TORCS robot driving
The driver has to decide in realtime (50 times a second), how to steer (steering angle, acceleration, brake pressure, gear selection, clutch) the car. Instead of the TORCS information it will use the model provided from the track generator. It got additional information based on the rules, whether to try to overtake or to keep slipstreaming etc. Also it uses the on board sensors (engines rpm etc.).
Driving Hard Problems
- It seems like we encode rules like: standard practice is to trail a car by 2 seconds
- And then we mention things like avoid other cars, don't make 3-G turns. The question is, how are those rules encoded?
- What about the situation where we want to make notice of things for later, like the location of potholes. Assuming the vision has decided that is a pothole, how do we add that to map, remove it when we go by because it has been fixed? In a racing game, we should make note about a jump so that we don't go off a cliff. Supposed we would just drive the car through the map to have it gather data. What is the maximum speed it could go through the map the first time?
View of the AI modules (rules, learning)
The AI modules get all information about our position, rotation and movement relative to the world (track) and opponents, obstacles. Also it will get informations from traffic signs, crossings, or parking lots). Here we will decide, what we want the robot to do (Racing, pitting, parking, ..., slipstreaming, overtaking, avoiding, ...). Later we will have the possibility to switch between possible tracks to come from the current position to the target.
Motion Planning's view
At this point we know all (what we want to use). We know where we are and what our current movement is (relative to the world and other relevant things). Also we know what we want the robot do to, where to go to, etc. From now on, we will not longer add information but reduce it to what is needed for the following steps. First we will have to give the known information about the track and obstacles (opponents), our position, rotation and movement to the track generator. Later we can switch here between road based and offroad driving as well. This is, why I split Motion Planning and Track Generator. For offroad driving, we would have to use another module.
View of the Track Generator
Here we try to estimate the parameters of the track, used later for the driver. Assuming we are on the road, we had to get back the information we got from TORCS but don't use. In our estimation (model of the near world), we will calculate a racingline to be prepared to all, what might come up. If not racing, it would be using the right lane to be prepared to turn at the next crossing, to be able to park in the next parking lot etc. The model is kept until we got newer informations from the former steps.
How to drive a torcs car with a robot
After initializing all data, TORCS calls the drive function of the robot for each driving timestep, setting the following values:
- Brake pressure
- Steering angle
Acceleration, Brake pressure and Clutch are normalized to be values from 0 to 1.
The acceleration and max breaking is calculated from the engine's data, read from the cartype XML. The steering angle is scaled to be in -1 +1 range. The scaling is done with a cartype specific constant, the steerlock value.
The steerlock value is used to limit the cars ability to direct the steereing wheels to an angle, depending on the car type's specific layout. Having a front wheel driven car type with the engine placed cross in the cars front, you are limited by its dimensions, compared to an rear whell driven type with it's engine placed along in the front or at the rere.
Racings cars use smaller steerlock values than normal cars.
The Gear numbers include the reverse gear(0), the neutral gear (1) and the total number of gears is also cartype specific.
What to learn from it?
- To be sure, that we take the same values like TORCS, we have to read it from the normal TORCS-setup files (XML-Format). The merging of cartype + driver +... setup files is controlled by min and max values, giving the allowed range. You can not define a driver specific value out of range.
Because all these parameters are known, we can implement the reading in the wrapper using the handles to the ready merged setup files provided to us by TROCS. We can set this values while our DriverBase.Init call in our C#-Objects using our TStaticData structure.
It is practice to have additional parameters in the drivers setup file, only used from the driver. Here we are free to define all we need and to read it with the C#-code!
- To get a replay, we only have to save Acceleration, Brake pressure, Clutch, Gear and Steering angle off all cars in the race.
- These values will come from our code, so we are free, where and when to save it (Our Dispatcher handles all cars). TORCS can reproduce a race exactly, as long as no random input came from the cars (like uninitialized variables used in some robots!)
Basic driving function of Sharpy
The methods used for the first implementation of the SharpyCS driver are very basic, but they are a good base for driving on unknown tracks!
TORCS provides the information about the hole track to drive on while the InitTrack-API-Call. It is separated to segments of unique type (Straight, Turn to left, Turn to right).
