This paper presents a new collision avoidance procedure for unmanned aerial vehicles in the presence of static and moving obstacles. The proposed procedure is based on a new form of local parametrized guidance vector fields, called collision avoidance vector fields, that produce smooth and intuitive maneuvers around obstacles. The maneuvers follow nominal collisionfree paths which we refer to as streamlines of the collision avoidance vector fields. In the case of multiple obstacles, the proposed procedure determines a mixed vector field that blends the collision avoidance vector field of each obstacle and assumes its form whenever a pre-defined distance threshold is reached. Then, in accordance to the computed guidance vector fields, different collision avoidance controllers that generate collision-free maneuvers are developed. Furthermore, it is shown that any tracking controller with convergence guarantees can be used with the avoidance controllers to track the streamlines of the collision avoidance vector fields. Finally, numerical simulations demonstrate the efficacy of the proposed approach and its ability to avoid collisions with static and moving pop-up threats in three different practical scenarios.
A recent functional model of the left ventricle characterizes the ventricle's contractile state with parameters, rather than variables. The ventricle is treated as a pressure generator that is time and volume dependent. The heart's complex dynamics develop from a single equation based on the formation and relaxation of crossbridge bonds within underlying heart muscle. This equation permits the calculation of ventricular elastance via E(v) = ∂p(v)/∂V(v). This heart model is defined independently from load properties, and ventricular elastance is dynamic and reflects changing numbers of crossbridge bonds. The model parameters were extracted from measured pressure and volume data from isolated canine hearts. The purpose of this paper is to present in some detail how to describe a particular canine left ventricle from measured data. The model is also extended to include heart rate variability, which arises naturally from the model structure. Computed results compare favorably with measurements both in this study and from the literature.
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