Dynamical Systems, Stochastic Processes, and Probabilistic Robotics

transcript

Dynamical Systems, Stochastic Processes,

and Probabilistic Robotics

David Rosen

Goals Overview of some of

the big ideas in autonomous systems

Theme: Dynamical and stochastic systems lie at the intersection of mathematics and engineering

ZOMG ROBOTS!!!

Actually, no universally accepted definition For this talk:

◦ Sensing (what’s going on?)◦ Decision (what to do?)◦ Planning (how to do it?)◦ Actuation & control (follow the plan)

What is a robot?

Dynamical SystemsHow do we begin to think about this problem?

General Mathematical Framework: Dynamical Systems

Sensing and Estimation

What’s going on?

Sensing and Estimation How do we know

what the state is at a given time?

Generally, we have some sensors:

◦ Laser rangefinders◦ GPS◦ Vision systems◦ etc…

Great!

Well, not quite…◦ In general, can’t measure all state variables

directly. Instead, an observation function H : M → O maps the current state x to some manifold O of outputs that can be directly measured

◦ Usually, dim O < dim M ◦ Given some observation z = H(x), can’t determine x

Sensing and Estimation Maybe we can use the

system dynamics (f ) together with multiple observations?

Observability: Is it possible to determine the state of the system given a finite-time sequence of observations?◦ “Virtual” sensors!

Detectability (weaker): Are all of the unobservable modes of the system stable?

Sensing and Estimation What about noise?

In general, uncorrected/unmodeled error accumulates over time.

Stochastic processes: nondeterministic dynamical systems that evolve according to probability distributions.

New model:

for randomly distributed variables wt and vt .

We assume that xt conditionally depends only upon xt-1 and the control ut (completeness):

Stochastic processes that satisfy this condition are called Markov chains.

Similarly, we assume that the measurement zt conditionally depends only upon the current state xt :

Thus, we get a sequence of states and observations like this:

This is called the hidden Markov model (HMM).

How can we estimate the state of a HMM at a given time?

Any ideas?

Hint: How might we obtainfrom ?

Bayes’ Rule

Punchline: If we regard probabilities in the Bayesian sense, then Bayes’ Rule provides a way to optimally update beliefs in response to new data. This is called Bayesian inference.

It also leads to recursive Bayesian estimation.

Define

Then by conditional independence in the Markov chain:

and by Bayes’ rule:

Recursive Bayesian Estimation: The Bayes Filter

This shows how to compute given only and the control input .

Recursive filter!

Initialize the filter with initial belief

Recursion step:

◦ Propagate:

◦ Update:

Recursive Bayesian Estimation: The Bayes Filter

Benefits of recursion:◦ Don’t need to

remember observations◦ Online implementation◦ Efficient!

Applications:◦ Guidance◦ Aerospace tracking◦ Autonomous mapping

(e.g., SLAM)◦ System identification◦ etc…

Example: Missile guidance This clip was

reportedly sampled from an Air Force training video on missile guidance, circa 1955.

It is factually correct.

See also:◦ Turboencabulator◦ Unobtainium

Rudolf Kalman

Trajectory GenerationHow do we identify trajectories of the system with desirable properties?

Recap: Control Systems

Controllability: given two arbitrary specified states p and q, does there exist a finite-time admissible control u that can drive the system from p to q ?

Reachability: Given an initial state p, what other states can be reached from p along system trajectories in a given length of time?

Stabilizability: Given an arbitrary state p, does there exist an admissible control u that can stabilize the system at p ?

Key questions for trajectory generation

Several methods for generating trajectories ◦ Rote playback◦ Online synthesis from libraries of moves◦ etc…

Optimal control: Minimize a cost functional

amongst all controls whose trajectories have prescribed initial and final states x0 and x1.

Trajectory Generation

Provides a set of necessary conditions satisfied by any optimal trajectory.

Can often be used to identify optimal controls of a system.

The Pontryagin Maximum Principle

Lev Pontryagin

Can also derive versions of the PMP for:

State-constrained control

Non-autonomous (i.e., time-dependent) dynamics.

etc…

Nothing Could Possibly Go Wrong…

Trajectory FollowingHow can we regulate autonomous systems?

The problem

Real-world systems suffer from noise, perturbations

If the underlying system is unstable, even small perturbations can drive the system off of the desired trajectory.

Example: Pendulum on a Cart

We have a desired trajectory that we would like to follow, called the reference.

At each time t, we can estimate the actual state of the system .

In general there is some nonzero error

at each time t.

What to do?

What to do? Maybe we can find some

rule for setting the control input u (t ) at each time t as a function of the error e (t ) such that the system is stabilized?

In that case, we have a feedback control law:

Many varieties of feedback controllers:

Proportional-integral-derivative (PID) control

Fuzzy logic control Machine learning Model adaptive control Robust control H∞ control etc…

Trajectory Following: Feedback Control

We started with what (at least conceptually) were very basic problems from engineeringe.g.,

make do this this

The Moral of the Story

and ended up investigating all of this:

Dynamical systems Stochastic processes Markov chains The hidden Markov model Bayesian inference Recursive Bayesian estimation The Pontryagin Maximum Principle Feedback stabilization

and this is just the introduction!

The Moral of the Story

Questions?

Dynamical Systems, Stochastic Processes, and Probabilistic Robotics

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