Sequential Motion Planning for Bipedal Somersault via Flywheel SLIP and Momentum Transmission with Task Space Control

In this paper, we present a sequential motion planning and control method for generating somersaults on bipedal robots. The somersault (backflip or frontflip) is considered as a coupling between an axile hopping motion and a rotational motion about the center of mass of the robot; these are encoded by a hopping Spring-loaded Inverted Pendulum (SLIP) model and the rotation of a Flywheel, respectively. We thus present the Flywheel SLIP model for generating the desired motion on the ground phase. In the flight phase, we present a momentum transmission method to adjust the orientation of the lower body based on the conservation of the centroidal momentum. The generated motion plans are realized on the full-dimensional robot via momentum-included task space control. Finally, the proposed method is implemented on a modified version of the bipedal robot Cassie in simulation wherein multiple somersault motions are generated

Robot motion planning via curve shortening flows

This work will present a series of developments of geometric heat flow method in robot motion planning and estimation. The key of geometric heat flow is to formulate the motion planning problem into a curve shortening problem. By solving the geometric heat flow, an arbitrary initial curve can be deformed to a curve of minimal length, which corresponds to a feasible motion. Preliminary theories and algorithms for motion planning based on geometric heat flow have been developed for driftless control affine systems. The main contribution of this research is to extend the algorithm to robotic systems, which are dynamic systems with drifts and different types of constraint. Early stages of the research focus on adapting the algorithm to solve motion planning problems for systems with drift. To tackle systems with drift, actuated curve length and affine geometric heat flow is proposed. The method is then enriched to solve robot gymnastics motion planning, in which the effect of state constraints is encoded into curve length. Free boundary conditions are also studied to enforce the conservation of the robot's momentum. The second stage of the research focus on the construction of the geometric heat flow framework for robot locomotion planning, which involves hybrid dynamics due to contact. The activation and deactivation of phase-dependent constraints are controlled by activation functions. Lastly, to solve 3D problems in robotics, planning and estimation in SO(3) space is formulated using the geometric heat flow method

Hopping, Landing, and Balancing with Springs

This work investigates the interaction of a planar double pendulum robot and springs, where the lower body (the leg) has been modified to include a spring-loaded passive prismatic joint. The thesis explores the mechanical advantage of adding a spring to the robot in hopping, landing, and balancing activities by formulating the motion problem as a boundary value problem; and also provides a control strategy for such scenarios. It also analyses the robustness of the developed controller to uncertain spring parameters, and an observer solution is provided to estimate these parameters while the robot is performing a tracking task. Finally, it shows a study of how well IMUs perform in bouncing conditions, which is critical for the proper operation of a hopping robot or a running-legged one

A computerized dynamic synthesis method for generating human aerial movements

A computerized method based on optimal dynamic synthesis was developed for generating the flight phase of somersaults. A virtual gymnast is modeled as a planar seven-segment multibody system with six internal degrees of freedom. The aerial movement is generated using a parametric optimization technique. The performance criterion to be minimized is the integral quadratic norm of the torque generators. The method produces realistic movements showing that somersaults perfectly piked or tucked appear spontaneously according to the value of the rotation potential of the initial movement. It provides accurate knowledge of the evolution of joint actuating torques controlling the somersault, and makes it possible to investigate precisely the configurational changes induced by modifications of the rotation potential. Four simulations are presented: one with a reference value for the rotation potential, two with reduced values, and the last with a different hip flexion limit. They give an insight into the coordination strategies which make the movement feasible when the rotation potential is decreased. The method gives accurate assessments of the energetic performance required, together with precise evaluations of the mechanical efforts to be produced for generating the acrobatic movement

Motion Planning and Control of Dynamic Humanoid Locomotion

Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot. Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d. As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics. The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end

