Blind walking of a planar biped on sloped terrain

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1998.Includes bibliographical references (leaf 66).by Chee-Meng Chew.M.S

Trajectory Optimization and Machine Learning to Design Feedback Controllers for Bipedal Robots with Provable Stability

This thesis combines recent advances in trajectory optimization of hybrid dynamical systems with machine learning and geometric control theory to achieve unprecedented performance in bipedal robot locomotion. The work greatly expands the class of robot models for which feedback controllers can be designed with provable stability. The methods are widely applicable beyond bipedal robots, including exoskeletons, and prostheses, and eventually, drones, ADAS, and other highly automated machines. One main idea of this thesis is to greatly expand the use of multiple trajectories in the design of a stabilizing controller. The computation of many trajectories is now feasible due to new optimization tools. The computations are not fast enough to apply in the real-time, however, so they are not feasible for model predictive control (MPC). The offline “library” approach will encounter the curse of dimensionality for the high-dimensional models common in bipedal robots. To overcome these obstructions, we embed a stable walking motion in an attractive low-dimensional surface of the system's state space. The periodic orbit is now an attractor of the low-dimensional state-variable model but is not attractive in the full-order system. We then use the special structure of mechanical models associated with bipedal robots to embed the low-dimensional model in the original model in such a manner that the desired walking motions are locally exponentially stable. The ultimate solution in this thesis will generate model-based feedback controllers for bipedal robots, in such a way that the closed-loop system has a large stability basin, exhibits highly agile, dynamic behavior, and can deal with significant perturbations coming from the environment. In the case of bipeds: “model-based” means that the controller will be designed on the basis of the full floating-base dynamic model of the robot, and not a simplified model, such as the LIP (Linear Inverted Pendulum). By “agile and dynamic” is meant that the robot moves at the speed of a normal human or faster while walking off a curb. By “significant perturbation” is meant a human tripping, and while falling, throwing his/her full weight into the back of the robot.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145992/1/xda_1.pd

Legged locomotion over irregular terrains: State of the art of human and robot performance

Legged robotic technologies have moved out of the lab to operate in real environments, characterized by a wide variety of unpredictable irregularities and disturbances, all this in close proximity with humans. Demonstrating the ability of current robots to move robustly and reliably in these conditions is becoming essential to prove their safe operation. Here, we report an in-depth literature review aimed at verifying the existence of common or agreed protocols and metrics to test the performance of legged system in realistic environments. We primarily focused on three types of robotic technologies, i.e., hexapods, quadrupeds and bipeds. We also included a comprehensive overview on human locomotion studies, being it often considered the gold standard for performance, and one of the most important sources of bioinspiration for legged machines. We discovered that very few papers have rigorously studied robotic locomotion under irregular terrain conditions. On the contrary, numerous studies have addressed this problem on human gait, being nonetheless of highly heterogeneous nature in terms of experimental design. This lack of agreed methodology makes it challenging for the community to properly assess, compare and predict the performance of existing legged systems in real environments. On the one hand, this work provides a library of methods, metrics and experimental protocols, with a critical analysis on the limitations of the current approaches and future promising directions. On the other hand, it demonstrates the existence of an important lack of benchmarks in the literature, and the possibility of bridging different disciplines, e.g., the human and robotic, towards the definition of standardized procedure that will boost not only the scientific development of better bioinspired solutions, but also their market uptake

Tractable Quantification of Metastability for Robust Bipedal Locomotion

This work develops tools to quantify and optimize performance metrics for bipedal walking, toward enabling improved practical and autonomous operation of two-legged robots in real-world environments. While speed and energy efficiency of legged locomotion are both useful and straightforward to quantify, measuring robustness is arguably more challenging and at least as critical for obtaining practical autonomy in variable or otherwise uncertain environmental conditions, including rough terrain. The intuitive and meaningful robustness quantification adopted in this thesis begins by stochastic modeling of disturbances such as terrain variations, and conservatively defining what a failure is, for example falling down, slippage, scuffing, stance foot rotation, or a combination of such events. After discretizing the disturbance and state sets by meshing, step-to-step dynamics are studied to treat the system as a Markov chain. Then, failure rates can be easily quantified by calculating the expected number of steps before failure. Once robustness is measured, other performance metrics can also be easily incorporated into the cost function for optimization.For high performance and autonomous operation under variations, we adopt a capacious framework, exploiting a hierarchical control structure. The low-level controllers, which use only proprioceptive (internal state) information, are optimized by a derivative-free method without any constraints. For practicability of this process, developing an algorithm for fast and accurate computation of our robustness metric was a crucial and necessary step. While the outcome of optimization depends on capabilities of the controller scheme employed, the convenient and time-invariant parameterization presented in this thesis ensures accommodating large terrain variations. In addition, given environment estimation and state information, the high-level control is a behavioral policy to choose the right low-level controller at each step. In this thesis, optimal switching policies are determined by applying dynamic programming tools on Markov decision processes obtained through discretization. For desirable performance in practice from policies that are formed using meshing-based approximation to the true dynamics, robustness of high-level control to environment estimation and discretization errors are ensured by modeling stochastic noise in the terrain information and belief state while solving for behavioral policies

