Radiodrome

In geometry, a radiodrome is the pursuit curve followed by a point that is pursuing another linearly-moving point. The term is derived from the Latin word radius (Eng. ray; spoke) and the Greek word dromos (Eng. running; racetrack), for there is a radial component in its kinematic analysis. The classic (and best-known) form of a radiodrome is known as the "dog curve"; this is the path a dog follows when it swims across a stream with a current after something it has spotted on the other side. Because the dog drifts with the current, it will have to change its heading; it will also have to swim further than if it had taken the optimal heading. This case was described by Pierre Bouguer in 1732.

A radiodrome may alternatively be described as the path a dog follows when chasing a hare, assuming that the hare runs in a straight line at a constant velocity.

Graph of a radiodrome, also known as a dog curve
The path of a dog chasing a hare running along a vertical straight line at a constant speed. The dog runs towards the momentary position of the hare, and will be changing his heading continuously.

Mathematical analysis

Introduce a coordinate system with origin at the position of the dog at time zero and with y-axis in the direction the hare is running with the constant speed Vt. The position of the hare at time zero is (Ax, Ay) with Ax > 0 and at time t it is

( T x   ,   T y )   =   ( A x   ,   A y + V t t )   . {\displaystyle (T_{x}\ ,\ T_{y})\ =\ (A_{x}\ ,\ A_{y}+V_{t}t)~.} (1)

The dog runs with the constant speed Vd towards the instantaneous position of the hare.

The differential equation corresponding to the movement of the dog, (x(t), y(t)), is consequently

x ˙ = V d   T x x ( T x x ) 2 + ( T y y ) 2 {\displaystyle {\dot {x}}=V_{d}\ {\frac {T_{x}-x}{\sqrt {(T_{x}-x)^{2}+(T_{y}-y)^{2}}}}} (2)
y ˙ = V d   T y y ( T x x ) 2 + ( T y y ) 2   . {\displaystyle {\dot {y}}=V_{d}\ {\frac {T_{y}-y}{\sqrt {(T_{x}-x)^{2}+(T_{y}-y)^{2}}}}~.} (3)


It is possible to obtain a closed-form analytic expression y=f(x) for the motion of the dog. From (2) and (3), it follows that

y ( x ) = T y y T x x {\displaystyle y'(x)={\frac {T_{y}-y}{T_{x}-x}}} . (4)

Multiplying both sides with T x x {\displaystyle T_{x}-x} and taking the derivative with respect to x, using that

d T y d x   =   d T y d t   d t d x   =   V t V d   y 2 + 1   , {\displaystyle {\frac {dT_{y}}{dx}}\ =\ {\frac {dT_{y}}{dt}}\ {\frac {dt}{dx}}\ =\ {\frac {V_{t}}{V_{d}}}\ {\sqrt {{y'}^{2}+1}}~,} (5)

one gets

y = V t   1 + y 2 V d ( A x x ) {\displaystyle y''={\frac {V_{t}\ {\sqrt {1+{y'}^{2}}}}{V_{d}(A_{x}-x)}}} (6)

or

y 1 + y 2 = V t V d ( A x x )   . {\displaystyle {\frac {y''}{\sqrt {1+{y'}^{2}}}}={\frac {V_{t}}{V_{d}(A_{x}-x)}}~.} (7)

From this relation, it follows that

sinh 1 ( y ) = B V t V d   ln ( A x x )   , {\displaystyle \sinh ^{-1}(y')=B-{\frac {V_{t}}{V_{d}}}\ \ln(A_{x}-x)~,} (8)

where B is the constant of integration determined by the initial value of y' at time zero, y' (0)= sinh(B − (Vt /Vd) lnAx), i.e.,

B = V t V d   ln ( A x ) + ln ( y ( 0 ) + y ( 0 ) 2 + 1 ) . {\displaystyle B={\frac {V_{t}}{V_{d}}}\ \ln(A_{x})+\ln \left(y'(0)+{\sqrt {{y'(0)}^{2}+1}}\right).} (9)


From (8) and (9), it follows after some computation that

y = 1 2 [ ( y ( 0 ) + y ( 0 ) 2 + 1 ) ( 1 x A x ) V t V d + ( y ( 0 ) y ( 0 ) 2 + 1 ) ( 1 x A x ) V t V d ] {\displaystyle y'={\frac {1}{2}}\left[\left(y'(0)+{\sqrt {{y'(0)}^{2}+1}}\right)\left(1-{\frac {x}{A_{x}}}\right)^{-{\frac {V_{t}}{V_{d}}}}+\left(y'(0)-{\sqrt {{y'(0)}^{2}+1}}\right)\left(1-{\frac {x}{A_{x}}}\right)^{\frac {V_{t}}{V_{d}}}\right]} . (10)

Furthermore, since y(0)=0, it follows from (1) and (4) that

y ( 0 ) = A y A x {\displaystyle y'(0)={\frac {A_{y}}{A_{x}}}} . (11)

