In this lesson, we'll create the project and prepare the main elements (character, controller, game mode).
We'll be using Unreal Engine 5.7 throughout the course. Nothing else is needed for now, and any assets and materials will be provided in the relevant lessons.

Ready to get started? :-)

To make our life easier in the future when testing and debugging the project, it will be useful to have additional views.

Let's start by implementing a new Top-Down view that we can activate at any time, alternating it with the default view of the starting template.

This new view will use the already existing Camera in the Character.

Enjoy the lesson!

To make our life easier in the future when testing and debugging the project, it will be useful to have additional views.

In this lesson we will implement a new Top-Down view that we can activate at any time, alternating it with the default view of the starting template.

This new view will use a Pawn and a Camera independent from the original one in the Character.

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In this lesson we will proceed with a quick Blockout of a game level, structured with the following criteria:

• allow us to test the AI's operating logic and game mechanics

• put the AI ​​into crisis and highlight some critical issues

• create a stimulating environment to test more complex game logic later

To create the Blockout of the level, it is not necessary to use specific tools, you can use whatever you want, tools and assets that you are already familiar with and feel comfortable with.
If you have no preferences, you can download the assets I'll be using during the lesson for free at this link:

https://www.fab.com/listings/a274824f-5b21-400a-893e-16e75c81fcaa

Although they are currently only updated up to Unreal 4.26, you can easily use them in projects based on more recent versions, as you'll see in the lesson.

Also attached to this lesson are the assets you need to import to create the Drone that we'll be using throughout the rest of the course. Again, you're free to use any assets you like in place of the ones provided.

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In this lesson, we’ll create and set up the Drone Blueprint and its AI Controller (AIC). I’ll also walk you through some logic schemes I’ve prepared while planning the project, and we'll analyze the flowcharts representing the logic we’re about to implement.

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In this lesson, we’ll refine and polish the level, set up the Navigation Mesh, and add 'senses' and 'perception' to the Drone. We’ll also begin outlining the different 'states' that will define its behavior.

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In this lesson, we’ll implement our first routine: patrolling! We’ll also run into our first few challenges and walk through some basic debugging to fix them.

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In this lesson, we’ll implement the Searching routine, add more drones to the level, and finalize the patrol paths using Patrol Points. We’ll also run some tests and debug the transition between patrolling and searching behaviors.

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In this lesson we will implement the Chasing and Attacking routines.

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In this lesson, we’ll implement the player detection logic and trigger the different behavior routines. We’ll also review our progress so far and provide an overview of potential improvements.

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In this lesson, we’ll create duplicates of some assets and the level so we can work on Module 03 without affecting our progress from Module 02.

The goal is to keep the results of each module fully functional and intact, exactly as we left them in the final lesson. To achieve this, we’ll work with a separate copy of the project for each new module. Additionally, we’ll set up a new Behavior Tree and Blackboard, while also cleaning up the AI Controller logic.

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In this lesson, we’ll implement our first behavior routine using the Behavior Tree: Patrolling.

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In this lesson, we’ll see how to define and use the main Sequences and our first Decorators. Plus, I’ll share some quick tips on how to navigate the Official Documentation and how to get the most out of the brand-new Virtual Assistant.

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In this lesson, we’ll revisit the AI Controller and its Perception settings. We’ll prepare the AI Controller to interface with the Behavior Tree and Blackboard, defining key exchange properties and variables such as the Target Actor and Last Known Location (LKL).

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In this lesson, we’ll focus on setting up the routine and managing a specific scenario: when the Drone acquires a target, entering the 'Target Acquired' state.

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In this lesson, we’ll focus on setting up the following logic: what happens when the Drone loses sight of its target? We’ll call this routine: Target Lost.

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In this lesson, we’ll look at how to automatically update the Last Known Location (LKL) using a Service while in the 'Target Acquired' state.

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In this lesson, we’ll implement a Searching routine for our Drone.

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In this lesson, we’ll take a moment for an overview of the dedicated debugging tool designed specifically for AI and Behavior Trees.

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In this lesson, we’ll lay the groundwork for experimenting with State Trees. We’ll duplicate and set up the necessary assets, create our very first State Tree, and do some 'cleanup' within the AI Controller.

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In this lesson, we’ll create some simple debugging tasks and implement our first parameters and conditions. At this stage, we’ll also need to review the Drone’s Perception settings within the AI Controller.

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In this lesson, we’ll see how to create custom Tagged Events to communicate with the State Tree. We’ll also tackle the creation of a custom Condition based on these Events.

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In questa lezione ci dedicheremo alla creazione di Transizioni basate su Eventi, alla implementazione della routine di Patrolling ed i suoi sotto-stati, inoltre vedremo come effettuare la lettura e la scrittura dei Parameters dello State Tree da un Task.

Buon proseguimento!

In this lesson, we’ll focus on the Patrolling routine and all of its associated sub-states.

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In this lesson, we’ll focus on the Searching routine.

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In this lesson, we’ll focus on the Drone's behavior when a target is spotted—the 'Target Acquired' state.

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In this lesson, we’ll handle the Drone's behavior when a target is lost (Target Lost). We have also finally reached the point where we can perform a comprehensive test and debug everything we’ve built so far.

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In this lesson, we’ll take an overview of the primary debugging tools available for State Trees.

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In this lesson, we’ll lay the groundwork for experimenting with EQS (Environment Query System). We’ll duplicate and set up the necessary assets to create a new, independent test level, just as we did for the previous modules.

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In this lesson, we’ll modify the State Tree to introduce a new repositioning logic (State) for the Drone within the 'Target Acquired' routine.

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In this lesson, we’ll set up our EQS and use a specific tool to test it: the EQS Test Pawn. We’ll use it to understand how the Environment Query System works and develop its features to suit our project's needs.

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In this lesson, we’ll focus on developing the remaining Tests to finalize our EQS and work on the Query Context.

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In this lesson, we’ll wrap up our work on the EQS. We’ll focus on refining the Tests, adjusting the settings, and finalizing the debugging process.

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In this lesson, we’ll duplicate the map, folders, and assets to prepare for Module 06. We’ll also import the necessary assets to build the Security Camera (SecCam) and create the Blueprints for its components.

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In this lesson, we’ll start working on the Camera and its Perception settings. We’ll also handle the setup for visual debugging aids.

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In this lesson, we’ll work on implementing the Camera’s movement, managing detection logic, and setting up communication with the Drone.

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In this lesson, we’ll work on implementing an Event using a Blueprint Interface to trigger the Drone's Alert State. We’ll also discover what a State Tree Event Payload is and how to use it.

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In this lesson, we’ll focus on explaining and fixing the Camera’s Perception orientation. This lesson will also mark the conclusion of Module 06!

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In this lesson, we’ll get ready for the final Module of the course! We’ll prepare the project for Smart Objects, enable the necessary plugins, and as usual, set up the specific folders and assets for this module.

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In this lesson, we’ll work on implementing a specific State Tree for the Drone to interface with Smart Objects. We’ll also create a new Task to handle the Smart Object Slot Claim.

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In this lesson, we’ll focus on implementing cover movement and lowering the Drone's altitude using the Character's 'Crouch' function.

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In this lesson, we’ll run a general functional test of the new State Tree and work on finalizing the Drone's cover movement using the Crouch mechanic.

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In this lesson, we’ll focus on debugging the State Tree Tasks and the Drone's parameters to finalize the cover procedure.

With this, we have reached the end of the project and the course—congratulations!

All the best for your future projects, and good luck! :-)