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Just a couple of words to link the poster that describes RUMBy’s architecture: you can download here the PDF version.

Together with Francesco Biral, I’m writing a simplified CNC software and machine tool simulator. Since it is mainly written in Ruby, we named it RubyNumericalControl, or RNC.

RNC will be used both as a didactic tool and as a research tool. In particular, since it is open and easily “hackable”, we plan to use it as a test platform for enhanced tool-path planning algorithms.

Here you can look at a screen capture of the visualizer:

A collegue of the Mechatronic Group, Mario De Cecco, was previously involved in the design of the OSIRIS experiment onboard the ROSETTA mission. Here is a beautifil picture taken by night, which shows the lights over populated areas frome Europe to the far East.

After a U turn in control strategy, now RUMBy can now drive with much greater accuracy. This result has been achieved by controlling the steering command in closed loop feedback with the planned yaw rate compared with the actual yaw rate.

The result is quite interesting, and is well illustrated by the x-y plot below:

The vehicle starts heading to the north, and is requested to reach, in minimum time, a point 100 meters east with a final heading towards west. The resulting trajectory is the red S-shaped line. The other curves represent the planned (and re-planned) trajectories and the steering and throttle control (negative throttle means braking).

Interestingly enough, the throttle command shows a triangle profile, which is due to the fact that RUMBy is always on the verge of lateral slip, and the planner corrects the throttle command in order to reduce the lateral slip whilst keeping the right trajectory.

Why did Rumby miss his goal? It was a little too close for his reduced maneuvering abilities. Here is what happens if the goal is twice as far (200 meters instead of 100).

Here are the effects of some tuning. Now “Frankestein” looks to have better control of his moves. The picture (click to enlarge) shows that the real trajectory departs very little from plans, except when the vehicle is close to his goal. The actual speed is also shown and compared to planned speed. On the speed profile the sequence of new plans is easier to see (recall that every while the “brain” updates motion plans, which allows to recover mismatches between planned and real motion). Motion ends when either the vehicle enters the circle surrounding the goal position, or the speed becomes zero (the latter is what happens in this case).

My friend Alberto Broggi (University of Parma) has been invited in Korea for a keynote speech with title: “VisLab’s footprint in the history of intelligent vehicles”. My compliments, Alberto!

(click to enlarge)

Here is one of the fist simulations of the closed loop behaviour of RUMBy. The system is made of a “body” (now still simulated, but it will be the real car model) and of a “brain”. The “body” is the car model, which sensors and low level data fusion and control loops. The brain is a computer, connected wireless to the body, which makes high level decision and especially the path planning with optimal control.

The body and the brain are here simulated and we are closing the loop. It looks like Frankestein awakening. The picture shows how Frankestein executes a navigation task. It has to start from the flag position, with initial velocity pointing upwards, and it has to reach the stop sign with velocity pointing right. Every while, the brain makes a motion plan, which is shown with the gray lines. The brain asks the body to execute that plan but the execution is imperfect, because the body does not react as the brain thought (there are mismatches between the model used by the brain for planning and the real dynamics of the body) and because there are time lags (the brain takes a while tio compute the plan and feeds it to the body with some lag). We can see, for example that the first plan asks to steer right more than the body really does. The red line is the actual executed trajectory. As the real motion drifts from plans, the brain is not discouraged, and makes continuously new plans to try to recover the situation. But “Frankestein” is somewhat drunk, and, as it approaches the goal, the execution becomes more and more critical. Eventually, Frankestein misses his goal.

But do not worry… that was a couple of days ago. We have now tuned the adrenaline and Frankestein behave a lot better. Stay tuned.

Three weeks of frantic coding, and finally it really looks like we’re almost there: the RUMBy platform is running!

This post is actually only a rumble of drums: all the systems are up and running, and now it is the time for doing some tests, plotting some charts, and crushing some floats…

MUCH more to come…

Here is the motorcycle driving simulator of the University of Padova. University of Padova (the Motorcycle Dynamic Research Group) is a partner of SafeRider Project. Their simulator will be used to test Advanced Driving Assistance Systems.