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'''Navigation function''' usually refers to a function of position, velocity, acceleration and time which is used to plan robot trajectories through the environment.  Generally, the goal of a navigation function is to create feasible, safe paths that avoid obstacles while allowing a robot to move from its starting configuration to its goal configuration.
 
== Potential functions as navigation functions ==
 
[[File:pf as navigation function.png|thumb|350px|A potential function. Imagine dropping a marble on the surface.  It will avoid the three obstacles and eventually reach the goal position in the center.]]
 
Potential functions assume that the environment or work space is known.  Obstacles are assigned a high potential value, and the goal position is assigned a low potential.  To reach the goal position, a robot only needs to follow the negative [[gradient]] of the surface.
 
We can formalize this concept mathematically as following: Let <math>X</math> be the state space of all possible configurations of a robot. Let <math>X_g \subset X</math> denote the goal region of the state space.
 
Then a potential function <math>\phi(x)</math> is called a (feasible) navigation function if <ref>Lavalle, Steven, [http://planning.cs.uiuc.edu/node369.html Planning Algorithms Chapter 8]</ref>
#<math>\phi(x)=0\ \forall x \in X_g</math>
#<math> \phi(x) = \infty</math> if and only if no point in <math> {X_{G}}</math> is reachable from <math> x</math>.
#For every reachable state, <math> x \in X \setminus {X_{G}}</math>, the local operator produces a state <math> x'</math> for which <math> \phi(x') < \phi(x)</math>.
 
== Navigation Function in Optimal Control ==
 
While for certain applications, it suffices to have a feasible navigation function, in many cases it is desirable to have an optimal navigation function with respect to a given cost [[functional]] <math>J</math>. Formalized as an [[optimal control]] problem, we can write
 
:<math>\text{minimize } J(x_{1:T},u_{1:T})=\int\limits_T L(x_t,u_t,t) dt</math>
:<math>\text{subject to } \dot{x_t} = f(x_t,u_t)</math>
whereby <math>x</math> is the state, <math>u</math> is the control to apply, <math>L</math> is a cost at a certain state <math>x</math> if we apply a control <math>u</math>, and <math>f</math> models the transition dynamics of the system.
 
Applying [[Bellman equation|Bellman's principle of optimality]] the optimal cost-to-go function is defined as
 
<math>\displaystyle \phi(x_t) = \min_{u_t \in U(x_t)} \Big\{ L(x_t,u_t) + \phi(f(x_t,u_t)) \Big\} </math>
 
Together with the above defined axioms we can define the optimal navigation function as
 
#<math>\phi(x)=0\ \forall x \in X_g</math>
#<math> \phi(x) = \infty</math> if and only if no point in <math> {X_{G}}</math> is reachable from <math> x</math>.  
#For every reachable state, <math> x \in X \setminus {X_{G}}</math>, the local operator produces a state <math> x'</math> for which <math> \phi(x') < \phi(x)</math>.
#<math>\displaystyle \phi(x_t) = \min_{u_t \in U(x_t)} \Big\{ L(x_t,u_t) + \phi(f(x_t,u_t)) \Big\} </math>
 
== Stochastic Navigation Function ==
 
If we assume the transition dynamics of the system or the cost function as subjected to noise, we obtain a [[optimal control|stochastic optimal control]] problem with a cost <math>J(x_t,u_t)</math> and dynamics <math>f</math>. In the field of [[reinforcement learning]] the cost is replaced by a reward function <math>R(x_t,u_t)</math> and the dynamics by the transition probabilities <math>P(x_{t+1}|x_t,u_t)</math>.
 
== See also ==
 
*[[Control Theory]]
*[[Optimal Control]]
*[[robot control|Robot Control]]
*[[motion planning|Motion Planning]]
*[[Reinforcement Learning]]
 
==References==
* {{citation
| last1 = LaValle | first1 = Steven M.
| title = Planning Algorithms
| edition = First
| publisher = Cambridge University Press
| year = 2006
| isbn = 978-0-521-86205-9
| url = http://planning.cs.uiuc.edu/
}}
* {{citation
| last1 = Laumond | first1 = Jean-Paul
| title = Robot Motion Planning and Control
| edition = First
| publisher = Springer
| year = 1998
| isbn = 3-540-76219-1
| url = http://homepages.laas.fr/jpl/book-toc.html
}}
 
==External links==
* [http://github.com/johnyf/nfsim NFsim]: MATLAB Toolbox for motion planning using Navigation Functions.
 
==References==
{{Reflist}}
 
[[Category:Robot control]]
 
 
{{robo-stub}}

Revision as of 22:12, 8 January 2014

Navigation function usually refers to a function of position, velocity, acceleration and time which is used to plan robot trajectories through the environment. Generally, the goal of a navigation function is to create feasible, safe paths that avoid obstacles while allowing a robot to move from its starting configuration to its goal configuration.

