Software Architecture and Development for Controlling a Hubo Humanoid Robot

Due to their human-like structure, humanoid robots are capable of doing some complex tasks. Since a humanoid robot has a large number of actuators and sensors, controlling it is a difficult task. For various tasks like balancing, driving a car, and interacting with humans, real-time response of the robot is essential. Efficiently controlling a humanoid robot requires a software that guarantees real-time interface and control mechanism so that real-time response of the robot is possible. Addition- ally, to reduce the development effort and time, the software should be open-source, multi-lingual and should have high-level constructs inbuilt in it. Currently Robot Operating System (ROS) and Microsoft Robotics Developer Studio (MRDS) are most commonly used software packages for controlling robots. Since ROS uses Transmission Control Protocol (TCP) for inter-process communication, the latency in communication is high. Therefore, if ROS is used, the robot cannot respond in real-time. On the other hand, MRDS is not an open-source but a proprietary soft- ware package. Therefore it cannot be optimized for a particular robot. Thus, there is an urgent need to develop a real-time, open-source, modular, and thin software for controlling humanoid robots. This thesis describes the design and architecture of two software packages developed to fill this gap. It is expected that in the near future a large number of humanoid robots will be used all around the world. The humanoid robots will be used to perform various tasks. The developed software packages have the potential to be the most commonly used software packages for controlling humanoid robots. These packages will assist humans in controlling and monitoring humanoid robots to perform search-and-rescue operations, explore the universe, assist in household chores, etc

Dynamic Walking: Toward Agile and Efficient Bipedal Robots

Dynamic walking on bipedal robots has evolved from an idea in science fiction to a practical reality. This is due to continued progress in three key areas: a mathematical understanding of locomotion, the computational ability to encode this mathematics through optimization, and the hardware capable of realizing this understanding in practice. In this context, this review article outlines the end-to-end process of methods which have proven effective in the literature for achieving dynamic walking on bipedal robots. We begin by introducing mathematical models of locomotion, from reduced order models that capture essential walking behaviors to hybrid dynamical systems that encode the full order continuous dynamics along with discrete footstrike dynamics. These models form the basis for gait generation via (nonlinear) optimization problems. Finally, models and their generated gaits merge in the context of real-time control, wherein walking behaviors are translated to hardware. The concepts presented are illustrated throughout in simulation, and experimental instantiation on multiple walking platforms are highlighted to demonstrate the ability to realize dynamic walking on bipedal robots that is agile and efficient

Negotiating Large Obstacles with a Humanoid Robot via Multi-Contact Motion Planning

Incremental progress in humanoid robot locomotion over the years has achieved essential capabilities such as navigation over at or uneven terrain, stepping over small obstacles and imbing stairls. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-contact motion planning, guided by physical human demonstrations. Our goal is to make humanoid locomotion problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the multi-contacts at the upper and lower body limbs. We propose a contact stability measure, which simplies the contact search from demonstration and contact transition motion generation for the multi-contact motion planning algorithm. The algorithm uses the whole-body motions generated via Quadratic Programming (QP) based solver methods. The multi-contact motion planning algorithm is applied for a challenging task of climbing over a relatively larger obstacle compared to the robot. We validate our planning approach with simulations and experiments for climbing over a large wooden obstacle with COMAN, which is a complaint humanoid robot with 23 degrees of freedom (DOF). We also propose a generalization method, the \Policy-Contraction Learning Method" to extend the algorithm for generating new multi-contact plans for our multi-contact motion planner, that can adapt to changes in the environment. The method learns a general policy and the multi-contact behavior from the human demonstrations, for generating new multi-contact plans for the obstacle-negotiation