To be coming…

This paper presents a new algorithm for optimal control (OC) of nonlinear dynamical systems. The main feature of this algorithm is that it allows the specification of the control objectives as a hierarchy of tasks. Each task is described by a cost function that the algorithm tries to minimize, while not affecting the tasks of higher priority. The concept of strict priority allows for an easier and more robust specification of the control objectives, without hand-tuning of task weights. The hierarchy also makes it possible to properly regularize the behavior of each task independently.

The recent works on quadrotor have focused on more and more challenging tasks on increasingly complex systems. Systems are often augmented with slung loads, inverted pendulums or arms, and accomplish complex tasks such as going through a window, grasping, throwing or catching. Usually, controllers are designed to accomplish a specific task on a specific system using analytic solutions, so each application needs long preparations. On the other hand, the direct multiple shooting approach is able to solve complex problems without any analytic development, by using on-the-shelf optimization solver. In this paper, we show that this approach is able to solve a wide range of problems relevant to quadrotor systems, from on-line trajectory generation for quadrotors, to going through a window for a quadrotor-and-pendulum system, through manipulation tasks for a aerial manipulator.

Predictive control is an efficient model-based methodology to control complex dynamical systems. In general, it boils down to the resolution at each control cycle of a large nonlinear optimization problem. A critical issue is then to provide a good guess to initialize the nonlinear solver so as to speed up convergence. This is particularly important when disturbances or changes in the environment prevent the use of the trajectory computed at the previous control cycle as initial guess. In this paper, we introduce an original and very efficient solution to automatically build this initial guess. We propose to rely on off-line computation to build an approximation of the optimal trajectories, that can be used on-line to initialize the predictive controller.

Planning, adapting and executing multi-contact locomotion movements on legged robots in complex environments remains an open problem. In this proposal, we introduce a complete pipeline to address this issue in the context of humanoid robots inside industrial environments. This pipeline relies on a multi-stage approach in order to simplify the process flow and to exploit at best state-of-the-art techniques both in terms of contact planning, whole-body control and perception. The main challenges lie in the choice of the different modules composing this pipeline as well as their mutual interactions: e.g. at which frequency rates each module has to work in order to allow safe and robust locomotion? or which information must transit between the modules? We named this project Loco3D standing for Locomotion in 3D, in contrast to the classic locomotion on quasi-flat terrains, where the motion of the center of mass of the robot is mostly limited to a 2D plane.

In this paper, we present a preliminary proof of concept (PoC) aiming at introducing humanoid robots in an aircraft factory. The PoC was aiming at demonstrating the capacity of HRP-2 to deal with three aspects needed in a factory: reactivity to change in the environment, visual feedback and on-line motion generation. The limits reached in this PoC are here highlighted to draw some direction of research focused on the needs of Aircraft manufacturers. Humanoid robot technology has reached a high level of maturity and now has been implemented on several high quality robots such as Asimo, Atlas, TORO or HRP-2. They have been long envisionned as universal worker which can be used in factory, this video present a PoC in this context.