Potential functions as navigation functions

File:Pf as navigation function.png
A potential function. Imagine dropping a marble on the surface. It will avoid the three obstacles and eventually reach the goal position in the center.

Potential functions assume that the environment or work space is known. Obstacles are assigned a high potential value, and the goal position is assigned a low potential. To reach the goal position, a robot only needs to follow the negative gradient of the surface.

We can formalize this concept mathematically as following: Let X be the state space of all possible configurations of a robot. Let XgX denote the goal region of the state space.

Then a potential function ϕ(x) is called a (feasible) navigation function if [1]

  1. ϕ(x)=0xXg
  2. ϕ(x)= if and only if no point in XG is reachable from x.
  3. For every reachable state, xXXG, the local operator produces a state x for which ϕ(x)<ϕ(x).

Navigation Function in Optimal Control

While for certain applications, it suffices to have a feasible navigation function, in many cases it is desirable to have an optimal navigation function with respect to a given cost functional J. Formalized as an optimal control problem, we can write

minimize J(x1:T,u1:T)=TL(xt,ut,t)dt
subject to xt˙=f(xt,ut)

whereby x is the state, u is the control to apply, L is a cost at a certain state x if we apply a control u, and f models the transition dynamics of the system.

Applying Bellman's principle of optimality the optimal cost-to-go function is defined as

ϕ(xt)=minutU(xt){L(xt,ut)+ϕ(f(xt,ut))}

Together with the above defined axioms we can define the optimal navigation function as

  1. ϕ(x)=0xXg
  2. ϕ(x)= if and only if no point in XG is reachable from x.
  3. For every reachable state, xXXG, the local operator produces a state x for which ϕ(x)<ϕ(x).
  4. ϕ(xt)=minutU(xt){L(xt,ut)+ϕ(f(xt,ut))}

Stochastic Navigation Function

If we assume the transition dynamics of the system or the cost function as subjected to noise, we obtain a stochastic optimal control problem with a cost J(xt,ut) and dynamics f. In the field of reinforcement learning the cost is replaced by a reward function R(xt,ut) and the dynamics by the transition probabilities P(xt+1|xt,ut).

See also

References

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    Naturally, you will have to pay a safety deposit and that is usually one month rent for annually of the settlement. That is the place your good religion deposit will likely be taken into account and will kind part or all of your security deposit. Anticipate to have a proportionate amount deducted out of your deposit if something is discovered to be damaged if you move out. It's best to you'll want to test the inventory drawn up by the owner, which can detail all objects in the property and their condition. If you happen to fail to notice any harm not already mentioned within the inventory before transferring in, you danger having to pay for it yourself.

    In case you are in search of an actual estate or Singapore property agent on-line, you simply should belief your intuition. It's because you do not know which agent is nice and which agent will not be. Carry out research on several brokers by looking out the internet. As soon as if you end up positive that a selected agent is dependable and reliable, you can choose to utilize his partnerise in finding you a home in Singapore. Most of the time, a property agent is taken into account to be good if he or she locations the contact data on his website. This may mean that the agent does not mind you calling them and asking them any questions relating to new properties in singapore in Singapore. After chatting with them you too can see them in their office after taking an appointment.

    Have handed an trade examination i.e Widespread Examination for House Brokers (CEHA) or Actual Property Agency (REA) examination, or equal; Exclusive brokers are extra keen to share listing information thus making certain the widest doable coverage inside the real estate community via Multiple Listings and Networking. Accepting a severe provide is simpler since your agent is totally conscious of all advertising activity related with your property. This reduces your having to check with a number of agents for some other offers. Price control is easily achieved. Paint work in good restore-discuss with your Property Marketing consultant if main works are still to be done. Softening in residential property prices proceed, led by 2.8 per cent decline within the index for Remainder of Central Region

    Once you place down the one per cent choice price to carry down a non-public property, it's important to accept its situation as it is whenever you move in – faulty air-con, choked rest room and all. Get round this by asking your agent to incorporate a ultimate inspection clause within the possibility-to-buy letter. HDB flat patrons routinely take pleasure in this security net. "There's a ultimate inspection of the property two days before the completion of all HDB transactions. If the air-con is defective, you can request the seller to repair it," says Kelvin.

    15.6.1 As the agent is an intermediary, generally, as soon as the principal and third party are introduced right into a contractual relationship, the agent drops out of the image, subject to any problems with remuneration or indemnification that he could have against the principal, and extra exceptionally, against the third occasion. Generally, agents are entitled to be indemnified for all liabilities reasonably incurred within the execution of the brokers´ authority.

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External links

  • NFsim: MATLAB Toolbox for motion planning using Navigation Functions.

References

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