Materials and methods for tagging purse seine and baitboat caught tunas

ENGLISH: The percentages of return of purse seine-tagged yellowfin and baitboat- tagged yellowfin and skipjack subjected to various combinations of treatments were tested statistically to determine the effects of the treatments. For yellowfin the precentages of return differed among sets or dates of release, and were higher for double-tagged fish, larger fish, and fish released soonest during each set. For skipjack the percentages of return differed among dates of release, and were higher for single-tagged fish and fish tagged in plastic-covered cradles. SPANISH: Los porcentajes de retorno de aleta amarilla marcado en barcos cerqueros y los de aleta amarilla y barrilete marcados en los de carnada sometidos a varias combinaciones de tratamientos fueron probados estadísticamente para determinar los efectos de los tratamientos. Con respecto al aleta amarilla los porcentajes de retorno fueron diferentes entre los lances o fechas de liberación, y fueron más altos para los peces con doble marca, peces de más talla y peces liberados lo más pronto durante cada lance. Los porcentajes de retorno del barrilete fueron diferentes entre las fechas de liberación, y fueron más altos para peces con una sola marca y peces marcados en cunas cubiertas con plástico. (PDF contains 41 pages.)

Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation

This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called the freezing robot problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m², while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m². We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.