High-DOF Motion Planning in Dynamic Environments using Trajectory Optimization

Motion planning is an important problem in robotics, computer-aided design, and simulated environments. Recently, robots with a high number of controllable joints are increasingly used for different applications, including in dynamic environments with humans and other moving objects. In this thesis, we address three main challenges related to motion planning algorithms for high-DOF robots in dynamic environments: 1) how to compute a feasible and constrained motion trajectory in dynamic environments; 2) how to improve the performance of realtime computations for high-DOF robots; 3) how to model the uncertainty in the environment representation and the motion of the obstacles. We present a novel optimization-based algorithm for motion planning in dynamic environments. We model various constraints corresponding to smoothness, as well as kinematics and dynamics bounds, as a cost function, and perform stochastic trajectory optimization to compute feasible high-dimensional trajectories. In order to handle arbitrary dynamic obstacles, we use a replanning framework that interleaves planning with execution. We also parallelize our approach on multiple CPU or GPU cores to improve the performance and perform realtime computations. In order to deal with the uncertainty of dynamic environments, we present an efficient probabilistic collision detection algorithm that takes into account noisy sensor data. We predict the future obstacle motion as Gaussian distributions, and compute the bounded collision probability between a high-DOF robot and obstacles. We highlight the performance of our algorithms in simulated environments as well as with a 7-DOF Fetch arm.Doctor of Philosoph

Generation of whole-body motion for humanoid robots with the complete dynamics

Cette thèse propose une solution au problème de la génération de mouvements pour les robots humanoïdes. Le cadre qui est proposé dans cette thèse génère des mouvements corps-complet en utilisant la dynamique inverse avec l'espace des tâches et en satisfaisant toutes les contraintes de contact. La spécification des mouvements se fait à travers objectifs dans l'espace des tâches et la grande redondance du système est gérée avec une pile de tâches où les tâches moins prioritaires sont atteintes seulement si elles n'interfèrent pas avec celles de plus haute priorité. À cette fin, un QP hiérarchique est utilisé, avec l'avantage d'être en mesure de préciser tâches d'égalité ou d'inégalité à tous les niveaux de la hiérarchie. La capacité de traiter plusieurs contacts non-coplanaires est montrée par des mouvements où le robot s'assoit sur une chaise et monte une échelle. Le cadre générique de génération de mouvements est ensuite appliqué à des études de cas à l'aide de HRP-2 et Romeo. Les mouvements complexes et similaires à l'humain sont obtenus en utilisant l'imitation du mouvement humain où le mouvement acquis passe par un processus cinématique et dynamique. Pour faire face à la nature instantanée de la dynamique inverse, un générateur de cycle de marche est utilisé comme entrée pour la pile de tâches qui effectue une correction locale de la position des pieds sur la base des points de contact permettant de marcher sur un terrain accidenté. La vision stéréo est également introduite pour aider dans le processus de marche. Pour une récupération rapide d'équilibre, le capture point est utilisé comme une tâche contrôlée dans une région désirée de l'espace. En outre, la génération de mouvements est présentée pour CHIMP, qui a besoin d'un traitement particulier.This thesis aims at providing a solution to the problem of motion generation for humanoid robots. The proposed framework generates whole-body motion using the complete robot dynamics in the task space satisfying contact constraints. This approach is known as operational-space inverse-dynamics control. The specification of the movements is done through objectives in the task space, and the high redundancy of the system is handled with a prioritized stack of tasks where lower priority tasks are only achieved if they do not interfere with higher priority ones. To this end, a hierarchical quadratic program is used, with the advantage of being able to specify tasks as equalities or inequalities at any level of the hierarchy. Motions where the robot sits down in an armchair and climbs a ladder show the capability to handle multiple non-coplanar contacts. The generic motion generation framework is then applied to some case studies using HRP-2 and Romeo. Complex and human-like movements are achieved using human motion imitation where the acquired motion passes through a kinematic and then dynamic retargeting processes. To deal with the instantaneous nature of inverse dynamics, a walking pattern generator is used as an input for the stack of tasks which makes a local correction of the feet position based on the contact points allowing to walk on non-planar surfaces. Visual feedback is also introduced to aid in the walking process. Alternatively, for a fast balance recovery, the capture point is introduced in the framework as a task and it is controlled within a desired region of space. Also, motion generation is presented for CHIMP which is a robot that needs a particular treatment

