A Hybrid Planning and Control Model for Biped Feet Rotation

This thesis proposes methods for biped walking locomotion with feet rotation. The chief objective of this work is to first generate a guide trajectory based on designing a zero moment point (ZMP) trajectory within the support polygon and obtain linear controlling methods to stabilize the walking procedure with feet rotation. With feet rotation, the walking procedure will be more humanlike, more flexible and possible saving energy. However, when the feet are rotating around their edge, either toe or heel, the entire robot is under-actuated which are more difficult to control. By using preview control, a dynamic model of the system can be derived to control the robot. This thesis is based upon a simplified model of the Reemc Robot by PAL Robotics. The simplified model has fixed arms, since only leg motions are considered, and two legs. Each leg has three degrees of freedom. The robot is presented as a three mass model. A guided gait trajectory is first generated as the boundary condition for the ZMP. Interpolation methods are used to generate a ZMP trajectory from a set of discrete points that stay inside the boundary condition. By designing the transition model from single support phase and double support phase, a general schema can be achieved. Following the assumptions of a linear inverted pendulum, trajectories of all three masses can be solved. Inverse kinematics can now give the reference joint trajectories, which, together with the reference ZMP trajectory, is used in control methods to minimize the error between the reference trajectory and actual trajectory in simulation. Control methods are used to stabilize the motion of the walking procedure. Preview control is used for the single support phase where the behavior of all three masses is linear. A proper input can be obtained through optimization. During the double support phase, the feet rotations are nonlinear and under-actuated since the feet are rotating around their edge where no torque can be produced from the ground. By using preview control, an input can be applied to the robot so that the robot can maintain dynamic stability.Ope

Acquisition and distribution of synergistic reactive control skills

Learning from demonstration is an afficient way to attain a new skill. In the context of autonomous robots, using a demonstration to teach a robot accelerates the robot learning process significantly. It helps to identify feasible solutions as starting points for future exploration or to avoid actions that lead to failure. But the acquisition of pertinent observationa is predicated on first segmenting the data into meaningful sequences. These segments form the basis for learning models capable of recognising future actions and reconstructing the motion to control a robot. Furthermore, learning algorithms for generative models are generally not tuned to produce stable trajectories and suffer from parameter redundancy for high degree of freedom robots This thesis addresses these issues by firstly investigating algorithms, based on dynamic programming and mixture models, for segmentation sensitivity and recognition accuracy on human motion capture data sets of repetitive and categorical motion classes. A stability analysis of the non-linear dynamical systems derived from the resultant mixture model representations aims to ensure that any trajectories converge to the intended target motion as observed in the demonstrations. Finally, these concepts are extended to humanoid robots by deploying a factor analyser for each mixture model component and coordinating the structure into a low dimensional representation of the demonstrated trajectories. This representation can be constructed as a correspondence map is learned between the demonstrator and robot for joint space actions. Applying these algorithms for demonstrating movement skills to robot is a further step towards autonomous incremental robot learning

MAPiS 2019 - First MAP-i Seminar: proceedings

This book contains a selection of Informatics papers accepted for presentation and discussion at “MAPiS 2019 - First MAP-i Seminar”, held in Aveiro, Portugal, January 31, 2019. MAPiS is the first conference organized by the MAP-i first year students, in the context of the Seminar course. The MAP-i Doctoral Programme in Computer Science is a joint Doctoral Programme in Computer Science of the University of Minho, the University of Aveiro and the University of Porto. This programme aims to form highly-qualified professionals, fostering their capacity and knowledge to the research area. This Conference was organized by the first grade students attending the Seminar Course. The aim of the course was to introduce concepts which are complementary to scientific and technological education, but fundamental to both completing a PhD successfully and entailing a career on scientific research. The students had contact with the typical procedures and difficulties of organizing and participate in such a complex event. These students were in charge of the organization and management of all the aspects of the event, such as the accommodation of participants or revision of the papers. The works presented in the Conference and the papers submitted were also developed by these students, fomenting their enthusiasm regarding the investigation in the Informatics area. (...)publishe