The Design and Realization of a Sensitive Walking Platform

Legged locomotion provides robots with the capability of adapting to different terrain conditions. General complex terrain traversal methodologies solely rely on proprioception which readily leads to instability under dynamical situations. Biological legged locomotion utilizes somatosensory feedback to sense the real-time interaction of the feet with ground to enhance stability. Nevertheless, limited attention has been given to sensing the feet-terrain interaction in robotics. This project introduces a paradigm shift in robotic walking called sensitive walking realized through the development of a compliant bipedal platform. Sensitive walking extends upon the success of sensitive manipulation which utilizes tactile feedback to localize an object to grasp, determine an appropriate manipulation configuration, and constantly adapts to maintain grasp stability. Based on the same concepts of sensitive manipulation, sensitive walking utilizes podotactile feedback to enhance real-time walking stability by effectively adapting to variations in the terrain. Adapting legged robotic platforms to sensitive walking is not as simple as attaching any tactile sensor to the feet of a robot. The sensors and the limbs need to have specific characteristics that support the implementation of the algorithms and allow the biped to safely come in contact with the terrain and detect the interaction forces. The challenges in handling the synergy of hardware and sensor design, and fabrication in a podotactile-based sensitive walking robot are addressed. The bipedal platform provides contact compliance through 12 series elastic actuators and contains 190 highly flexible tactile sensors capable of sensing forces at any incident angle. Sensitive walking algorithms are provided to handle multi-legged locomotion challenges including stairs and irregular terrain

Bipedal humanoid robot control by fuzzy adjustment of the reference walking plane

The two-legged humanoid structure has advantages for an assistive robot in the human living and working environment. A bipedal humanoid robot can avoid typical obstacles at homes and offices, reach consoles and appliances designed for human use and can be carried in human transport vehicles. Also, it is speculated that the absorption of robots in the human shape into the human society can be easier than that of other artificial forms. However, the control of bipedal walk is a challenge. Walking performance on solely even floor is not satisfactory. The complications of obtaining a balanced walk are dramatically more pronounced on uneven surfaces like inclined planes, which are quite commonly encountered in human surroundings. The difficulties lie in a variety of tasks ranging from sensor and data fusion to the design of adaptation systems which respond to changing surface conditions. This thesis presents a study on bipedal walk on inclined planes with changing slopes. A Zero Moment Point (ZMP) based gait synthesis technique is employed. The pitch angle reference for the foot sole plane −as expressed in a coordinate frame attached at the robot body − is adjusted online by a fuzzy logic system to adapt to different walking surface slopes. Average ankle pitch torques and the average value of the body pitch angle, computed over a history of a predetermined number of sampling instants, are used as the inputs to this system. The proposed control method is tested via walking experiments with the 29 degreesof- freedom (DOF) human-sized full-body humanoid robot SURALP (Sabanci University Robotics Research Laboratory Platform). Experiments are performed on even floor and inclined planes with different slopes. The results indicate that the approach presented is successful in enabling the robot to stably enter, ascend and leave inclined planes with 15 percent (8.5 degrees) grade. The thesis starts with a terminology section on bipedal walking and introduces a number of successful humanoid robot projects. A survey of control techniques for the walk on uneven surfaces is presented. The design and construction of the experimental robotic platform SURALP is discussed with the mechanical, electronic, walking reference generation and control aspects. The fuzzy reference adjustment system proposed for the walk on inclined planes is detailed and experimental results are presented

Metastable legged-robot locomotion

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 195-215).A variety of impressive approaches to legged locomotion exist; however, the science of legged robotics is still far from demonstrating a solution which performs with a level of flexibility, reliability and careful foot placement that would enable practical locomotion on the variety of rough and intermittent terrain humans negotiate with ease on a regular basis. In this thesis, we strive toward this particular goal by developing a methodology for designing control algorithms for moving a legged robot across such terrain in a qualitatively satisfying manner, without falling down very often. We feel the definition of a meaningful metric for legged locomotion is a useful goal in and of itself. Specifically, the mean first-passage time (MFPT), also called the mean time to failure (MTTF), is an intuitively practical cost function to optimize for a legged robot, and we present the reader with a systematic, mathematical process for obtaining estimates of this MFPT metric. Of particular significance, our models of walking on stochastically rough terrain generally result in dynamics with a fast mixing time, where initial conditions are largely "forgotten" within 1 to 3 steps. Additionally, we can often find a near-optimal solution for motion planning using only a short time-horizon look-ahead. Although we openly recognize that there are important classes of optimization problems for which long-term planning is required to avoid "running into a dead end" (or off of a cliff!), we demonstrate that many classes of rough terrain can in fact be successfully negotiated with a surprisingly high level of long-term reliability by selecting the short-sighted motion with the greatest probability of success. The methods used throughout have direct relevance to machine learning, providing a physics-based approach to reduce state space dimensionality and mathematical tools to obtain a scalar metric quantifying performance of the resulting reduced-order system.by Katie Byl.Ph.D