State Estimation and Control of Legged Robot Locomotion
on Dynamic Rigid Surfaces (DRSs)
This project will focus on creating new methods to model, estimate, and control the movement of legged robots for enabling stable locomotion on dynamic rigid surfaces (DRS) (i.e., surfaces that move and do not deform). While today’s legged robot systems have demonstrated remarkable capabilities in traversing stationary surfaces (e.g., stairs, sand, and grass), legged locomotion on DRS (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 DRS 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 on a DRS; (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 DRS.
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 on a DRS; (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 DRS.
Sponsor: National Science Foundation.
Trajectory Tracking Control of Hybrid Systems with Impulse Effects
This project focuses on advancing the knowledge in the control of hybrid systems for achieving highly versatile bipedal robotic walking through reliable global-position tracking control. In this work, Lyapunov theory would be extended to general hybrid systems with state-triggered jumps for creating a large class of controllers that can provably solve the problem of trajectory tracking. The project would 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.
Sponsor: National Science Foundation.
Human Biomechanical Modeling
In this research, we focus on analytically and numerically revealing the fundamental principles of the physical interaction between a human and an assistive device. The research outcomes would be used to inform the design and control of adaptive robotic exoskeletons for enhancing the energy efficiency of human locomotion as well as enabling reliable human-intent inference.
Sponsor: U.S. Army.
5G-enabled Robot Teleoperation
The goal of this project is to combine 5G technology with legged locomotion for enabling reliable teleoperation during time-critical missions such as search and rescue as well as emergency responses. With teleoperation, we incorporate the human experience and expertise into robotic systems while keeping human operators in safe, remote environments. To teleoperate a robot during time-critical missions, the human operator needs high-resolution, real-time information about the field to make timely decisions. Moreover, significant computational power is demanded to enable online motion planning for guaranteeing stability and agility of robot operation. Through a 5G network, the human operator can access reliable, high-resolution, real-time video streaming to make feasible, timely decisions for the robot to act on. With edge computing, the robot is able to translate the high-level commands from human operators into joint-level action in real time.
Sponsors: Verizon 5G Lab and Ericsson.