Robot multiple contact control

Versatile Locomotion Planning and Control for Humanoid Robots

We propose a locomotion framework for bipedal robots consisting of a new motion planning method, dubbed trajectory optimization for walking robots plus (TOWR+), and a new whole-body control method, dubbed implicit hierarchical whole-body controller (IHWBC). For versatility, we consider the use of a composite rigid body (CRB) model to optimize the robot’s walking behavior. The proposed CRB model considers the floating base dynamics while accounting for the effects of the heavy distal mass of humanoids using a pre-trained centroidal inertia network. TOWR+ leverages the phase-based parameterization of its precursor, TOWR, and optimizes for base and end-effectors motions, feet contact wrenches, as well as contact timing and locations without the need to solve a complementary problem or integer program. The use of IHWBC enforces unilateral contact constraints (i.e., non-slip and non-penetration constraints) and a task hierarchy through the cost function, relaxing contact constraints and providing an implicit hierarchy between tasks. This controller provides additional flexibility and smooth task and contact transitions as applied to our 10 degree-of-freedom, line-feet biped robot DRACO. In addition, we introduce a new open-source and light-weight software architecture, dubbed planning and control (PnC), that implements and combines TOWR+ and IHWBC. PnC provides modularity, versatility, and scalability so that the provided modules can be interchanged with other motion planners and whole-body controllers and tested in an end-to-end manner. In the experimental section, we first analyze the performance of TOWR+ using various bipeds. We then demonstrate balancing behaviors on the DRACO hardware using the proposed IHWBC method. Finally, we integrate TOWR+ and IHWBC and demonstrate step-and-stop behaviors on the DRACO hardware.

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.

Neural Network Based Contact Force Control Algorithm for Walking Robots

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

Contact-space resolution model for a physically consistent view of simultaneous collisions in articulated-body systems: theory and experimental results

Multi-surface interactions occur frequently in articulated-rigid-body systems such as robotic manipulators. Real-time prediction of contact-interaction forces is challenging for systems with many degrees of freedom (DOFs) because joint and contact constraints must be enforced simultaneously. While several contact models exist for systems of free rigid bodies, fewer models are available for articulated-body systems. In this paper, we extend the method of Ruspini and Khatib and develop the contact-space resolution (CSR) model by applying the operational space theory of robot manipulation. Through a proper choice of contact-space coordinates, the projected dynamics of the system in the contact space is obtained. We show that the projection into the dynamically consistent null space preserves linear and angular momentum in a subspace of the system dynamics complementary to the joint and contact constraints. Furthermore, we illustrate that a simultaneous collision event between two articulated bodies can be resolved as an equivalent simultaneous collision between two non-articulated rigid bodies through the projected contact-space dynamics. Solving this reduced-dimensional problem is computationally efficient, but determining its accuracy requires physical experimentation. To gain further insights into the theoretical model predictions, we devised an apparatus consisting of colliding 1-, 2-, and 3-DOF articulated bodies where joint motion is recorded with high precision. Results validate that the CSR model accurately predicts the post-collision system state. Moreover, for the first time, we show that the projection of system dynamics into the mutually complementary contact space and null space is a physically verifiable phenomenon in articulated-rigid-body systems.

A Control System for a Tool Use Robot: Drawing a Circle by Educing Functions of a Compass

[abstFig src='/00290002/12.jpg' width='260' text='Axes in the compass coordinate system' ] Robotic tool use is one of various approaches for actualizing versatility of robots, and is thus the focus of many studies. However, selection of the controllers for tool use and how to design them remains indeterminate. This paper addresses the task of drawing a circle with a compass as an example of tool use. This task mandates to deal with complex contact at multiple points and needs to educe functions of the compass to draw a circle accurately. This paper demonstrates the implementation and corresponding method of compass controller design. The method of designing the controller for the compass entails decomposing the usage of the compass into semantic units and subsequently defining a coordinate system and fabricating the controller via mapping of the semantic units to axes. The implementation of a controller for compass use indicates that the ability of the compass to accurately draw a circle is educed via mechanical constraints of the compass. We validated the implemented controller by drawing a circle and comparing the result to a circle drawn using a pencil.

Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator

The goal of this work is to develop a soft-robotic manipulation system that is capable of autonomous, dynamic, and safe interactions with humans and its environment. First, we develop a dynamic model for a multi-body fluidic elastomer manipulator that is composed entirely from soft rubber and subject to the self-loading effects of gravity. Then, we present a strategy for independently identifying all of the unknown components of the system; these are the soft manipulator, its distributed fluidic elastomer actuators, as well as the drive cylinders that supply fluid energy. Next, using this model and trajectory-optimization techniques we find locally-optimal open-loop policies that allow the system to perform dynamic maneuvers we call grabs. In 37 experimental trials with a physical prototype, we successfully perform a grab 92% of the time. Last, we introduce the idea of static bracing for a soft elastomer arm and discuss how forming environmental braces might be an effective manipulation strategy for this class of robots. By studying such an extreme example of a soft robot, we can begin to solve hard problems inhibiting the mainstream use of soft machines.

Methods and Experimental Protocols to Design a Simulated Bio-Mimetic Quadruped Robot

Abstract This paper presents a bio-mimetic approach to design and simulate a tortoise-like virtual robot. This study takes a multidisciplinary approach: from in vivo and in vitro experiments on animals, data are collected and used to design, control and simulate a bio-mimetic virtual robot using MD ADAMS platform. From the in vitro experiments, the geometrical and inertial properties of body limbs are measured, and a model of tortoise kinematics is derived. From the in vivo experiments the contact forces between each limb and the ground are measured. The contributions of hind and forelimbs in the generation of propelling and braking forces are studied. The motion of the joints between limb segments are recorded and used to solve the inverse kinematics problem. A virtual model of a tortoise-like robot is built; it is a linkage of 15 rigid bodies articulated by 22 degrees of freedom. This model is referred to as TATOR II. It has the inertial and geometrical properties measured during the in vitro experiments. TATOR II motion is achieved using a Proportional-Derivative controller copying the joint angle trajectories calculated from the in vivo experiments.