This paper introduces the adaptive bias and adaptive gain (ABAG) algorithm for closed-loop electronic speed control (ESC) of the brushless direct current (BLDC) motors typically used to spin the propellers in multi-rotor aerial robots. The ABAG algorithm is adaptive and robust in the sense that it does not require the knowledge of any mechanical/electrical parameter of the motor/propeller group and that neither a pre-calibration nor the knowledge of the feedforward/nominal input is needed. The ABAG algorithm is amenable to an extremely low complexity implementation. We experimentally prove that it can run in 27.5 μs on a 8 MHz microcontroller with no floating point unit and limited arithmetic capabilities allowing only 8-bit additions, subtractions and multiplications. Besides the controller implementation we present a self-contained open source software architecture that handles the entire speed control process, including clock synchronization, and over-current and blockage safeties. The excellent performance and robustness of ABAG are shown by experimental tests and aerial physical interaction experiments.

The topic of reusable software in robotics is now largely addressed. Components based architectures, where components are independent units that can be reused accross applications, have become more popular. As a consequence, a long list of middlewares and integration tools is available in the community, often in the form of open-source projects. However, these projects are generally self contained with little reuse between them. This paper presents a software engineering approach that intends to grant middleware independance to robotic software components so that a clear separation of concerns is achieved between highly reusable algorithmic parts and integration frameworks. Such a decoupling let middle-wares be used interchangeably, while fully benefitting from their specific, individual features. This work has been integrated into a new version of the open-source GenoM component generator tool: GenoM3

Abstract This paper deals with whole‐body motion planning and dynamic control for humanoid from two aspects: locomotion including manipulation and reaching. In the first part, we address a problem of simultaneous locomotion and manipulation planning that combines a geometric and kinematic motion planner with a dynamic humanoid motion generator. The second part deals with whole‐body reaching tasks by using a generalized inverse kinematics (IK) method to fully exploit the high redundancy of the humanoid robot. Through experiments using humanoid platform HRP‐2 No. 14 installed at LAAS‐CNRS, we first verify the validity of each method. An integrated experiment is then presented that unifies the both results via visual perception to execute an object‐fetching task. Copyright © 2009 John Wiley & Sons, Ltd.

The current research analyses and demonstrates how spoken language can be used by human users to communicate with the HRP-2 humanoid to program the robot's behavior in a cooperative task. The task involves the humans and the HRP-2 working together to assemble a piece of furniture. The objectives of the system are to 1) Allow the human to impart knowledge of how to accomplish a cooperative task to the robot, i.e. to program the robot, in the form of a sensory-motor action plan. 2) To do this in a semi-natural and real-time manner using spoken language. In this framework, a system for spoken language programming (SLP) is presented, and experimental results are presented from this prototype system. In Experiment 1, the human programs the robot to assist in assembling a small table. In Experiment 2, the generalization of the system is demonstrated as the user programs the robot to assist in taking the table apart. The SLP is evaluated in terms of the changes in efficiency as revealed by task completion time and number of command operations required to accomplish the tasks with and without SLP. Lessons learned are discussed, along with plans for improving the system, including developing a richer base of robot action and perception predicates that will allow the use of richer language. We thus demonstrate - for the first time - the capability for a human user to tell a humanoid what to do in a cooperative task so that in real time, the robot performs the task, and acquires new skills that significantly facilitate the cooperative human-robot interaction.

Robotics systems and software are becoming more and more complex: the need of standard specifications is certainly a key issue in the near future. There is a need to define, from the software point of view, a generic robot. The Orocos project was started to address this problem. It aims at developing a set of robotics software in particular domains and, as a first step, will define a software framework to do that. This paper presents the future evolutions of G/sup en/oM (component generator) which we propose as a programming framework in the context of the project. G/sup en/oM proposes the definition of generic components that are used to implement robotics functionalities (vision, control, motion planning, ...), and the paper presents the definition of those components. They have been designed so that they can be connected together and externally controlled to form a modular functional layer on robots. We have defined three main entities of which components are made: a set of algorithms, an execution engine, and a communication library. The paper explains their role and presents a formal description language that let us achieves a real decoupling between code of the components (the algorithms) and the execution engine.

Presents a method that estimates robot displacements in outdoor unstructured terrain. It computes the displacements on the basis of associations of 3D points sets produced by consecutive stereovision frames, the associations being determined by tracking pixels from one image frame to the other. The paper details the various steps of the algorithms, and first experimental results are presented: they show that the algorithm is able to estimate the 6 parameters of the robot position with a relative error smaller than about 5%, processing several hundreds of images over several tens of meters.

We present an approach to refine the pose estimate of an outdoor mobile robot evolving on flat terrains cluttered with obstacles. We propose an algorithm to extract relevant obstacle contour lines on the basis of stereo-vision data. The algorithm is very robust with respect to the uncertainties on the data, and do not require a very fine and precise obstacle extraction procedure. We explain how the contour lines are compared from one image to another to refine the pose estimate provided by the robot internal sensors.