Using Model-based Optimal Control for Conceptional Motion Generation for the Humannoid Robot HRP-2 14 and Design Investigations for Exo-Skeletons

The research field of bipedal locomotion has been active since a few decades now. At one hand, the legged locomotion principle comprises highly flexible and robust mobility for technical applications. At the other hand, a thorough technical understanding of bipedalism supports efforts of clinicians and engineers to help people, suffering from reduced locomotion capabilities caused by fatal incidents. Since the technology enabled the construction of numerous robotic devices, among them: various humanoids, researchers started to investigate bipedalism by abstraction and adoption for technical applications. Findings from humanoid robotics are further exploited for the construction of devices for human performance augmentation and mobility support or gait rehabilitation, among them: orthosis and exo-skeletons. Although this research continuously progresses, the motion capacities of humanoid robots still lack far behind those of humans in terms of forward velocity, robustness and appearance of the overall motion. Generally, it is claimed that the difference of performance between humans and robotics is not only due to the limiting characteristics of the employed technology, e.g. constructive lack of specific determinants of gait for bipedalism or dynamic limits of the actuation system, but as well to the adopted methods for motion generation and control. For humanoid robotics, methods for motion generation are classified into optimization-based methods and those that employ heuristics, that are mostly distinguished based on the problem complexity (computation time) and the resulting dynamic error between the generated motion and the dynamics of the real robot. The implementation of the dynamic motion on the robotic platform is usually comprised with an on-line stabilizing control system. This control system must then identify and resolve instantaneously the dynamic error to maintain a continuously stable operation of the device. A large dynamic error and breach of the dynamic limits of the actuation system can quickly lead to a fatal destabilization of the device. This work proposes a contribution to the model computation and the strategy of the problem formulation of direct multiple-shooting based optimal control (Bock et. al.) for dynamically stable optimization-based motion generation. The computation of the whole-body dynamic model inside the optimization relies either on forward or inverse dynamics approach. As the inverse dynamics approach has frequently been perceived as less resource intensive than the forward dynamics approach, a new generic algorithm for insufficiently constrained, under-actuated dynamic systems has been developed and thoroughly tested to comply with all numerical restrictions of the enveloping optimization algorithm. Based on this contribution, various optimal control problems for the humanoid platform HRP-2 14 have been formulated to assess the influence of different biologically inspired optimization criteria on the final motion characteristics of walking motions. From thorough bibliographic researches a dynamically more accurate model was comprised, by taking into account the impact absorbing element in the ankle joint complex. Based on the experiences of the previous study, a problem formulation for the limiting case of, dynamically overstepping an obstacle of 20cm x 11cm (height x width) with only two steps, while maintaining its stable operation was accomplished. This is a new record for this platform. In a further part, this work proposes an iterative comprehensive model-based optimal control approach for the conception of a lower limb exo-skeleton that respects the integrated nature of such a mechatronic device. In this contribution, a human effectively wearing such a lower limb exo-skeleton is modeled. The approach then substantiates all system components in an iterative procedure, based on the complete system model, effectively resolving all complex inter-dependencies between the different components of the system. The study in this work is conducted on an important benchmark motion, walking, of a healthy human being. From this study the limiting characteristics of the system are determined and substantial propositions to the realization of various system components are formulated

Optimal Motion Planning for Humanoid Robots

Accepted in 2013 IEEE International Conference on Robotics and AutomationInternational audienceThis paper aims at combining state of the art developments of path planning and optimal control and to create the algorithmic foundations to tackle optimal control problems in cluttered environments. Our contribution is three-fold: first, we describe a simple method to automatically generate minimum bounding capsules around exact robot body geometries represented by meshes. Second, we use the bounding capsules to implement distance constraints for an optimal control problem solver and achieve (self-)collision avoidance. Finally, we propose a complete two-stage framework for optimal motion planning on complex robots. This framework is successfully applied to generate optimal collision-free trajectories both in simulation and on the humanoid robot HRP-2