Master of Science

thesisThis research studies the passive dynamics of an under-actuated trotting quadruped. The goal of this project is to perform three-dimensional (3D) dynamic simulations of a trotting quadruped robot to find proper leg configurations and stiffness range, in order to achieve stable trotting gait. First, a 3D simulation framework that includes all the six degrees of freedom of the body is introduced. Directionally compliant legs together with different leg configurations are employed to achieve passive stability. Compliant legs passively support the body during stance phase and during flight phase a motor is used to retract the legs. Leg configurations in the robot's sagittal and frontal plane are introduced. Numerical experiments are conducted to search the design space of the leg, focusing on increasing the passive stability of the robot. Increased stability is defined as decreased pitching, rolling, and yawing motion of the robot. The results indicate that optimized leg parameters can guarantee passive stable trotting with reduced roll, pitch, and yaw. Studies suggest that a quadruped robot with compliant legs is dynamically stable while trotting. Results indicate that the robot based on a biological model (i.e., caudal inclination of humeri and cranial inclination of femora) has the best performance. Stiff springs at hips and shoulders, soft spring at knees and elbows, and stiff springs at ankles and wrists are recommended. The results of this project provide a conceptual framework for understanding the movements of a trotting quadruped

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

LeggedWalking on Inclined Surfaces

The main contribution of this MS Thesis is centered around taking steps towards successful multi-modal demonstrations using Northeastern's legged-aerial robot, Husky Carbon. This work discusses the challenges involved in achieving multi-modal locomotion such as trotting-hovering and thruster-assisted incline walking and reports progress made towards overcoming these challenges. Animals like birds use a combination of legged and aerial mobility, as seen in Chukars' wing-assisted incline running (WAIR), to achieve multi-modal locomotion. Chukars use forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and overhangs. Husky's design takes inspiration from birds such as Chukars. This MS thesis presentation outlines the mechanical and electrical details of Husky's legged and aerial units. The thesis presents simulated incline walking using a high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.Comment: Masters thesi

Thrust control, stabilization and energetics of a quadruped running robot

In order to achieve powered autonomous running robots it is essential to develop efficient actuator systems, especially for generating the radial thrust in the legs. In addition, the control of the radial thrust of the legs can be a simple, effective method for stabilizing the body pitch in a running gait. This paper presents the mechanical systems, models and control strategies employed to generate and control leg thrust in the KOLT quadruped running robot. An analytical model of the electro-pneumatic leg thrusting system is presented and analyzed to evaluate its performance and to facilitate the design of control strategies. Several experiments have been conducted to estimate the energy losses and determine their origins as well as to compute the energetic efficiency of the actuation system. Two thrust control methods are also proposed and tested experimentally. The closed loop method regulates thrust through the control of the hip liftoff speed, a conceptually simple control strategy that stabilizes the body pitch in pronk and trot gaits without the need for central feedback, even on irregular terrain. The open-loop control method regulates the energy added in each hop based on the model of the actuator system. The efficacy of these models and techniques is tested in several planar trot and pronk experiments, and the results are analyzed focusing on the body stabilization, the power consumption and the energetic efficiency. © SAGE Publications 2008 Los Angeles

Learning dynamic motor skills for terrestrial locomotion

The use of Deep Reinforcement Learning (DRL) has received significantly increased attention from researchers within the robotics field following the success of AlphaGo, which demonstrated the superhuman capabilities of deep reinforcement algorithms in terms of solving complex tasks by beating professional GO players. Since then, an increasing number of researchers have investigated the potential of using DRL to solve complex high-dimensional robotic tasks, such as legged locomotion, arm manipulation, and grasping, which are difficult tasks to solve using conventional optimization approaches. Understanding and recreating various modes of terrestrial locomotion has been of long-standing interest to roboticists. A large variety of applications, such as rescue missions, disaster responses and science expeditions, strongly demand mobility and versatility in legged locomotion to enable task completion. In order to create useful physical robots, it is necessary to design controllers to synthesize the complex locomotion behaviours observed in humans and other animals. In the past, legged locomotion was mainly achieved via analytical engineering approaches. However, conventional analytical approaches have their limitations, as they require relatively large amounts of human effort and knowledge. Machine learning approaches, such as DRL, require less human effort compared to analytical approaches. The project conducted for this thesis explores the feasibility of using DRL to acquire control policies comparable to, or better than, those acquired through analytical approaches while requiring less human effort. In this doctoral thesis, we developed a Multi-Expert Learning Architecture (MELA) that uses DRL to learn multi-skill control policies capable of synthesizing a diverse set of dynamic locomotion behaviours for legged robots. We first proposed a novel DRL framework for the locomotion of humanoid robots. The proposed learning framework is capable of acquiring robust and dynamic motor skills for humanoids, including balancing, walking, standing-up fall recovery. We subsequently improved upon the learning framework and design a novel multi-expert learning architecture that is capable of fusing multiple motor skills together in a seamless fashion and ultimately deploy this framework on a real quadrupedal robot. The successful deployment of learned control policies on a real quadrupedal robot demonstrates the feasibility of using an Artificial Intelligence (AI) based approach for real robot motion control

System Design of a Cheetah Robot Toward Ultra-high Speed

High-speed legged locomotion pushes the limits of the most challenging problems of design and development of the mechanism, also the control and the perception method. The cheetah is an existence proof of concept of what we imitate for high-speed running, and provides us lots of inspiration on design. In this paper, a new model of a cheetah-like robot is developed using anatomical analysis and design. Inspired by a biological neural mechanism, we propose a novel control method for controlling the muscles' flexion and extension, and simulations demonstrate good biological properties and leg's trajectory. Next, a cheetah robot prototype is designed and assembled with pneumatic muscles, a musculoskeletal structure, an antagonistic muscle arrangement and a J-type cushioning foot. Finally, experiments of the robot legs swing and kick ground tests demonstrate its natural manner and validate the design of the robot. In the future, we will test the bounding behaviour of a real legged system