Planning and executing motions for multibody systems in free-fall

The purpose of this research is to develop an end-to-end system that can be applied to a multibody system in free-fall to analyze its possible motions, save those motions in a database, and design a controller that can execute those motions. A goal is for the process to be highly automated and involve little human intervention. Ideally, the output of the system would be data and algorithms that could be put in ROM to control the multibody system in free-fall. The research applies to more than just robots in space. It applies to any multibody system in free-fall. Mathematical techniques from nonlinear control theory were used to study the nature of the system dynamics and its possible motions. Optimization techniques were applied to plan motions. Image compression techniques were proposed to compress the precomputed motion data for storage. A linearized controller was derived to control the system while it executes preplanned trajectories

Developing agile motor skills on virtual and real humanoids

Demonstrating strength and agility on virtual and real humanoids has been an important goal in computer graphics and robotics. However, developing physics- based controllers for various agile motor skills requires a tremendous amount of prior knowledge and manual labor due to complex mechanisms of the motor skills. The focus of the dissertation is to develop a set of computational tools to expedite the design process of physics-based controllers that can execute a variety of agile motor skills on virtual and real humanoids. Instead of designing directly controllers real humanoids, this dissertation takes an approach that develops appropriate theories and models in virtual simulation and systematically transfers the solutions to hardware systems. The algorithms and frameworks in this dissertation span various topics from spe- cific physics-based controllers to general learning frameworks. We first present an online algorithm for controlling falling and landing motions of virtual characters. The proposed algorithm is effective and efficient enough to generate falling motions for a wide range of arbitrary initial conditions in real-time. Next, we present a robust falling strategy for real humanoids that can manage a wide range of perturbations by planning the optimal contact sequences. We then introduce an iterative learning framework to easily design various agile motions, which is inspired by human learn- ing techniques. The proposed framework is followed by novel algorithms to efficiently optimize control parameters for the target tasks, especially when they have many constraints or parameterized goals. Finally, we introduce an iterative approach for exporting simulation-optimized control policies to hardware of robots to reduce the number of hardware experiments, that accompany expensive costs and labors.Ph.D

Analysis and generation of highly dynamic motions of anthropomorphic systems: application to parkour

Cette thèse propose une approche interdisciplinaire originale du traitement du mouvement humain corps-complet grâce à l'utilisation couplée d'approches issues de la biomécanique, du contrôle moteur et de la robotique. Les méthodes biomécaniques sont utilisées pour l'enregistrement, le traitement et l'analyse du mouvement humain. L'approche > du contrôle moteur est étendue à l'étude des mouvements hautement dynamiques. Ceci permet de déterminer si d'éventuelles tâches dynamiques sont contrôlées et stabilisées par le cerveau, puis d'inférer une organisation hiérarchique des tâches motrices. Le formalisme de l'espace des tâches utilisé en robotique pour la génération de mouvement corps-complet ainsi que la hiérarchie des tâches extraites dans l'étude du contrôle moteur sont utilisés pour simuler des mouvements humains hautement dynamiques. Cette approche permet de mieux comprendre le mouvement humain et de générer des mouvements inspirés de l'humain pour d'autres systèmes anthropomorphes tel que des robots ou avatars. La discipline du Parkour, impliquant des actions hautement dynamiques tels que des sauts et des techniques d'atterisage, est choisie pour illustrer l'approche proposée.This thesis proposes an original and interdisciplinary approach to the treatment of whole-body human movements through the synergistic utilization of biomechanics, motor control and robotics. Robust methods of biomechanics are used to record, process and analyze whole-body human motions. The Uncontrolled Manifold approach (UCM) of motor control is extended to study highly dynamic movements processed in the biomechanical study, in order to determine if hypothesized dynamic tasks are being controlled stably by the central nervous system. This extension permits also to infer a hierarchical organization of the controlled dynamic tasks. The task space formalism of motion generation in robotics is used to generate whole-body motion by taking into account the hierarchy of tasks extracted in the motor control study. This approach permits to better understand the organization of human dynamic motions and provide a new methodology to generate whole-body human motions with anthropomorphic systems. A case study of highly dynamic and complex movements of Parkour, including jumps and landings, is utilized to illustrate the proposed approach