1. EQS basics
1.1 Generator node
Generator: Generate a series of test points in the range where it needs to be generated for weighted sum testing.
1.1.1 Actors of Class
It is used to find the Actor class within the range, regardless of the terrain, viewing angle, etc., as long as the Actor is in the range class, it will be found.
1.1.2 Points:Grid
Used to generate a grid, there are size and spacing parameters, and you need to specify which object to generate around.
1.2 Scenarios
EnvQueryContext_Item (item) is the small ball seen in the Test Pawn, which is the point generated by the generator.
EnvQueryContext_Querier (querier) is the Pawn occupied by the AI controller, which executes the behavior tree that starts the scene query.
In Actors of Class, if the search center uses EnvQueryContext_Item, players cannot be found, and if the search center uses EnvQueryContext_Quener, players can be found within a certain range.
Custom scenario : use EnvQueryContext_BlueprintBase (EQC) as the environment query scenario, and return the target through the overloaded function in the blueprint.
1.3 Testing
In EQS, tests are used to determine which of the items produced by the generator is the best choice .
1.3.1 Distance test
Note:
(1) Under relative standardization, the smallest is 0 and the largest is 1.
(2) The constants are all 1 under absolute normalization; the closest one is 0 under relative normalization.
(3) Inverting linearity is the reverse of linearity.
(4) The score factor can usually be regarded as a multiplication factor.
(5) Because the score is 0-1, the score will be more inclined to 0 after square, and the score will be more inclined to 1 after square root.
1.3.2 Dot test (dot product test, direction test)
Note:
(1) There are two modes, one is Rotation, that is, the direction of the vector directly, and the other is two points, the direction of the vector from the first point to the end point.
(2) Using the dot product of two vectors is to calculate the score of the angle between the two vector directions, as shown in the figure below, the score in the forward direction is 1, and the score in the backward direction is 0.
1.3.3 Trace test
The Boolean match is true, that is, match the value of true, set the value of true to Trace, and filter it out. As shown below.
1.3.4 Overlay test
1.3.5 Project projection test
The Project projection test is mainly used to correct the position of the test point, such as generated on uneven ground.
There is a Projection Data property in the Grid that can be used to correct the position of the generated point, which is consistent with the role of the Project projection test.
Adding a Project projection test can filter and score according to modifying the properties of Projection Data.
2. Simple application of EQS
2.1 NPC looking for players
Check the EQS option in the project settings.
(1) Create new Player Charater class EQS_NPC, AIController class EQS_AI, blackboard EQS_BB, behavior tree EQS_BT, scene query blueprint class EQ_FindPlayer.
Create a new EnvQueryContext_BlueprintBase blueprint class and name it EQC_PlayerContext.
(2) Overload the Provide Single Actor function in the EQC_PlayerContext blueprint to add the return value of the player object in EQS.
(3) In the EQ_FindPlayer blueprint class, lead out the Point:Grid node and change it to a radius of 1000 and an interval of 150.
Added only filter test Trace .
Add the score-only test Distance and set the integration factor to -1.
(4) Add key frames to the blackboard, IsFindObject of Boolean type, ObjectPosition of vector type, and ObjectActor of object type. Note: the Base Class of ObjectActor is Actor .
(5) Write BTTask for random movement
(6) Add the AIPerception component in the NPC_AI blueprint, and add AI Sight config.
Updated when target awareness was added for AIPerception.
Update the storage Actor when the target is aware and determine whether it is a Player.
When no target is found, set a timer of 5 seconds, and set no target found, lose target after 5 seconds. When the perception is found, it enters the Fight logic.
(7) Write the corresponding behavior tree logic.
The logic of the behavior tree execution process is as follows : First, AI patrols. If it sees the player, it will trigger a visual perception update , and the AI will always look at the player ;
Final effect : After the AI sees the player, it faces the player. If the player dodges, the AI will move to a new position (the result of the EQS query) and continue to look at the player.
2.2 Hiding players
Add a filtered-only test Trace.
Add score-only test Distance.
Then add the score-only test Distance.
Note: EnvQueryContext_Querier searches around the AI character, that is, it returns the Pawn controlled by the AI controller.
The role here is the point that is farthest from the character and closest to the AI itself.