All the bots used for the endurance world championship take and evaluate it to get a precalculated racingline. The most robots look at the track beeing of constant width, as the main track TORCS provides is indeed. But there are additional sides, having different chracteristics than the main track. This sides can have different start width and end width, individual friction and so on (based on the segments). The best robots use this additional width under some conditions.
All the functions used there are not of interest, if racing on a (partly) unknow track.
The method used for a basic steering of Sharpy, works with the current segment and the „visible“ part of the following segments. As intentionally used for racing, it drives in the middle of the road (so the lateral offset is zero).
The basic steering angle is calculated from the current postion to a target point, some way in front of the car. It is corrected by the yaw angle of the car. If the car is at a side of the track, it drives back to the middle. If it drives to a turn, the point used as target goes to the turn's inner side. This results in driving to the inner side of the turn. How much depends on the way used to look ahead. This „look ahead distance“ is corrected by the car's current speed. So in short, fast turns, it goes closer to the edges, in long, slower ones it stays more in the middle.
To make a robot drive not in the middle of the road, you have to add a lateral offset to the target point. The same is used, while overtaking and collision avoiding. If an opponent is near in front, the robot has to make a decision: Stay behind or overtake. If possible it will try to overtake by increasing the lateral offset to the better side. Better means here, to be at the inner side of the current or next comming turn.
Beeing on a long straight, the bot has to look a long way in front, but in this case, the next turn will be visible, indeed. In a turn, it uses the current situation (the current segment) to make the decision. So we can assume, that this approach will work, even if the track is unknown and we use only visible parts.
Assuming that we are racing, we allways try to accelerate as much as possible. But how much is possible? Here we start at the current segment too: Driving in a turn, the current segment's radius gives us the limit of the speed we can use (together with segment's and tyre's friction and other parameters of the car). If we are faster, we have to brake! If not or if we are on a straight, we have to look ahead. Again assuming that we are in a race (having no cars comming from the other side), we can calculate the way we would need to stop (speed1 = current speed, speed2 = 0). This gives us the max. distance to look ahead. Now we look from segment to segment in front, till we find a segment, limiting the speed. If there is one, we compare the distance to it with the brakedistance needed to slow down to the limiting segment's possible speed. If the brakedistance becomes greater, we brake. Here we assume, that we can look far enough. If we are in a situation, not seeing far enough, we can modify that method by replacing the brakedistance by the visible distance (or the half of it, if not in a race). Again, it would work (as base priciple).
With this simple methods, we allways use the brakes maximum pressure if braking. The faster bots don't use this binary approach. The used brake pressure is adjusted by the traction circle, the situation (opponents close), the lateral offset (beeing on the outer side) and other parameters. This is done while calculating the brakedistance, so we can combine it later.
For collision avoiding, a filter is used, to correct the basic brakepressure, if an opponent comes to close. the reaction is made, using the nearest opponent's data.
Avoiding cars aside
Our basic Sharpy has the CalcOffset method, to get the lateral offset from the middle of the road. Here we first check, wether there are cars aside. If so, we have to check wether there is more than one car aside.
If it is one car, we move to the other side of the track. If there are two cars, we check wether we are in the middle. If so we steer to the middle between both opponents, if not we go to the outer side of the track.
If there are more than two cars aside, we look only to the two next of us!
Here we mixed two offsets: One is were we are, the other is were we want to steer to! ToDo: To get better results (lower lap times, less damages) we will calculate the change of offset and use methods to control the speed of change. To get a smoother drive, we will replace the "middle of the track" line by the "main line of the track" and use the both distances to left and right instead of the track's width.
What to learn here?
We can see, what our robot needs to get from the "motion planning". The "main line" of the "track" and the distances to the sides, but in 3D! This info we want as long as posible in front of our car.
For an opponent we have to know were it is and to where it moves how fast, yawing or not. At the moment we use a fixed car with and car length (same for opponents and own car), but later we will have to deal with different dimensions.
To be able to calculate the possible speed along our main line, we need assumptions concerning the friction (of track and tyres). Let's assume, our car has no wings!