A new Three.js experiment explores two strategies for locating missing persons: a sweep searchlight and a stationary beacon. This conceptual model isolates variables for controlled comparison, but is not intended as valid search-and-rescue guidance.
The experiment is presented as a one-page application using Three.js to visualize two strategies for locating missing persons in different scenarios. The two methods under examination are the sweep searchlight, where the rescuer moves to illuminate the area, and the raise beacon, where the rescuer remains stationary.
Users can interact with the experiment by running a local server using Python to access the visual model via a web browser.
The page utilizes a CDN for Three.js, requiring internet access for the first load.
The experiment includes presets that showcase typical scenarios, such as a mobile hiker favoring the beacon and an injured individual complicating the beacon's effectiveness.
The experiment runs Monte Carlo comparisons to evaluate the effectiveness of both search strategies under various random conditions.
Users can observe outcomes based on factors like line-of-sight occlusion in dense forests, resulting in varying effectiveness between the strategies.
It's important to note that this experiment is a conceptual model, not intended for real-world search-and-rescue applications.
Significant factors like weather, terrain elevation, equipment limitations, and human behavior are not included in this simplified approach.
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A new Three.js experiment explores two strategies for locating missing persons: a sweep searchlight and a stationary beacon. This conceptual model isolates variables for controlled comparison, but is not intended as valid search-and-rescue guidance.