés De La Esperanza, FJ. (2011). Postprocesamiento CAM-ROBOTICA orientado al prototipado y mecanizado en células robotizadas complejas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10627Palanci

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

A model-based approach to robot kinematics and control using discrete factor graphs with belief propagation

Much of recent researches in robotics have shifted the focus from traditionally-specific industrial tasks to investigations of new types of robots with alternative ways of controlling them. In this paper, we describe the development of a generic method based on factor graphs to model robot kinematics. We focused on the kinematics aspect of robot control because it provides a fast and systematic solution for the robot agent to move in a dynamic environment. We developed neurally-inspired factor graph models that can be applied on two different robotic systems: a mobile platform and a robotic arm. We also demonstrated that we can extend the static model of the robotic arm into a dynamic model useful for imitating natural movements of a human hand. We tested our methods in a simulation environment as well as in scenarios involving real robots. The experimental results proved the flexibility of our proposed methods in terms of remodeling and learning, which enabled the modeled robot to perform reliably during the execution of given tasks