A Stability-Estimator to Unify Humanoid Locomotion: Walking, Stair-Climbing and Ladder-Climbing

The field of Humanoid robotics research has often struggled to find a unique niche that is not better served by other forms of robot. Unlike more traditional industrials robots with a specific purpose, a humanoid robot is not necessarily optimized for any particular task, due to the complexity and balance issues of being bipedal. However, the versatility of a humanoid robot may be ideal for applications such as search and rescue. Disaster sites with chemical, biological, or radiation contamination mean that human rescue workers may face untenable risk. Using a humanoid robot in these dangerous circumstances could make emergency response faster and save human lives. Despite the many successes of existing mobile robots in search and rescue, stair and ladder climbing remains a challenging task due to their form. To execute ladder climbing motions effectively, a humanoid robot requires a reliable estimate of stability. Traditional methods such as Zero Moment Point are not applicable to vertical climbing, and do not account for force limits imposed on end-effectors. This dissertation implements a simple contact wrench space method using a linear combination of contact wrenches. Experiments in simulation showed ZMP equivalence on flat ground. Furthermore, the estimator was able to predict stability with four point contact on a vertical ladder. Finally, an extension of the presented method is proposed based on these findings to address the limitations of the linear combination.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Optimization of Humanoid's Motions under Multiple Constraints in Vehicle-Handling Task

In this dissertation, an approach on whole body motion optimization is presented for humanoid vehicle-handling task. To achieve this goal, the author built a reinforcement-learning-agent based trajectory-optimization framework. The framework planned and optimized a guideline input trajectory with respect to various kinematic and dynamic constraints. A path planner module designed an initial suboptimal motion. Reinforcement learning was then implemented to optimize the trajectories with respect to time-varying constraints at the body and joint level. The cost functions in the body level calculated a robot's static balancing ability, collisions and validity of the end-effector movement. Quasi-static balancing and collisions were computed from kinematic models of the robot and the vehicle. Various costs such as joint angle and velocity limits were computed in the joint level. Energy consumption such as torque limit obedience was also checked at the joint level. Such physical limits of each joint ensured both spatial and temporal smoothness of the generated trajectories. Keeping overall structure of the framework, cost functions and learning algorithm were selected adaptively based on the requirements of given tasks. After the optimization process, experimental tests of the presented approach are demonstrated through simulations using a virtual robot model. Verification-and-validation process then confirmed the efficacy of the optimized trajectory approach using the robot's real physical platform. For both test and verification process, different types of robot and vehicle were used to prove potentials for extension of the trajectory-optimization framework.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Mechatronics Design Process with Energy Optimization for Industrial Machines

The need for designing industrial machines with higher energy efficiency, reliability, flexibility, and accuracy has increased to satisfy market demand for higher productivity at reduced costs in a sustainable manner. As machines become more complex, model-based design is essential to overcome the challenges in mechatronic system design. However, a well-designed mechanical system with a well-designed and tuned control system are not sufficient for machines to operate at high-performance conditions; this also heavily depends on trajectory planning and the appropriate selection of the motors controlling the axes of the machine. In this work, a model-based design approach to properly select motors for single-axes or multi-axes coordinated systems was proposed. Additionally, a trajectory planning approach was also proposed to improve performance of industrial machines. The proposed motor selection process and trajectory planning approach were demonstrated via modeling, simulation, and experimental validation for three systems: two-inertia system, planar robot, and self-balancing transporter. Over 25% of the electric energy delivered in the U.S. in 2013 was used in the industrial sector according to the U.S. Energy Information Administration, with an estimated efficiency of 80% according to the Lawrence Livermore National Laboratory. This entails major responsibility by the industry to utilize energy efficiently and promote sustainable energy usage. To help improve the energy efficiency in the industrial sector, a novel method to optimize the energy of single-axis and multi-axis coordinated systems of industrial machines was developed. Based on trajectory boundaries and the kinetic model of the mechanism and motors, this proposed energy optimization method performs iterations to recalculate the shape of the motion profile for each motor of the system being optimized until it converges to a motion profile with optimal energy cost and within these boundaries. This method was validated by comparing the energy consumption of those three systems while commanded by the optimized motion profile and then by motion profiles typically used in industrial applications. The energy saved was between 5% and 10%. The implementation cost of this method in industrial systems resides in machine-code changes; no physical changes are needed