Flocking with Obstacle Avoidance

In this paper, we provide a dynamic graph theoretical framework for flocking in presence of multiple obstacles. In particular, we give formal definitions of nets and flocks as spatially induced graphs. We provide models of nets and flocks and discuss the realization/embedding issues related to structural nets and flocks. This allows task representation and execution for a network of agents called alpha-agents. We also consider flocking in the presence of multiple obstacles. This task is achieved by introducing two other types of agents called beta-agents and gamma-agents. This framework enables us to address split/rejoin and squeezing maneuvers for nets/flocks of dynamic agents that communicate with each other. The problems arising from switching topology of these networks of mobile agents make the analysis and design of the decision-making protocols for such networks rather challenging. We provide simulation results that demonstrate the effectiveness of our theoretical and computational tools

Proprioceptive Robot Collision Detection through Gaussian Process Regression

This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.Comment: Published at ACC 201

Modeling, hardware-in-the-loop simulations and control design for a vertical axis wind turbine with high solidity

Vertical axis wind turbines (VAWTs) are advantageous in gusty, turbulent winds with rapidly changing direction such as surface winds by the virtue of their omnidirectional and simple design. Thus, a small-scale VAWT is favorable in urban areas, e.g., on top of a building, as well as in rural areas away from integrated grid systems where it can be used as a portable generator. In this thesis, a methodology is presented for the assessment of overall performance for a small-scale VAWT system that consists of a three-straight-bladed rotor with high solidity, electromechanical and power electronics components and controller. Salient features of this approach include a validated computational fluid dynamics (CFD) model and a hardware-inthe- loop (HIL) simulation. The time-dependent, two-dimensional CFD model is coupled with the dynamics of the rotor subject to inertia and generator load. The HIL test-bed consists of an electrical motor, a gearbox, a generator, a rectifier and a programmable electronic load. In this setup, the electrical motor emulates the VAWT rotor. The HIL simulation is used to study the impact of electromechanical energy conversion on the overall performance and to evaluate control algorithms in real-time. For variable-speed control of the turbine, maximum power point tracking (MPPT) and model predictive control (MPC) algorithms and a simple MPC-mimicking control are designed and tested. According to results, the coupled CFD model is an effective tool in evaluation of the realistic transient behavior of the VAWT including the inertial effects of the rotor and the feedback control; the electromechanical energy conversion has a profound effect on the power characteristics and the efficiency of the VAWT system; the MPC and MPC-mimicking control algorithms outperform the MPPT algorithms in terms of energy output by allowing deviations from the maximum power instantaneously for future gains in energy generation; and all of the controllers perform satisfactorily under step wind, wind gust and real wind conditions