If, now, Vt ≠ Vd, relation (10) integrates to

y = C A x 2 [ ( y ( 0 ) + y ( 0 ) 2 + 1 ) ( 1 x A x ) 1 V t V d 1 V t V d + ( y ( 0 ) y ( 0 ) 2 + 1 ) ( 1 x A x ) 1 + V t V d 1 + V t V d ] , {\displaystyle y=C-{\frac {A_{x}}{2}}\left[{\frac {\left(y'(0)+{\sqrt {{y'(0)}^{2}+1}}\right)\left(1-{\frac {x}{A_{x}}}\right)^{1-{\frac {V_{t}}{V_{d}}}}}{1-{\frac {V_{t}}{V_{d}}}}}+{\frac {\left(y'(0)-{\sqrt {{y'(0)}^{2}+1}}\right)\left(1-{\frac {x}{A_{x}}}\right)^{1+{\frac {V_{t}}{V_{d}}}}}{1+{\frac {V_{t}}{V_{d}}}}}\right],} (12)

where C is the constant of integration. Since again y(0)=0, it's

C = A x 2 [ y ( 0 ) + y ( 0 ) 2 + 1 1 V t V d + y ( 0 ) y ( 0 ) 2 + 1 1 + V t V d ] {\displaystyle C={\frac {A_{x}}{2}}\left[{\frac {y'(0)+{\sqrt {{y'(0)}^{2}+1}}}{1-{\frac {V_{t}}{V_{d}}}}}+{\frac {y'(0)-{\sqrt {{y'(0)}^{2}+1}}}{1+{\frac {V_{t}}{V_{d}}}}}\right]} . (13)


The equations (11), (12) and (13), then, together imply

y = 1 2 { A y + A x 2 + A y 2 1 V t V d [ 1 ( 1 x A x ) 1 V t V d ] + A y A x 2 + A y 2 1 + V t V d [ 1 ( 1 x A x ) 1 + V t V d ] } {\displaystyle y={\frac {1}{2}}\left\{{\frac {A_{y}+{\sqrt {A_{x}^{2}+A_{y}^{2}}}}{1-{\frac {V_{t}}{V_{d}}}}}\left[1-\left(1-{\frac {x}{A_{x}}}\right)^{1-{\frac {V_{t}}{V_{d}}}}\right]+{\frac {A_{y}-{\sqrt {A_{x}^{2}+A_{y}^{2}}}}{1+{\frac {V_{t}}{V_{d}}}}}\left[1-\left(1-{\frac {x}{A_{x}}}\right)^{1+{\frac {V_{t}}{V_{d}}}}\right]\right\}} . (14)

If Vt = Vd, relation (10) gives, instead,

y = C A x 2 [ ( y ( 0 ) + y ( 0 ) 2 + 1 ) ln ( 1 x A x ) + 1 2 ( y ( 0 ) y ( 0 ) 2 + 1 ) ( 1 x A x ) 2 ] {\displaystyle y=C-{\frac {A_{x}}{2}}\left[\left(y'(0)+{\sqrt {{y'(0)}^{2}+1}}\right)\ln \left(1-{\frac {x}{A_{x}}}\right)+{\frac {1}{2}}\left(y'(0)-{\sqrt {{y'(0)}^{2}+1}}\right)\left(1-{\frac {x}{A_{x}}}\right)^{2}\right]} . (15)

Using y(0)=0 once again, it follows that

C = A x 4 ( y ( 0 ) y ( 0 ) 2 + 1 ) . {\displaystyle C={\frac {A_{x}}{4}}\left(y'(0)-{\sqrt {{y'(0)}^{2}+1}}\right).} (16)

The equations (11), (15) and (16), then, together imply that

y = 1 4 ( A y A x 2 + A y 2 ) [ 1 ( 1 x A x ) 2 ] 1 2 ( A y + A x 2 + A y 2 ) ln ( 1 x A x ) {\displaystyle y={\frac {1}{4}}\left(A_{y}-{\sqrt {A_{x}^{2}+A_{y}^{2}}}\right)\left[1-\left(1-{\frac {x}{A_{x}}}\right)^{2}\right]-{\frac {1}{2}}\left(A_{y}+{\sqrt {A_{x}^{2}+A_{y}^{2}}}\right)\ln \left(1-{\frac {x}{A_{x}}}\right)} . (17)


If Vt < Vd, it follows from (14) that

lim x A x y ( x ) = 1 2 ( A y + A x 2 + A y 2 1 V t V d + A y A x 2 + A y 2 1 + V t V d ) . {\displaystyle \lim _{x\to A_{x}}y(x)={\frac {1}{2}}\left({\frac {A_{y}+{\sqrt {A_{x}^{2}+A_{y}^{2}}}}{1-{\frac {V_{t}}{V_{d}}}}}+{\frac {A_{y}-{\sqrt {A_{x}^{2}+A_{y}^{2}}}}{1+{\frac {V_{t}}{V_{d}}}}}\right).} (18)

If Vt ≥ Vd, one has from (14) and (17) that lim x A x y ( x ) = {\displaystyle \lim _{x\to A_{x}}y(x)=\infty } , which means that the hare will never be caught, whenever the chase starts.

See also

  • Mice problem

References

  • Nahin, Paul J. (2012), Chases and Escapes: The Mathematics of Pursuit and Evasion, Princeton: Princeton University Press, ISBN 978-0-691-12514-5.
  • Gomes Teixera, Francisco (1909), Imprensa da universidade (ed.), Traité des Courbes Spéciales Remarquables, vol. 2, Coimbra, p. 255{{citation}}: CS1 maint: location missing publisher (link)