Sylvain Bertrand

Team IHMC's Lessons Learned from the DARPA Robotics Challenge Trials

This article is a summary of the experiences of the Florida Institute for Human & Machine Cognition IHMC team during the DARPA Robotics Challenge DRC Trials. The primary goal of the DRC is to develop robots capable of assisting humans in responding to natural and manmade disasters. The robots are expected to use standard tools and equipment to accomplish the mission. The DRC Trials consisted of eight different challenges that tested robot mobility, manipulation, and control under degraded communications and time constraints. Team IHMC competed using the Atlas humanoid robot made by Boston Dynamics. We competed against 16 international teams and placed second in the competition. This article discusses the challenges we faced in transitioning from simulation to hardware. It also discusses the lessons learned both during the competition and in the months of preparation leading up to it. The lessons address the value of reliable hardware and solid software practices. They also cover effective approaches to bipedal walking and designing for human-robot teamwork. Lastly, the lessons present a philosophical discussion about choices related to designing robotic systems.

Summary of Team IHMC's virtual robotics challenge entry

This paper presents a high level overview of the work done by Team IHMC (Florida Institute for Human and Machine Cognition) to win the DARPA Virtual Robotics Challenge (VRC), held June 18-20 2013. The VRC consisted of a series of three tasks (driving a vehicle, walking over varied terrain, and manipulating a fire hose), to be completed in simulation using a model of the humanoid robot Atlas. Team IHMC was able to complete all of these challenges multiple times during the competition. The paper presents our approach, as well as a birds-eye view of the major software components and their integration.

Design of a Momentum-Based Control Framework and Application to the Humanoid Robot Atlas

This paper presents a momentum-based control framework for floating-base robots and its application to the humanoid robot “Atlas”. At the heart of the control framework lies a quadratic program that reconciles motion tasks expressed as constraints on the joint acceleration vector with the limitations due to unilateral ground contact and force-limited grasping. We elaborate on necessary adaptations required to move from simulation to real hardware and present results for walking across rough terrain, basic manipulation, and multi-contact balancing on sloped surfaces (the latter in simulation only). The presented control framework was used to secure second place in both the DARPA Robotics Challenge Trials in December 2013 and the Finals in June 2015.

Stability Analysis of an UAV Controller using Singular Perturbation Theory

Abstract This paper presents the stability analysis of a hierarchical controller for an Unmanned Aerial Vehicle, using singular perturbation theory. Position and attitude control laws are successively designed by considering a time-scale separation between the translational dynamics and the orientation dynamics of a six degrees of freedom Vertical Take Off and Landing UAV model. In addition, for the design of the position controller, we consider the case where the linear velocity of the vehicle is not measured. A partial state feedback control law is proposed, based on the introduction of virtual states in the translational dynamics of the system.

Attitude tracking of rigid bodies on the special orthogonal group with bounded partial state feedback

A solution to the attitude tracking problem of rigid bodies with kinematic representation directly on the special orthogonal group SO(3) of rotation matrices is proposed. A dynamic partial state feedback controller is designed to address the case where no angular velocity measurements are available. In addition, the gains in the control design can be tuned in advance to ensure that the torque inputs satisfy arbitrary saturation bounds. Stability conditions are provided based on Lyapunov function analysis and Barbalats lemma. Simulation results are presented to illustrate the performance of the proposed control scheme.

Stabilization of a Small Unmanned Aerial Vehicle Model without Velocity Measurement

This paper presents a method to design guidance and control laws for small vertical take off and landing unmanned aerial vehicles when no measurement of linear velocity or angular velocity is available. The control strategy is based on the introduction of virtual states in the state equation of the system and allows the design of stabilizing feedback controllers without using any observer. Simulation results are provided for six degrees of freedom model of a small rotorcraft-based unmanned aerial vehicle.

Cooperative Nonlinear Model Predictive Control for Flocks of Vehicles

Abstract This paper describes the guidance of a group of autonomous cooperating vehicles using model predictive control. The developed control strategy allows to find a feasible near optimal control sequence with a short and constant computation delay in all situations. It makes use of the nonlinear model of the vehicle and takes other vehicle intentions into account. Numerical simulations are provided where a group of vehicles must reach several way-points while avoiding obstacles and collisions inside the group. These simulations allow to compare computation delay and efficiency of the proposed approach with traditional optimisation.

Walking on partial footholds including line contacts with the humanoid robot atlas

We present a method for humanoid robot walking on partial footholds such as small stepping stones and rocks with sharp surfaces. Our algorithm does not rely on prior knowledge of the foothold, but information about an expected foothold can be used to improve the stepping performance. After a step is taken, the robot explores the new contact surface by attempting to shift the center of pressure around the foot. The available foothold is inferred by the way in which the foot rotates about contact edges and/or by the achieved center of pressure locations on the foot during exploration. This estimated contact area is then used by a whole body momentum-based control algorithm. To walk and balance on partial footholds, we combine fast, dynamic stepping with the use of upper body angular momentum to regain balance. We applied this method to the Atlas humanoid designed by Boston Dynamics to walk over small contact surfaces, such as line and point contacts. We present experimental results and discuss performance limitations.

Team IHMC's Lessons Learned from the DARPA Robotics Challenge: Finding Data in the Rubble

This article presents a retrospective analysis of Team IHMCs experience throughout the DARPA Robotics Challenge DRC, where we took first or second place overall in each of the three phases. As an extremely demanding challenge typical of DARPA, the DRC required rapid research and development to push the boundaries of robotics and set a new benchmark for complex robotic behavior. We present how we addressed each of the eight tasks of the DRC and review our performance in the Finals. While the ambitious competition schedule limited extensive experimentation, we will review the data we collected during the approximately three years of our participation. We discuss some of the significant lessons learned that contributed to our success in the DRC. These include hardware lessons, software lessons, and human-robot integration lessons. We describe refinements to the coactive design methodology that helped our designers connect human-machine interaction theory to both implementation and empirical data. This approach helped our team focus our limited resources on the issues most critical to success. In addition to helping readers understand our experiences in developing on a Boston Dynamics Atlas robot for the DRC, we hope this article will provide insights that apply more widely to robotics development and design of human-machine systems.

CONTRACTIVE MODEL PREDICTIVE CONTROL OF AN UNMANNED AERIAL VEHICLE MODEL

Abstract This paper presents the design of guidance and control laws for a small rotorcraft-based Unmanned Aerial Vehicle model using a contractive nonlinear Model Predictive Control scheme. Stability issues are addressed and simulation results are provided for a six degrees of freedom model in the case of trajectory tracking.