Description
Minimize the time taken for a robot arm to travel between two points. This model is from the COPS benchmarking suite. See http://www-unix.mcs.anl.gov/~more/cops/. The number of discretization points can be specified using the command line parameter --nh. COPS performance tests have been reported for nh = 50, 100, 200, 400
Large Model of Type : NLP
Category : GAMS Model library
Main file : robot.gms
$title Robot Arm COPS 2.0 #8 (ROBOT,SEQ=236)
$onText
Minimize the time taken for a robot arm to
travel between two points.
This model is from the COPS benchmarking suite.
See http://www-unix.mcs.anl.gov/~more/cops/.
The number of discretization points can be specified using the command
line parameter --nh. COPS performance tests have been reported for nh
= 50, 100, 200, 400
Dolan, E D, and More, J J, Benchmarking Optimization
Software with COPS. Tech. rep., Mathematics and Computer
Science Division, 2000.
Vanderbei, R, Nonlinear Optimization Models.
Keywords: nonlinear programming, engineering, robotics
$offText
$if set n $set nh %n%
$if not set nh $set nh 50
Set h 'intervals' / h0*h%nh% /;
Scalar
nh 'number of intervals' / %nh% /
L 'total length of arm' / 5 /
max_u_rho / 1 /
max_u_the / 1 /
max_u_phi / 1 /;
Variable
rho(h)
the(h)
phi(h)
rho_dot(h)
the_dot(h)
phi_dot(h)
u_rho(h)
u_the(h)
u_phi(h)
step
tf
i_the(h)
i_phi(h);
Equation
tf_eqn
rho_eqn(h)
the_eqn(h)
phi_eqn(h)
u_rho_eqn(h)
u_the_eqn(h)
u_phi_eqn(h)
i_the_eqn(h)
i_phi_eqn(h);
tf_eqn.. tf =e= step*nh;
i_phi_eqn(h).. i_phi(h) =e= (power(L - rho(h),3) + power(rho(h),3))/3.0;
i_the_eqn(h).. i_the(h) =e= i_phi(h)*sqr(sin(phi(h)));
rho_eqn(h-1).. rho(h) =e= rho(h-1) + 0.5*step*(rho_dot(h) + rho_dot(h-1));
the_eqn(h-1).. the(h) =e= the(h-1) + 0.5*step*(the_dot(h) + the_dot(h-1));
phi_eqn(h-1).. phi(h) =e= phi(h-1) + 0.5*step*(phi_dot(h) + phi_dot(h-1));
u_rho_eqn(h-1)..
rho_dot(h) =e= rho_dot(h-1) + 0.5*step*(u_rho(h) + u_rho(h-1))/L;
u_the_eqn(h-1)..
the_dot(h) =e= the_dot(h-1) + 0.5*step*(u_the(h)/i_the(h) + u_the(h-1)/i_the(h-1));
u_phi_eqn(h-1)..
phi_dot(h) =e= phi_dot(h-1) + 0.5*step*(u_phi(h)/i_phi(h) + u_phi(h-1)/i_phi(h-1));
rho.lo(h) = 0; rho.up(h) = L;
the.lo(h) = -pi; the.up(h) = pi;
phi.lo(h) = 0; phi.up(h) = pi;
u_rho.lo(h) = -max_u_rho; u_rho.up(h) = max_u_rho;
u_the.lo(h) = -max_u_the; u_the.up(h) = max_u_the;
u_phi.lo(h) = -max_u_phi; u_phi.up(h) = max_u_phi;
i_the.lo(h) = 0.0001;
i_phi.lo(h) = 0.0001;
Set firstlast(h) / h0, h%nh% /;
the.fx('h0') = 0;
the.fx('h%nh%') = 2*pi/3;
rho.fx(firstlast) = 4.5;
phi.fx(firstlast) = pi/4;
rho_dot.fx(firstlast) = 0;
the_dot.fx(firstlast) = 0;
phi_dot.fx(firstlast) = 0;
i_phi.fx(firstlast(h)) = (power(L - rho.l(h),3) + power(rho.l(h),3))/3.0;
i_the.fx(firstlast(h)) = i_phi.l(h)*sqr(sin(phi.l(h)));
rho.l(h) = 4.5;
the.l(h) = (2*pi/3)*sqr(ord(h)/nh);
phi.l(h) = pi/4;
rho_dot.l(h) = 0.0;
the_dot.l(h) = (4*pi/3)*(ord(h)/nh);
phi_dot.l(h) = 0.0;
step.l = 1/nh;
i_phi.l(h) = (power(L - rho.l(h),3) + power(rho.l(h),3))/3.0;
i_the.l(h) = i_phi.l(h)*sqr(sin(phi.l(h)));
Model robot / all /;
$if set workSpace robot.workSpace = %workSpace%
solve robot miniziming tf using nlp;