A Holistic Approach to Human-Supervised Humanoid Robot Operations in Extreme Environments

Nuclear energy will play a critical role in meeting clean energy targets worldwide. However, nuclear environments are dangerous for humans to operate in due to the presence of highly radioactive materials. Robots can help address this issue by allowing remote access to nuclear and other highly hazardous facilities under human supervision to perform inspection and maintenance tasks during normal operations, help with clean-up missions, and aid in decommissioning. This paper presents our research to help realize humanoid robots in supervisory roles in nuclear environments. Our research focuses on National Aeronautics and Space Administration (NASA’s) humanoid robot, Valkyrie, in the areas of constrained manipulation and motion planning, increasing stability using support contact, dynamic non-prehensile manipulation, locomotion on deformable terrains, and human-in-the-loop control interfaces

Direct Trajectory Optimization of Robotic Mechanical Systems with Unscheduled Contact Sequences

In questo studio sono valutate le prestazioni dei metodi di ottimizzazione numerica come mezzi per identificare movimenti dinamici ottimi di sistemi robotici meccanici interagenti con l'ambiente tramite sequenze di contatto non programmate. Nello specifico l'attenzione è concentrata su di uno schematico modello di umanoide bidimensionale (rappresentato come una catena seriale di corpi rigidi a cinque GdL con base fissa nello spazio) impegnato nell'azione di alzarsi (sedersi) da (verso) una posizione supina o prona, entrando in contatto o distaccandosi dal terreno a seconda della necessità tramite l'utilizzo delle mani, dei gomiti, delle anche, delle ginocchia e dei piedi. Le differenti alternative nell'impostazione del problema (che comportano differenti equazioni di vincolo nel processo di ottimizzazione) sono rappresentate dall'introduzione esplicita (o meno) delle forze di contatto tra le variabili libere di ottimizzazione e dalla suddivisione della pianificazione in due successive ottimizzazioni di crescente complessità a livello dinamico. La forma dei comportamenti ottenuti e la sensitività del processo di convergenza sono valutate principalmente al variare dei parametri del modello di contatto e dei pesi associati ai termini della funzione di costo; anche alcune tecniche per guidare l'umanoide ad interagire efficacemente con l'ambiente sono discusse. Lo scopo finale di questo studio è lo sviluppo di una delle poche analisi parametriche complete sulle prestazioni raggiungibili con i metodi di ottimizzazione numerica per la pianificazione del movimento di un intero sistema dinamico con sequenze di contatto non specificate a priori. This study evaluates the performances of numerical optimization methods as a tool to identify optimal dynamic motions for robotic mechanical systems interacting with the environment through unscheduled contact sequences. Specifically the attention is focuses on a schematic two-dimensional humanoid model (represented as a fixed-base five-DoF articulated serial chain of rigid bodies) in the tasks of getting up (sitting down) from (to) supine and prone positions, opportunistically making and breaking contacts with the ground through hands, elbows, hips, knees, and feet. The different alternatives in the problem transcription (which lead to different constraint equations in the nonlinear program) are determined by the explicit introduction (or not) of contact forces among the free optimization variables and by the split of the planning into two consecutive optimizations of rising dynamic complexity. Shapes of the emergent behaviors and sensitivity of the convergence process are evaluated mainly with respect to contact model parameters and weights of cost function terms; various techniques to guide the humanoid to effectively interact with the environment are also discussed. The final aim of this study is to develop one of the very few complete parametric analysis on the performances achievable with optimization-based methods for whole-body dynamic motion planning with a priori unspecified contact sequences

Disturbance models for offset-free nonlinear predictive control

Offset-free model predictive control refers to a class of control algorithms able to track asymptotically constant reference signals despite the presence of unmeasured, nonzero mean disturbances acting on the process and/or plant model mismatch. Generally, in these formulations the nominal model of the plant is augmented with integrating disturbances, i.e. with a properly designed disturbance model, and state and disturbance are estimated from output measurements. To date the vast majority of offset-free MPC applications are based on linear models, however, since process dynamics are generally inherently nonlinear, these may perform poorly or even fail in some situations. Better results can be achieved by making use of nonlinear formulations and hence of nonlinear model predictive control (NMPC) technology. However, the obstacles associated with implementing NMPC frameworks are nontrivial. In this work the offset-free tracking problem with nonlinear models is addressed. Firstly some basic concepts related to the observability of nonlinear systems and state estimation are reviewed, focusing on the digital filtering and putting a strong accent on the role of the disturbance model. Thus, a class of disturbance models in which the integrated term is added to model parameters is presented together with an efficient and practical strategy for its design and subsequent implementation in offset-free NMPC frameworks

A Computational Framework for Environment-Aware Robotic Manipulation Planning

In this paper, we present a computational framework for direct trajectory optimization of general manipulation systems with unspecified contact sequences, exploiting environmental constraints as a key tool to accomplish a task. Two approaches are presented to describe the dynamics of systems with contacts, which are based on a penalty formulation and on a velocity- based time-stepping scheme, respectively. In both cases, object and environment contact forces are included among the free optimization variables, and they are rendered consistent via suitably devised sets of complementarity conditions. To maximize computational efficiency, we exploit sparsity patterns in the linear algebra expressions generated during the solution of the optimization problem and leverage Algorithmic Differentiation to calculate derivatives. The benefits of the proposed methods are evaluated in three simulated planar manipulation tasks, where essential interactions with environmental constraints are automatically synthesized and opportunistically exploited.status: accepte