18,640 research outputs found
A mosaic of eyes
Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties
Planning robot actions under position and shape uncertainty
Geometric uncertainty may cause various failures during the execution of a robot control program. Avoiding such failures makes it necessary to reason about the effects of uncertainty in order to implement robust strategies. Researchers first point out that a manipulation program has to be faced with two types of uncertainty: those that might be locally processed using appropriate sensor based motions, and those that require a more global processing leading to insert new sensing operations. Then, they briefly describe how they solved the two related problems in the SHARP system: how to automatically synthesize a fine motion strategy allowing the robot to progressively achieve a given assembly relation despite position uncertainty, and how to represent uncertainty and to determine the points where a given manipulation program might fail
Model-driven engineering approach to design and implementation of robot control system
In this paper we apply a model-driven engineering approach to designing
domain-specific solutions for robot control system development. We present a
case study of the complete process, including identification of the domain
meta-model, graphical notation definition and source code generation for
subsumption architecture -- a well-known example of robot control architecture.
Our goal is to show that both the definition of the robot-control architecture
and its supporting tools fits well into the typical workflow of model-driven
engineering development.Comment: Presented at DSLRob 2011 (arXiv:cs/1212.3308
Generation of dynamic motion for anthropomorphic systems under prioritized equality and inequality constraints
In this paper, we propose a solution to compute full-dynamic motions for a humanoid robot, accounting for various kinds of constraints such as dynamic balance or joint limits. As a first step, we propose a unification of task-based control schemes, in inverse kinematics or inverse dynamics. Based on this unification, we generalize the cascade of quadratic programs that were developed for inverse kinematics only. Then, we apply the solution to generate, in simulation, wholebody motions for a humanoid robot in unilateral contact with the ground, while ensuring the dynamic balance on a non horizontal surface
Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
Recently several hierarchical inverse dynamics controllers based on cascades
of quadratic programs have been proposed for application on torque controlled
robots. They have important theoretical benefits but have never been
implemented on a torque controlled robot where model inaccuracies and real-time
computation requirements can be problematic. In this contribution we present an
experimental evaluation of these algorithms in the context of balance control
for a humanoid robot. The presented experiments demonstrate the applicability
of the approach under real robot conditions (i.e. model uncertainty, estimation
errors, etc). We propose a simplification of the optimization problem that
allows us to decrease computation time enough to implement it in a fast torque
control loop. We implement a momentum-based balance controller which shows
robust performance in face of unknown disturbances, even when the robot is
standing on only one foot. In a second experiment, a tracking task is evaluated
to demonstrate the performance of the controller with more complicated
hierarchies. Our results show that hierarchical inverse dynamics controllers
can be used for feedback control of humanoid robots and that momentum-based
balance control can be efficiently implemented on a real robot.Comment: appears in IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Teaching humanoid robotics by means of human teleoperation through RGB-D sensors
This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels
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