Resolved Motion Control for 3D Underactuated Bipedal Walking using Linear Inverted Pendulum Dynamics and Neural Adaptation

We present a framework to generate periodic trajectory references for a 3D under-actuated bipedal robot, using a linear inverted pendulum (LIP) based controller with adaptive neural regulation. We use the LIP template model to estimate the robot's center of mass (CoM) position and velocity at the end of the current step, and formulate a discrete controller that determines the next footstep location to achieve a desired walking profile. This controller is equipped on the frontal plane with a Neural-Network-based adaptive term that reduces the model mismatch between the template and physical robot that particularly affects the lateral motion. Then, the foot placement location computed for the LIP model is used to generate task space trajectories (CoM and swing foot trajectories) for the actual robot to realize stable walking. We use a fast, real-time QP-based inverse kinematics algorithm that produces joint references from the task space trajectories, which makes the formulation independent of the knowledge of the robot dynamics. Finally, we implemented and evaluated the proposed approach in simulation and hardware experiments with a Digit robot obtaining stable periodic locomotion for both cases.Comment: 7 pages, to appear in IROS 202

Safe Whole-Body Task Space Control for Humanoid Robots

Complex robotic systems require whole-body controllers to deal with contact interactions, handle closed kinematic chains, and track task-space control objectives. However, for many applications, safety-critical controllers are important to steer away from undesired robot configurations to prevent unsafe behaviors. A prime example is legged robotics, where we can have tasks such as balance control, regulation of torso orientation, and, most importantly, walking. As the coordination of multi-body systems is non-trivial, following a combination of those tasks might lead to configurations that are deemed dangerous, such as stepping on its support foot during walking, leaning the torso excessively, or producing excessive centroidal momentum, resulting in non-human-like walking. To address these challenges, we propose a formulation of an inverse dynamics control enhanced with exponential control barrier functions for robotic systems with numerous degrees of freedom. Our approach utilizes a quadratic program that respects closed kinematic chains, minimizes the control objectives, and imposes desired constraints on the Zero Moment Point, friction cone, and torque. More importantly, it also ensures the forward invariance of a general user-defined high Relative-Degree safety set. We demonstrate the effectiveness of our method by applying it to the 3D biped robot Digit, both in simulation and with hardware experiments.Comment: 8 pages, 12 figure

Safe Path Planning for Polynomial Shape Obstacles via Control Barrier Functions and Logistic Regression

Safe path planning is critical for bipedal robots to operate in safety-critical environments. Common path planning algorithms, such as RRT or RRT*, typically use geometric or kinematic collision check algorithms to ensure collision-free paths toward the target position. However, such approaches may generate non-smooth paths that do not comply with the dynamics constraints of walking robots. It has been shown that the control barrier function (CBF) can be integrated with RRT/RRT* to synthesize dynamically feasible collision-free paths. Yet, existing work has been limited to simple circular or elliptical shape obstacles due to the challenging nature of constructing appropriate barrier functions to represent irregular-shaped obstacles. In this paper, we present a CBF-based RRT* algorithm for bipedal robots to generate a collision-free path through complex space with polynomial-shaped obstacles. In particular, we used logistic regression to construct polynomial barrier functions from a grid map of the environment to represent arbitrarily shaped obstacles. Moreover, we developed a multi-step CBF steering controller to ensure the efficiency of free space exploration. The proposed approach was first validated in simulation for a differential drive model, and then experimentally evaluated with a 3D humanoid robot, Digit, in a lab setting with randomly placed obstacles.Comment: 7 pages, 8 figures. Supplemental Video: https://youtu.be/r_hkuK5cMw

Real-Time Navigation for Bipedal Robots in Dynamic Environments

The popularity of mobile robots has been steadily growing, with these robots being increasingly utilized to execute tasks previously completed by human workers. For bipedal robots to see this same success, robust autonomous navigation systems need to be developed that can execute in real-time and respond to dynamic environments. These systems can be divided into three stages: perception, planning, and control. A holistic navigation framework for bipedal robots must successfully integrate all three components of the autonomous navigation problem to enable robust real-world navigation. In this paper, we present a real-time navigation framework for bipedal robots in dynamic environments. The proposed system addresses all components of the navigation problem: We introduce a depth-based perception system for obstacle detection, mapping, and localization. A two-stage planner is developed to generate collision-free trajectories robust to unknown and dynamic environments. And execute trajectories on the Digit bipedal robot's walking gait controller. The navigation framework is validated through a series of simulation and hardware experiments that contain unknown environments and dynamic obstacles.Comment: Submitted to 2023 IEEE International Conference on Robotics and Automation (ICRA). For associated experiment recordings see https://www.youtube.com/watch?v=WzHejHx-Kz

The Interplay between Urban Development Patterns and Vulnerability to Flood Risk in Kisumu City, Kenya

