# Plain-vanilla deflection

Let's assume the shooter can aim and shoot anywhere instantly, the target is moving at a constant velocity and the projectile will travel at a constant velocity too. We are given as inputs the target's current position, its velocity and the speed of our projectile. We'll use coordinates where the shooter is at the origin and has zero velocity.## First-order correction

As mentioned before, if we naively aim at the target's current position, by the time the projectile gets there, the target will have moved. We can compute how long it will take for the projectile to get to the target's current position, compute where the target will be then and aim there instead.```
Position compute_first_order_correction(Position target_position, Vector target_velocity, float projectile_speed) {
float t = distance(Origin, target_position) / projectile_speed;
return target_position + t * target_velocity;
}
```

This simple piece of code is probably good enough in many cases (if the target is moving slowly compared to the projectile speed, if the target is moving perpendicularly to the shooter-to-target vector, or if we want to sometimes miss because a more precise solution would be detrimental to the fun of the game).
## Iterative approximation

For a more precise solution, you could iterate this first-order correction until it converges.```
Position iterative_approximation(Position target_position, Vector target_velocity, float projectile_speed) {
float t = 0.0f;
for (int iteration = 0; iteration < MAX_ITERATIONS; ++iteration) {
float old_t = t;
t = distance(Origin, target_position + t * target_velocity) / projectile_speed;
if (t - old_t < EPSILON)
break;
}
return target_position + t * target_velocity;
}
```

## Computing the answer directly

In the iterative approximation, we would stop if we found a place where*old_t*and

*t*match. This gives us an equation to solve:

*t = distance(Origin, target_position + t * target_velocity) / projectile_speed*Let's do some computations to try to solve it.

*t = sqrt(dot_product(target_position + t * target_velocity, target_position + t * target_velocity)) / projectile_speed t^2 * projectile_speed^2 = dot_product(target_position + t * target_velocity, target_position + t * target_velocity) t^2 * projectile_speed^2 = dot_product(target_position, target_position) + 2 * t * dot_product(target_position, target_velocity) + t^2 * dot_product(target_velocity, target_velocity)*This is a second-degree equation in

*t*which we can easily solve, leading to the following code:

```
// a*x^2 + b*x + c = 0
float first_positive_solution_of_quadratic_equation(float a, float b, float c) {
float discriminant = b*b - 4.0f*a*c;
if (discriminant < 0.0f)
return -1.0f; // Indicate there is no solution
float s = std::sqrt(discriminant);
float x1 = (-b-s) / (2.0f*a);
if (x1 > 0.0f)
return x1;
float x2 = (-b+s) / (2.0f*a);
if (x2 > 0.0f)
return x2;
return -1.0f; // Indicate there is no positive solution
}
Position direct_solution(Position target_position, Vector target_velocity, float projectile_speed) {
float a = dot_product(target_velocity, target_velocity) - projectile_speed * projectile_speed;
float b = 2.0f * dot_product(target_position, target_velocity);
float c = dot_product(target_position, target_position);
float t = first_positive_solution_to_quadratic_equation(a, b, c);
if (t <= 0.0f)
return Origin; // Indicate we failed to find a solution
return target_position + t * target_velocity;
}
```

# The general case

There are many variations of the problem we could consider: Accelerating targets, accelerating projectiles, situations where it takes time to aim at a new direction... All of them can be solved following the same template. The things that could change can be encoded in two functions:*position_of_target_at(time)**time_to_hit(position)*

*t*at which the following equation holds:

*t = time_to_hit(position_of_target_at(t))*We then compute where the target will be at time

*t*and aim there. Just as before, we could do a first-order correction, use iterative approximation or solve the problem directly. It might be the case that an analytical solution can be found, like we did in the previous section, but things can get messy quickly and you may have to resort to a numerical solution.

# Conclusion

This article covered three methods to implement deflection in your games: First-order correction, iterative approximation and directly finding the solution. You'll need to use some judgement to decide which one to use. Hopefully this article gives you enough to make an informed decision.## Article Update Log

30 Oct 2015: Initial release 4 Nov 2015: Minor fixes
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