fix example and coverage env introduction

README.md

@@ -9,7 +9,7 @@
 
 Welcome to the official GitHub repository for the Drone Swarm Search Environment (DSSE). This project offers a comprehensive simulation platform designed for developing, testing, and refining search strategies using drone swarms. Researchers and developers will find a versatile toolset supporting a broad spectrum of simulations, which facilitates the exploration of complex drone behaviors and interactions in dynamic, real-world scenarios.
 
-In this repository, we have implemented two distinct types of environments. The first is a dynamic environment that simulates maritime search and rescue operations for shipwreck survivors. It models the movement of individuals in the sea using a dynamic probability matrix, with the objective for drones being to locate and identify these individuals. The second is a static environment utilizing the Lagrangian particle simulation from the open-source [Opendrift library](https://github.com/OpenDrift/opendrift), which incorporates real-world ocean data to create a probability matrix for drone navigation tasks. In this scenario, drones are tasked with efficiently identifying all relevant cells that meet a certain probability threshold.
+In this repository, we have implemented two distinct types of environments. The first is a dynamic environment that simulates maritime search and rescue operations for shipwreck survivors. It models the movement of individuals in the sea using a dynamic probability matrix, with the objective for drones being to locate and identify these individuals. The second is a environment utilizing the Lagrangian particle simulation from the open-source [Opendrift library](https://github.com/OpenDrift/opendrift), which incorporates real-world ocean and wind data to create a probability matrix for drone SAR tasks. In this scenario, drones are tasked with covering the full search area within the lowest time possible, while prioritizing higher probability areas.
 
 
 ## 📚 Documentation Links
@@ -75,6 +75,7 @@ def random_policy(obs, agents):
 opt = {
     "drones_positions": [(10, 5), (10, 10)],
     "person_pod_multipliers": [0.1, 0.4, 0.5, 1.2],
+    "vector": (0.3, 0.3),
 }
 observations, info = env.reset(options=opt)
 
@@ -84,6 +85,7 @@ while not done:
     actions = random_policy(observations, env.get_agents())
     observations, rewards, terminations, truncations, infos = env.step(actions)
     done = any(terminations.values()) or any(truncations.values())
+
 ```
 
 
@@ -110,11 +112,10 @@ pip install DSSE[coverage]
 from DSSE import CoverageDroneSwarmSearch
 
 env = CoverageDroneSwarmSearch(
-    grid_size=40,
     drone_amount=3,
-    dispersion_inc=0.1,
-    vector=(1, 1),
     render_mode="human",
+    disaster_position=(-24.04, -46.17),  # (lat, long)
+    pre_render_time=10, # hours to simulate
 )
 
 opt = {

docs/.vitepress/config.mts

@@ -68,7 +68,6 @@ export default defineConfig({
                     { text: 'Reward', link: '/Documentation/docsCoverage#reward' },
                     { text: 'Termination & Truncation', link: '/Documentation/docsCoverage#termination-truncation' },
                     { text: 'Info', link: '/Documentation/docsCoverage#info' },
-                    { text: 'How to Cite This Work', link: '/Documentation/docsCoverage#how-to-cite-this-work' },
                     { text: 'License', link: '/Documentation/docsCoverage#license' },
                   ]
                 },

docs/Documentation/docsCoverage.md

@@ -350,21 +350,5 @@ The `env.save_matrix()` method is not only convenient for saving the probability
 
 `env.close()` will simply close the render window. Not a necessary function but may be used.
 
-## How to Cite This Work
-
-If you use this package, please consider citing it with this piece of BibTeX:
-
-```bibtex
-@misc{castanares2023dsse,
-      title={DSSE: A Drone Swarm Search Environment}, 
-      author={Manuel Castanares, Luis F. S. Carrete, Enrico F. Damiani, Leonardo D. M. de Abreu, José Fernando B. Brancalion, and Fabrício J. Barth},
-      year={2024},
-      eprint={2307.06240},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      doi={10.48550/arXiv.2307.06240}
-}
-```
-
 ## License
 This documentation is licensed under the terms of the [MIT License](https://opensource.org/licenses/MIT). See the LICENSE file for more details.