Robotic arm | EPFL Graph Search

A robotic arm is a type of mechanical arm, usually programmable, with similar functions to a human arm; the arm may be the sum total of the mechanism or may be part of a more complex robot. The links of such a manipulator are connected by joints allowing either rotational motion (such as in an articulated robot) or translational (linear) displacement. The links of the manipulator can be considered to form a kinematic chain. The terminus of the kinematic chain of the manipulator is called the end effector and it is analogous to the human hand. However, the term "robotic hand" as a synonym of the robotic arm is often proscribed. Types

Cartesian robot / Gantry robot: Used for pick and place work, application of sealant, assembly operations, handling machine tools and arc welding. It is a robot whose arm has three prismatic joints, whose axes are coincident with a Cartesian coordinator.
collaborative robot / Cobot: Cobot applications contrast with traditional industrial

Arm-hand motion-force coordination for physical interactions with non-flat surfaces using dynamical systems: Toward compliant robotic massage

Aude Billard, Karl Gustav Henriks, Mahdi Khoramshahi, Seyed Sina Mirrazavi Salehian, Aileen Charlotte Naef

Many manipulation tasks require coordinated motions for arm and fingers. Complexity increases when the task requires to control for the force at contact against a non-flat surface; This becomes even more challenging when this contact is done on a human. All these challenges are regrouped when one, for instance, massages a human limb. When massaging, the robotic arm is required to continuously adapt its orientation and distance to the limb while the robot fingers exert desired patterns of forces and motion on the skin surface. To address these challenges, we adopt a Dynamical System (DS) approach that offers a unified motion-force control approach and enables to easily coordinate multiple degrees of freedom. As each human limb may slightly differ, we learn a model of the surface using support vector regression (SVR) which enable us to obtain a distance-to-surface mapping. The gradient of this mapping, along with the DS, generates the desired motions for the interaction with the surface. A DS-based impedance control for the robotic fingers allows to control separately for force along the normal direction of the surface while moving in the tangential plane. We validate our approach using the KUKA IIWA robotic arm and Allegro robotic hand for massaging a mannequin arm covered with a skin-like material.We show that our approach allows for 1) reactive motion planning to reach for an unknown surface, 2) following desired motion patterns on the surface, and 3) exerting desired interaction forces profiles. Our results show the effectiveness of our approach; especially the robustness toward uncertainties for shape and the given location of the surface.

2020

Foot-Interfaces Control and Human-Robot Interaction in Four-Handed Manipulation

Jacob Hernandez Sanchez

Humans have a great capacity to perform complex manipulations. However, there are many tasks for which two hands are not enough, such as in surgery. For such tasks, a dyad of people is required. However, when not used to work together, a dyad collaboration can be intermittent, slow and prone to misunderstandings if relied solely on verbal cues. The current PhD thesis report concerns the design and control of foot interfaces to enable solo four-handed manipulations. In the first part, we present the platform design in five DoF, with position mapping and haptic force feedback. We tackle the known issue of fatigue when using position mapping, by partially compensating for the dynamics of the leg. Finally, we perform a series of feasibility validations on bipedal teleoperation in 3 and 4 and 5 DoF. In the second part, a behavioural study investigates a symmetric bipedal holding and transport of an object while working on it with the hands. This study sheds light to the human capacity to use the redundancy given by augmentation with the feet, to assist the task of the hands. We investigate the effect of shared-control for lower limb teleoperation. Two modalities are used: 1. synergistic control of two robotic arms with one foot, 2. force assistance. We evaluate these strategies in terms of objective and subjective metrics, such as physical and cognitive workload and fluency.Aiming at enabling individualized foot dexterous control, in the third part of the thesis, we addressed the problem of bio-mechanical coupling of foot rotations. This is done by detecting intention to grasp, and selectively easing this action while haptically constraining the foot gestures for tool alignment. A user study was conducted to evaluate our proposed approach in a surgical laparoscopic gripper.In the fourth and final part, we focus on investigating more demanding tasks for the feet, as well as the coordinated control of four hands, during hybrid-robotic laparoscopic surgery. One foot controls a camera, while the other one performs a grasping task. Noteworthy is that all four-arms can be controlled simultaneously. For easing the solo surgical task, we design haptic assistance, for autonomous grasping operated by the foot. Finally, we perform a systematic behavioural study to understand the effect of our haptic-shared-control, along with an assistance modality for camera tool-tracking. We cover uni-pedal, bipedal and four-handed task scenarios. Our main conclusion is that foot gestures can be conditioned with haptic feedback, to effectively control multiple degrees of freedom of a supernumerary robotic arm. Thereby enabling four-arm simultaneous manipulations. This is done specifically by leveraging 1. a foot kinesthetic haptic platform that allows for the use of two feet, 2. leg compensation, and virtual assistive impedance, which are useful to increase comfort and alleviate fatigue. 3. Hybridization of control mappings for foot gestures, with limited range of motion and oblique axes of rotation, 4. Haptic shared-control strategies for prehensile force assistance, coordination assistance, and DoF-individuation assistance. We found that robotic assistance is highly appreciated, and has an important role in simplifying four-handed tasks. Indeed, these are instrumental considerations to enable a more fluent, less demanding, and more performant interaction, in tasks where the feet are supplementing the task of hands.

EPFL2022

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Ugo Albanese, Alessandro Ambrosano, Nino Cauli, Oliver Denninger, Stefan Deser, Egidio Falotico, Marc-Oliver Gewaltig, Luc Guyot, Georg Hinkel, Murat Kirtay, Paul Levi, Patrick Maier, Daniel Peppicelli, Igor Peric, Eduardo Ros, Stefan Ulbrich, Patrick Van Der Smagt, Lorenzo Vannucci, Axel von Arnim, Sandro Weber

Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain-body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 "Neurorobotics" of the Human Brain Project (HBP). 1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.

2017

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

2017

Foot-Interfaces Control and Human-Robot Interaction in Four-Handed Manipulation

Jacob Hernandez Sanchez

EPFL2022