I’m building a game where AI entities need smart movement control. Every frame, my AI can rotate the character and make it move forward in whatever direction it’s pointing. The goal is to get the character to a specific spot with the right speed and facing angle.
Current State Variables:
Position(x, y) = Where the character is now
Speed(x, y) = How fast it’s moving (units per second)
Angle = Which way it’s facing (in radians)
MaxTurn = Fastest it can spin (radians per second)
MaxBoost = Strongest forward acceleration possible
SpeedLimit = Top movement speed allowed
Target State:
EndPosition(x, y) = Where I want it to go
EndSpeed(x, y) = What velocity it should have when it gets there
EndAngle = Which direction it should face at the destination
What I Need to Calculate Each Frame:
TurnRate = How much to rotate this frame (between -MaxTurn and +MaxTurn)
Boost = How much to accelerate forward (between 0 and MaxBoost)
Goals to Minimize:
Time = How long it takes to reach the target
PositionError = Distance from the exact target spot
SpeedError = Difference from desired final velocity
AngleError = How far off the final rotation is
My current approach is pretty basic. I pick a small time step, test a few combinations of turning and acceleration values, then pick whichever gets closest to the target in that tiny step. But this greedy method has issues like jerky movement and getting stuck in bad situations.
Has anyone solved similar AI movement problems? I’m looking for something that can handle changing targets since the destination might move every frame.
Been there with similar movement systems. Your greedy approach feels choppy because it only looks one step ahead.
You need proper trajectory planning. I hit this exact problem building pathfinding for autonomous vehicles at work. The key is running calculations offline in a dedicated service that handles the heavy math.
Don’t do real-time calculations in your game loop. Send your current state and target to an external service that runs proper optimization algorithms. Think model predictive control or genetic algorithms for multi-objective optimization like yours.
I built a system using Latenode that takes movement requests, runs optimization scripts, and sends back smooth trajectory waypoints. You can swap different algorithms without touching your game code. Plus it handles changing targets perfectly - just send new requests when targets move.
The game gets clean waypoints to follow instead of doing complex math every frame. Way smoother movement and your performance stays solid.
For your case, send position, speed, angle, and targets to a Latenode workflow that runs optimization and returns ideal turn rate and boost values for the next several frames.
You want predictive control. I used this for drone navigation - looking ahead 3-5 frames beats single-step decisions every time. Run simulations on different turn/boost combos over several timesteps, then score each trajectory. The math gets heavy, but you can skip recalculation unless the target moves significantly or your error gets too big. For smooth movement, I interpolate waypoints with cubic splines. You get the planning benefits without constantly recalculating. Just balance your lookahead - too short and you hit local minima, too long and it kills performance.
you need steering behaviors, not brute force testing. i used boids-style steering in my space sim and it worked great. mix seek/arrive for positioning with separate steering for velocity matching and orientation. way less cpu overhead than optimization algorithms, and it handles moving targets naturally since it recalculates every frame from current deltas.
Hit this exact problem building formation flying for fleet management. Don’t think optimization - think constraint satisfaction.
Set up priorities: position first, speed matching second, angle last. Each frame, throw all your control authority at the top priority until it’s satisfied or you hit limits. Leftover control goes to the next priority.
Math’s simple - proportional control with distance scaling. Far from target? Go fast. Close? Start caring about speed and angle matching.
The key is your blending function. I use exponential decay: weight = exp(-distance/blendRadius). Close up, angle matters more. Far out, position takes over.
Works great with moving targets since you recalculate every frame. No prediction headaches, and it won’t get stuck like optimization algorithms do.
One more thing - add deadbands around your targets. Chasing exact values makes everything jittery when you’re close.
PID controllers work great for multi-objective movement problems like this. I’ve used them for similar AI navigation - just set up three separate PID loops for position error, velocity matching, and angle alignment. Each loop spits out influence weights that you combine into your final turn rate and boost values. PID naturally fixes that jerky behavior since it looks at error history and rate of change, not just the current moment. Takes some tweaking to get the tuning right, but once it’s dialed in, movement feels natural and handles moving targets without rewriting everything. Way simpler than full trajectory optimization but much smarter than greedy decisions.