4,568 research outputs found
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
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Model for eukaryotic tail-anchored protein binding based on the structure of Get3
The Get3 ATPase directs the delivery of tail-anchored (TA) proteins to the endoplasmic reticulum (ER). TA-proteins are characterized by having a single transmembrane helix (TM) at their extreme C terminus and include many essential proteins, such as SNAREs, apoptosis factors, and protein translocation components. These proteins cannot follow the SRP-dependent co-translational pathway that typifies most integral membrane proteins; instead, post-translationally, these proteins are recognized and bound by Get3 then delivered to the ER in the ATP dependent Get pathway. To elucidate a molecular mechanism for TA protein binding by Get3 we have determined three crystal structures in apo and ADP forms from Saccharomyces cerevisae (ScGet3-apo) and Aspergillus fumigatus (AfGet3-apo and AfGet3-ADP). Using structural information, we generated mutants to confirm important interfaces and essential residues. These results point to a model of how Get3 couples ATP hydrolysis to the binding and release of TA-proteins
Predictive Context-Based Adaptive Compliance for Interaction Control of Robot Manipulators
In classical industrial robotics, robots are concealed within structured and well-known environments performing highly-repetitive tasks. In contrast, current robotic applications require more direct interaction with humans, cooperating with them to achieve a common task and entering home scenarios. Above all, robots are leaving the world of certainty to work in dynamically-changing and unstructured environments that might be partially or completely unknown to them. In such environments, controlling the interaction forces that appear when a robot contacts a certain environment (be the environment an object or a person) is of utmost importance. Common sense suggests the need to leave the stiff industrial robots and move towards compliant and adaptive robot manipulators that resemble the properties of their biological counterpart, the human arm. This thesis focuses on creating a higher level of intelligence for active compliance control methods applied to robot manipulators. This work thus proposes an architecture for compliance regulation named Predictive Context-Based Adaptive Compliance (PCAC) which is composed of three main components operating around a 'classical' impedance controller. Inspired by biological systems, the highest-level component is a Bayesian-based context predictor that allows the robot to pre-regulate the arm compliance based on predictions about the context the robot is placed in. The robot can use the information obtained while contacting the environment to update its context predictions and, in case it is necessary, to correct in real time for wrongly predicted contexts. Thus, the predictions are used both for anticipating actions to be taken 'before' proceeding with a task as well as for applying real-time corrective measures 'during' the execution of a in order to ensure a successful performance. Additionally, this thesis investigates a second component to identify the current environment among a set of known environments. This in turn allows the robot to select the proper compliance controller. The third component of the architecture presents the use of neuroevolutionary techniques for selecting the optimal parameters of the interaction controller once a certain environment has been identified
Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller
In this study, we present a feedforward control system designed for active
gravity compensation on an upper body exoskeleton. The system utilizes only
positional data from internal motor sensors to calculate torque, employing
analytical control equations based on Newton-Euler Inverse Dynamics. Compared
to feedback control systems, the feedforward approach offers several
advantages. It eliminates the need for external torque sensors, resulting in
reduced hardware complexity and weight. Moreover, the feedforward control
exhibits a more proactive response, leading to enhanced performance. The
exoskeleton used in the experiments is lightweight and comprises 4 Degrees of
Freedom, closely mimicking human upper body kinematics and three-dimensional
range of motion. We conducted tests on both hardware and simulations of the
exoskeleton, demonstrating stable performance. The system maintained its
position over an extended period, exhibiting minimal friction and avoiding
undesired slewing
DNA cruciform arms nucleate through a correlated but non-synchronous cooperative mechanism
Inverted repeat (IR) sequences in DNA can form non-canonical cruciform
structures to relieve torsional stress. We use Monte Carlo simulations of a
recently developed coarse-grained model of DNA to demonstrate that the
nucleation of a cruciform can proceed through a cooperative mechanism. Firstly,
a twist-induced denaturation bubble must diffuse so that its midpoint is near
the centre of symmetry of the IR sequence. Secondly, bubble fluctuations must
be large enough to allow one of the arms to form a small number of hairpin
bonds. Once the first arm is partially formed, the second arm can rapidly grow
to a similar size. Because bubbles can twist back on themselves, they need
considerably fewer bases to resolve torsional stress than the final cruciform
state does. The initially stabilised cruciform therefore continues to grow,
which typically proceeds synchronously, reminiscent of the S-type mechanism of
cruciform formation. By using umbrella sampling techniques we calculate, for
different temperatures and superhelical densities, the free energy as a
function of the number of bonds in each cruciform along the correlated but
non-synchronous nucleation pathways we observed in direct simulations.Comment: 12 pages main paper + 11 pages supplementary dat
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