Modeling, Estimation, and Control for Legged Locomotion
within Non-inertial Environments
This project focuses on creating new methods to model, estimate, and control the movement of legged robots for enabling stable locomotion in non-inertial environments where the ground moves in the inertial frame. While today’s legged robot systems have demonstrated remarkable capabilities in traversing stationary surfaces (e.g., stairs, sand, and grass), legged locomotion in non-inertial environments (e.g., ships, aircraft, and trains) is a new robot functionality that has not been tackled. This new functionality will empower legged robots to negotiate complex, dynamic human environments (that are prohibitively challenging for wheeled or tracked robots) to allow them to aid in numerous critical high-risk applications, such as shipboard firefighting and fire suppression, cleaning/disinfection of public transportation vehicles to contain the spread of infectious diseases, and surveillance and monitoring aboard these vehicles for public security support. Enabling such functionality demands reliable robot estimation and control, which are substantially challenging due to the high complexity of the associated robot behaviors that are hybrid (involving continuous leg-swinging motions and discrete foot-landing events) and subject to the time-varying ground movement.
The research goal of the project is to draw upon dynamic modeling, state estimation, feedback control, and theory of hybrid systems to advance the control theory of legged robots in order to realize provably stable legged locomotion on a DRS. To achieve the research goal, four main objectives will be pursued: (i) formulation of a physics-based model that captures the hybrid, time-varying robot dynamics associated with legged locomotion on a DRS; (ii) creation of new methods of designing state estimators that achieve real-time state estimation with convergence guarantees by provably expanding invariant filtering methodology from continuous systems to hybrid dynamical systems that include legged robots moving in non-inertial environments; (iii) derivation of a Lyapunov-based controller design methodology to produce stable locomotion on a DRS by handling the hybrid, time-varying robot dynamics under uncertainties that reside in both continuous phases and discrete events; and (iv) integration of the modeling, state estimation, and controller design into a model-based framework that provably sustains legged locomotion on a dynamic ground.
The research goal of the project is to draw upon dynamic modeling, state estimation, feedback control, and theory of hybrid systems to advance the control theory of legged robots in order to realize provably stable legged locomotion on a DRS. To achieve the research goal, four main objectives will be pursued: (i) formulation of a physics-based model that captures the hybrid, time-varying robot dynamics associated with legged locomotion on a DRS; (ii) creation of new methods of designing state estimators that achieve real-time state estimation with convergence guarantees by provably expanding invariant filtering methodology from continuous systems to hybrid dynamical systems that include legged robots moving in non-inertial environments; (iii) derivation of a Lyapunov-based controller design methodology to produce stable locomotion on a DRS by handling the hybrid, time-varying robot dynamics under uncertainties that reside in both continuous phases and discrete events; and (iv) integration of the modeling, state estimation, and controller design into a model-based framework that provably sustains legged locomotion on a dynamic ground.
Sponsors: National Science Foundation, Office of Naval Research.
Experiment videos:
Global-Position Tracking Control for Versatile Locomotion
This project aims to contribute to the advancement of the national health, security and welfare, by improving capabilities, safety and reliability of bipedal walking robots. Bipedal robots are emerging as a critical technology for a broad range of important applications, such as search and rescue, emergency response, home assistance and health care. This project will address important technological challenges of controlling and tracking robot movements in various surroundings. The resulting highly versatile walking robot will be able to avoid obstacles, such as avoid pedestrians in human-populated environments and falling debris on disaster sites. This project will integrate the research with education and outreach activities for engaging students from diverse groups to participate in robotics and control research.
The goal of this project is to advance the knowledge in the control of hybrid systems for achieving highly versatile bipedal robotic walking through reliable global-position tracking control. The research draws upon feedback control theory and hybrid systems to create a systematic method of tracking controller design for general hybrid systems with state-triggered jumps that include bipedal walking robots. To reach the research goal, the following objectives will be pursued: (1) to formulate a new method of reference trajectory modification for solving the intermittent tracking-error divergence issue underlying the trajectory tracking problem of hybrid systems with state-triggered jumps; (2) to extend Lyapunov theory to general hybrid systems with state-trigged jumps for creating a large class of controllers that can provably solve the tracking problem; and (3) to synthesize a control framework that exploits the researched controller design to achieve high versatility simultaneously with provable stability, agility, and energy efficiency for bipedal robotic walking. This project will lay a foundation for the creation of next-generation legged robot systems capable of safe and reliable real-world operations.
