Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

Control of Humanoid Robots for Use in Unstructured Environments

Humanoid robots have the potential to replace human beings for dangerous tasks, such as disaster relief. One of the most important abilities for a humanoid robot is the ability to manipulate its surroundings. We developed human-in-the-loop techniques for the Atlas platform to compete in the DARPA Robotics Challenge. Many of the tasks in the event required actuation of the environment, such as turning a valve, pulling a lever, and opening a door. This paper will detail our work on manipulation for humanoid robots. In particular, we will discuss our approaches to effective operator interface design, manipulation techniques and motion planning

Design and Implement Towards Enhanced Physical Interactive Performance Robot Bodies

In this thesis, it will introduce the design principle and implement details towards enhanced physical interactive performance robot bodies, which are more specically focused on under actuated principle robotic hands and articulated leg robots. Since they both signicantly function as the physical interactive robot bodies against external environment, while their current performance can hardly satisfy the requirement of undertaking missions in real application. Regarding to the enhanced physical interactive performances, my work will emphasis on the three following specific functionalities, high energy efficiency, high strength and physical sturdiness in both robotics actuation and mechanism. For achieving the aforementioned targets, multiple design methods have been applied, rstly the elastic energy storage elements and compliant actuation have been adopted in legged robots as Asymmetrical Compliant Actuation (ACA), implemented for not only single joint but also multiple joints as mono and biarticulation congurations in order to achieve higher energy effciency motion. Secondly the under actuated principle and modular nger design concept have been utilized on the development of robotic hands for enhancing the grasping strength and physical sturdiness meanwhile maintaining the manipulation dexterity. Lastly, a novel high payload active tuning Parallel Elastic Actuation (PEA) and Series Elastic Actuation (SEA) have been adopted on legged robots for augmenting energy eciency and physical sturdiness. My thesis contribution relies on the novel design and implement of robotics bodies for enhancing physical interactive performance and we experimentally veried the design effectiveness in specic designed scenario and practical applications

Introduction to the Use of Robotic Tools for Search and Rescue

Modern search and rescue workers are equipped with a powerful toolkit to address natural and man-made disasters. This introductory chapter explains how a new tool can be added to this toolkit: robots. The use of robotic assets in search and rescue operations is explained and an overview is given of the worldwide efforts to incorporate robotic tools in search and rescue operations. Furthermore, the European Union ICARUS project on this subject is introduced. The ICARUS project proposes to equip first responders with a comprehensive and integrated set of unmanned search and rescue tools, to increase the situational awareness of human crisis managers, such that more work can be done in a shorter amount of time. The ICARUS tools consist of assistive unmanned air, ground, and sea vehicles, equipped with victim-detection sensors. The unmanned vehicles collaborate as a coordinated team, communicating via ad hoc cognitive radio networking. To ensure optimal human-robot collaboration, these tools are seamlessly integrated into the command and control equipment of the human crisis managers and a set of training and support tools is provided to them to learn to use the ICARUS system

Chapter Introduction to the Use of Robotic Tools for Search and Rescue

Modern search and rescue workers are equipped with a powerful toolkit to address natural and man-made disasters. This introductory chapter explains how a new tool can be added to this toolkit: robots. The use of robotic assets in search and rescue operations is explained and an overview is given of the worldwide efforts to incorporate robotic tools in search and rescue operations. Furthermore, the European Union ICARUS project on this subject is introduced. The ICARUS project proposes to equip first responders with a comprehensive and integrated set of unmanned search and rescue tools, to increase the situational awareness of human crisis managers, such that more work can be done in a shorter amount of time. The ICARUS tools consist of assistive unmanned air, ground, and sea vehicles, equipped with victim-detection sensors. The unmanned vehicles collaborate as a coordinated team, communicating via ad hoc cognitive radio networking. To ensure optimal human-robot collaboration, these tools are seamlessly integrated into the command and control equipment of the human crisis managers and a set of training and support tools is provided to them to learn to use the ICARUS system

XTENTH-CAR: A Proportionally Scaled Experimental Vehicle Platform for Connected Autonomy and All-Terrain Research

Connected Autonomous Vehicles (CAVs) are key components of the Intelligent Transportation System (ITS), and all-terrain Autonomous Ground Vehicles (AGVs) are indispensable tools for a wide range of applications such as disaster response, automated mining, agriculture, military operations, search and rescue missions, and planetary exploration. Experimental validation is a requisite for CAV and AGV research, but requires a large, safe experimental environment when using full-size vehicles which is time-consuming and expensive. To address these challenges, we developed XTENTH-CAR (eXperimental one-TENTH scaled vehicle platform for Connected autonomy and All-terrain Research), an open-source, cost-effective proportionally one-tenth scaled experimental vehicle platform governed by the same physics as a full-size on-road vehicle. XTENTH-CAR is equipped with the best-in-class NVIDIA Jetson AGX Orin System on Module (SOM), stereo camera, 2D LiDAR and open-source Electronic Speed Controller (ESC) with drivers written for both versions of the Robot Operating System (ROS 1 & ROS 2) to facilitate experimental CAV and AGV perception, motion planning and control research, that incorporate state-of-the-art computationally expensive algorithms such as Deep Reinforcement Learning (DRL). XTENTH-CAR is designed for compact experimental environments, and aims to increase the accessibility of experimental CAV and AGV research with low upfront costs, and complete Autonomous Vehicle (AV) hardware and software architectures similar to the full-sized X-CAR experimental vehicle platform, enabling efficient cross-platform development between small-scale and full-scale vehicles.Comment: $\copyright$ 2023 ASME. This work has been accepted to ASME for publicatio