Trajectory Deformations from Physical Human-Robot Interaction

Robots are finding new applications where physical interaction with a human is necessary: manufacturing, healthcare, and social tasks. Accordingly, the field of physical human-robot interaction (pHRI) has leveraged impedance control approaches, which support compliant interactions between human and robot. However, a limitation of traditional impedance control is that---despite provisions for the human to modify the robot's current trajectory---the human cannot affect the robot's future desired trajectory through pHRI. In this paper, we present an algorithm for physically interactive trajectory deformations which, when combined with impedance control, allows the human to modulate both the actual and desired trajectories of the robot. Unlike related works, our method explicitly deforms the future desired trajectory based on forces applied during pHRI, but does not require constant human guidance. We present our approach and verify that this method is compatible with traditional impedance control. Next, we use constrained optimization to derive the deformation shape. Finally, we describe an algorithm for real time implementation, and perform simulations to test the arbitration parameters. Experimental results demonstrate reduction in the human's effort and improvement in the movement quality when compared to pHRI with impedance control alone

Model-Based Control Using Koopman Operators

This paper explores the application of Koopman operator theory to the control of robotic systems. The operator is introduced as a method to generate data-driven models that have utility for model-based control methods. We then motivate the use of the Koopman operator towards augmenting model-based control. Specifically, we illustrate how the operator can be used to obtain a linearizable data-driven model for an unknown dynamical process that is useful for model-based control synthesis. Simulated results show that with increasing complexity in the choice of the basis functions, a closed-loop controller is able to invert and stabilize a cart- and VTOL-pendulum systems. Furthermore, the specification of the basis function are shown to be of importance when generating a Koopman operator for specific robotic systems. Experimental results with the Sphero SPRK robot explore the utility of the Koopman operator in a reduced state representation setting where increased complexity in the basis function improve open- and closed-loop controller performance in various terrains, including sand.Comment: 8 page

A family of asymptotically stable control laws for flexible robots based on a passivity approach

A general family of asymptotically stabilizing control laws is introduced for a class of nonlinear Hamiltonian systems. The inherent passivity property of this class of systems and the Passivity Theorem are used to show the closed-loop input/output stability which is then related to the internal state space stability through the stabilizability and detectability condition. Applications of these results include fully actuated robots, flexible joint robots, and robots with link flexibility

Reactive task planning for multi-robot systems in partial known environment

openThe thesis investigates the planning and control problem for a group of mobile agents moving in a partially known workspace. A task will be assigned to each robot in the form of a linear temporal logic (LTL) formula. First an automaton-based method is introduced for the motion planning of a single agent, which guarantees the satisfaction of the assigned LTL task. Then a model-predictive controller considers state and input constraints leading the agent to a safe navigation. Based on a real scenario of a partial-known environment and agents can have only local sensing, two decentralized control strategies are proposed for online re-planning, which rely on a sampling-based algorithm. The first approach assumes local communication between agents, while the second one exploits a more general communication-free case. Finally, the human-in-the-loop scenario is considered, where a human may additionally take control of the agents, a mixed initiative controller is then implemented to prevent dangerous human behaviors while guarantee the satisfaction of the LTL specification. Using the developed ROS software package, several experiments were carried out to demonstrate the effectiveness and the potential applicability of the proposed strategies.The thesis investigates the planning and control problem for a group of mobile agents moving in a partially known workspace. A task will be assigned to each robot in the form of a linear temporal logic (LTL) formula. First an automaton-based method is introduced for the motion planning of a single agent, which guarantees the satisfaction of the assigned LTL task. Then a model-predictive controller considers state and input constraints leading the agent to a safe navigation. Based on a real scenario of a partial-known environment and agents can have only local sensing, two decentralized control strategies are proposed for online re-planning, which rely on a sampling-based algorithm. The first approach assumes local communication between agents, while the second one exploits a more general communication-free case. Finally, the human-in-the-loop scenario is considered, where a human may additionally take control of the agents, a mixed initiative controller is then implemented to prevent dangerous human behaviors while guarantee the satisfaction of the LTL specification. Using the developed ROS software package, several experiments were carried out to demonstrate the effectiveness and the potential applicability of the proposed strategies