Flooding is becoming a predominantly urban event in recent times. However, the reason why urban areas are becoming places of flood risk has not been clearly understood. Even though past studies had explained the flood risk as a function of the natural and physical environment, more recent studies are now attributing the escalation of urban flood risk to the overall patterns of these areas. Different urban development processes yield dissimilar urban patterns, but how these disparate urban patterns within the same town configure flood risk has not been fully explored. This study attempts to fill this research gap and explores how development processes create urban spatial patterns and further examine how such patterns shape flood risk in Kisumu city. Keywords: Urban development, spatial patterns, flood risk

Variation in Termination of Brachial Artery Among Black African Population

Background:As the main arterial supply of the upper limb, brachial artery (BA) terminates by dividing into ulnar and radial artery about 1cm at the neck of radius as documented in standard anatomy text books. However, due to variations in results from different studies, BA may terminate at the level of neck of radius, radial tuberosity, mid arm and proximal arm. Based on its clinical utility such as blood pressure monitoring and surgical procedures, few reported disparities in certain populations and paucity data especially in black African population, exploration of variations in termination of BA is warranted.Objective:The purpose of this study was to evaluate variation in termination of brachial artery among black African population.Methodology:This was a cross sectional descriptive study carried out in human anatomy laboratories in Maseno, Uzima and Masinde muliro universities in Western Kenya. In this study, 77 cadavers constituting (n=154) upper limb specimens of back African population were sampled using stratified sampling technique. Data on termination of BA, laterality of the upper limb and sex of the cadaver were recorded in data entry form. Brachium region was exposed to access the brachial artery where its course to termination was assessed. Descriptive statistics was used to assess frequency distribution of variant termination while Chi-square was used to determine difference in proportion of normal termination and cumulative variation of termination of BA with regards to laterality of the upper limb. Results:Out of 154 upper limbs studied, the majority (89.0%) had a normal termination at the radial neck, while 7.8% terminated at the radial tuberosity. A small percentage of the upper limbs (1.3% and 1.9%) had termination at midarm and proximal arm, respectively. There was no statistically significant difference in variation in the left and right limbs (p=0.333 and p= 0.564) respectively relative to the normal termination.Conclusion and recommendations: There are variations in termination of brachial artery among the black African population, however, the variation from the normal morphology is not statistically significant, though clinically significant. Termination at radial tuberosity is the most common variant and more common in men than women. Understanding variant termination of BA among black African population is key to all health care professionals especially surgeons, radiologists, anatomists and medical students as such variants may lead to misdiagnosis and post operative related complications. Thus, further population and race specific studies need to be undertaken on such variants. Keywords:Brachial artey, Ulnar artery, Radial artery, Radial tuberosity, Radial neck, Midarm. DOI:10.7176/JNSR/14-12-03 Publication date:September 30th 202

Enhancing the performance of a safe controller via supervised learning for truck lateral control

Correct-by-construction techniques, such as control barrier functions (CBFs), can be used to guarantee closed-loop safety by acting as a supervisor of an existing or legacy controller. However, supervisory-control intervention typically compromises the performance of the closed-loop system. On the other hand, machine learning has been used to synthesize controllers that inherit good properties from a training dataset, though safety is typically not guaranteed due to the difficulty of analyzing the associated neural network. In this paper, supervised learning is combined with CBFs to synthesize controllers that enjoy good performance with provable safety. A training set is generated by trajectory optimization that incorporates the CBF constraint for an interesting range of initial conditions of the truck model. A control policy is obtained via supervised learning that maps a feature representing the initial conditions to a parameterized desired trajectory. The learning-based controller is used as the performance controller and a CBF-based supervisory controller guarantees safety. A case study of lane keeping for articulated trucks shows that the controller trained by supervised learning inherits the good performance of the training set and rarely requires intervention by the CBF supervisorComment: submitted to IEEE Transaction of Control System Technolog

On Safety Testing, Validation, and Characterization with Scenario-Sampling: A Case Study of Legged Robots

The dynamic response of the legged robot locomotion is non-Lipschitz and can be stochastic due to environmental uncertainties. To test, validate, and characterize the safety performance of legged robots, existing solutions on observed and inferred risk can be incomplete and sampling inefficient. Some formal verification methods suffer from the model precision and other surrogate assumptions. In this paper, we propose a scenario sampling based testing framework that characterizes the overall safety performance of a legged robot by specifying (i) where (in terms of a set of states) the robot is potentially safe, and (ii) how safe the robot is within the specified set. The framework can also help certify the commercial deployment of the legged robot in real-world environment along with human and compare safety performance among legged robots with different mechanical structures and dynamic properties. The proposed framework is further deployed to evaluate a group of state-of-the-art legged robot locomotion controllers from various model-based, deep neural network involved, and reinforcement learning based methods in the literature. Among a series of intended work domains of the studied legged robots (e.g. tracking speed on sloped surface, with abrupt changes on demanded velocity, and against adversarial push-over disturbances), we show that the method can adequately capture the overall safety characterization and the subtle performance insights. Many of the observed safety outcomes, to the best of our knowledge, have never been reported by the existing work in the legged robot literature