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
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 {\displaystyle X} be the state space of all possible configurations of a robot. Let X g ⊂ X {\displaystyle X_{g}\subset X} denote the goal region of the state space. Then a potential function ϕ ( x ) {\displaystyle \phi (x)} is called a (feasible) navigation function if
ϕ ( x ) = 0 ∀ x ∈ X g {\displaystyle \phi (x)=0\ \forall x\in X_{g}}
ϕ ( x ) = ∞ {\displaystyle \phi (x)=\infty } if and only if no point in X g {\displaystyle {X_{g}}} is reachable from x {\displaystyle x} . For every reachable state, x ∈ X ∖ X g {\displaystyle x\in X\setminus {X_{g}}} , the local operator produces a state x ′ {\displaystyle x'} for which ϕ ( x ′ ) < ϕ ( x ) {\displaystyle \phi (x')<\phi (x)} .
Probabilistic navigation function Probabilistic navigation function is an extension of the classical navigation function for static stochastic scenarios. The function is defined by permitted collision probability, which limits the risk during motion. The Minkowski sum used for in the classical definition is replaced with a convolution of the geometries and the Probability Density Functions of locations. Denoting the target position by x d {\displaystyle x_{d}} , the Probabilistic navigation function is defined as:
φ ( x ) = γ d ( x ) [ γ d K ( x ) + β ( x ) ] 1 K {\displaystyle {\varphi }(x)={\frac {\gamma _{d}(x)}{{\left[{\gamma _{d}^{K}(x)+\beta \left(x\right)}\right]}^{\frac {1}{K}}}}}
where K {\displaystyle K} is a predefined constant like in the classical navigation function, which ensures the Morse nature of the function. γ d ( x ) {\displaystyle \gamma _{d}(x)} is the distance to the target position | | x − x d | | 2 {\displaystyle {||x-{x_{d}}|{|^{2}}}} , and β ( x ) {\displaystyle \beta \left(x\right)} takes into account all obstacles, defined as
β ( x ) = ∏ i = 0 N o β i ( x ) {\displaystyle \beta \left(x\right)=\prod \limits _{i=0}^{N_{o}}{{\beta _{i}}\left(x\right)}}
where β i ( x ) {\displaystyle \beta _{i}(x)} is based on the probability for a collision at location x {\displaystyle x} . The probability for a collision is limited by a predetermined value Δ {\displaystyle \Delta } , meaning:
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