The goal of this project is to advance the knowledge in the control of hybrid systems for achieving highly versatile bipedal robotic walking through reliable global-position tracking control. The research draws upon feedback control theory and hybrid systems to create a systematic method of tracking controller design for general hybrid systems with state-triggered jumps that include bipedal walking robots. To reach the research goal, the following objectives will be pursued: (1) to formulate a new method of reference trajectory modification for solving the intermittent tracking-error divergence issue underlying the trajectory tracking problem of hybrid systems with state-triggered jumps; (2) to extend Lyapunov theory to general hybrid systems with state-trigged jumps for creating a large class of controllers that can provably solve the tracking problem; and (3) to synthesize a control framework that exploits the researched controller design to achieve high versatility simultaneously with provable stability, agility, and energy efficiency for bipedal robotic walking. This project will lay a foundation for the creation of next-generation legged robot systems capable of safe and reliable real-world operations.
Sponsors: National Science Foundation, Verizon 5G Lab, Ericsson.
Experiment videos:
Modeling, State Estimation, and Control for Performance Augmenting Exoskeletons
Exoskeletons can provide people with movement assistance when they become fatigued during long periods of exertion. While the focus of this project is on the use of adaptive exoskeletons by people who are able-bodied, the results could be applied to help people who have diseases such as multiple sclerosis, where people need increased assistance throughout the day as they become more tired. The interdisciplinary team will develop new human-robot interaction methods through adaptive exoskeleton control by using novel fabric-embedded sensors to measure how a person is moving and to develop a model to understand how these movements indicate when a person is becoming tired. Commercially-available exoskeletons do not explicitly address fatigue issues for enhancing endurance. Additionally, commercially-available sensors for sensing the wearer's body movement and muscle activation are rigid and can be uncomfortable when worn between the body and an exoskeleton system, which often also has rigid parts. The comfortable and breathable fabric-embedded sensors combined with an adaptive exoskeleton controller that can measure a person's fatigue in real time will allow endurance enhancement for human and exoskeleton performance. The project includes a soft-robotics design curriculum for broadening participation in computing.
Understanding and quantifying fatigue in human body is a complex research problem. This project will utilize a soft, breathable sensor garment between the wearer's body and the exoskeleton to sense fatigue and come up with a fatigue index. Such a fabric embedded sensing will allow for the development of exoskeletons that are more power efficient, provide assistance only when needed (at the power level that is needed based upon the wearer's fatigue level), and reduce metabolic costs for the wearer while preventing potential muscle atrophy that could arise from always-on exoskeleton assistance. An interdisciplinary approach, combining controls and robotics, human performance measurement, materials and soft robotics, and human-robot interaction, will be used to accomplish this goal. This research will establish a fatigue index that can 1) reliably and quantitatively indicate the level of physical fatigue, and 2) be easily obtained based on kinematic and kinetic data. The strain-field and fabric-embedded sensors will provide the kinematic data, which will then be used to estimate the kinetic data. Exploiting these data, the research will draw upon feedback control theory and human biomechanical modeling to create a systematic method for monitoring fatigue with provable estimation accuracy. An adaptive exoskeleton control framework will be systematically derived to explicitly address fatigue issues through three synergistically connected layers: activator, optimizer, and real-time controller. The resulting exoskeleton controller will enable adaptive assistance for endurance enhancement by delaying the onset of wearers' fatigue and allowing better usage of exoskeleton power.
Understanding and quantifying fatigue in human body is a complex research problem. This project will utilize a soft, breathable sensor garment between the wearer's body and the exoskeleton to sense fatigue and come up with a fatigue index. Such a fabric embedded sensing will allow for the development of exoskeletons that are more power efficient, provide assistance only when needed (at the power level that is needed based upon the wearer's fatigue level), and reduce metabolic costs for the wearer while preventing potential muscle atrophy that could arise from always-on exoskeleton assistance. An interdisciplinary approach, combining controls and robotics, human performance measurement, materials and soft robotics, and human-robot interaction, will be used to accomplish this goal. This research will establish a fatigue index that can 1) reliably and quantitatively indicate the level of physical fatigue, and 2) be easily obtained based on kinematic and kinetic data. The strain-field and fabric-embedded sensors will provide the kinematic data, which will then be used to estimate the kinetic data. Exploiting these data, the research will draw upon feedback control theory and human biomechanical modeling to create a systematic method for monitoring fatigue with provable estimation accuracy. An adaptive exoskeleton control framework will be systematically derived to explicitly address fatigue issues through three synergistically connected layers: activator, optimizer, and real-time controller. The resulting exoskeleton controller will enable adaptive assistance for endurance enhancement by delaying the onset of wearers' fatigue and allowing better usage of exoskeleton power.
Sponsors: U.S. Army, National Science Foundation.
Collaborators: Drs. Holly Yanco (UMass Lowell), Pei-Chun Kao (UMass Lowell), Rebecca Kramer-Bottiglio (Yale).