Consider a 4-Link robot manipulator shown below. Use the forward kinematic D-H table and write an m file that plots the manipulator. The instructions are given in the module 6. Submit your solutions by the due date, in a single MATLAB m file. Solution Please give the kinetic D-H table else it would be difficult to code as we need to know the rotation spin axis and other momentum of manipulator Stating a general example code for manipulator with data function X = fwd_kin(q,x) % given a position in the configuration space, calculate the position of % the end effector in the workspace for a two-link manipulator. % q: vector of joint positions % x: design vector (link lengths) % X: end effector position in cartesian coordinates % configuration space coordinates: q1 = q(1); % theta 1 q2 = q(2); % theta 2 % manipulator parameters: l1 = x(1); % link 1 length l2 = x(2); % link 2 length % calculate end effector position: X = [l1*cos(q1) + l2*cos(q1+q2) l1*sin(q1) + l2*sin(q1+q2)]; % SimulateTwolink.m uses inverse dynamics to simulate the torque % trajectories required for a two-link planar robotic manipulator to follow % a prescribed trajectory. It also computes total energy consumption. This % code is provided as supplementary material for the paper: % % \'Engineering System Co-Design with Limited Plant Redesign\' % Presented at the 8th AIAA Multidisciplinary Design Optimization % Specialist Conference, April 2012. % % The paper is available from: % % http://systemdesign.illinois.edu/publications/All12a.pdf % % Here both the physical system design and control system design are % considered simultaneously. Manipulator link length and trajectory % specification can be specified, and torque trajectory and energy % consumption are computed based on this input. It was found that maximum % torque and total energy consumption calculated using inverse dynamics % agreed closely with results calculated using feedback linearization, so % to simplify optimization problem solution an inverse dynamics approach % was used, which reduces the control design vector to just the trajectory % design. % % In the conference paper several cases are considered, each with its own % manipulator task, manipulator design, and trajectory design. The % specifications for each of these five cases are provided here, and can be % explored by changing the case number variable (cn). % % This code was incorporated into a larger optimization project. The code % presented here includes only the analysis portion of the code, no % optimization. % % A video illustrating the motion of each of these five cases is available % on YouTube: % % http://www.youtube.com/watch?v=OR7Y9-n5SjA % % Author: James T. Allison, Assistant Professor, University of Illinois at % Urbana-Champaign % Date: 4/10/12 clear;clc % simulation parameters: p.dt = 0.0005; % simulation step size tf = 2; p.tf = tf; % final time p.ploton = 0; % turn off additional plotting capabilities p.ploton2 = 0; p.Tallow = 210; % maximum .

1. Consider a 4-Link robot manipulator shown below. Use the forward kinematic D-H table and
write an m file that plots the manipulator. The instructions are given in the module 6. Submit
your solutions by the due date, in a single MATLAB m file.
Solution
Please give the kinetic D-H table else it would be difficult to code as we need to know the
rotation spin axis and other momentum of manipulator
Stating a general example code for manipulator with data
function X = fwd_kin(q,x)
% given a position in the configuration space, calculate the position of
% the end effector in the workspace for a two-link manipulator.
% q: vector of joint positions
% x: design vector (link lengths)
% X: end effector position in cartesian coordinates
% configuration space coordinates:
q1 = q(1); % theta 1
q2 = q(2); % theta 2
% manipulator parameters:
l1 = x(1); % link 1 length
l2 = x(2); % link 2 length
% calculate end effector position:
X = [l1*cos(q1) + l2*cos(q1+q2)
l1*sin(q1) + l2*sin(q1+q2)];
% SimulateTwolink.m uses inverse dynamics to simulate the torque
% trajectories required for a two-link planar robotic manipulator to follow
% a prescribed trajectory. It also computes total energy consumption. This
% code is provided as supplementary material for the paper:
%
% 'Engineering System Co-Design with Limited Plant Redesign'
% Presented at the 8th AIAA Multidisciplinary Design Optimization
% Specialist Conference, April 2012.
%
% The paper is available from:
%
% http://systemdesign.illinois.edu/publications/All12a.pdf

2. %
% Here both the physical system design and control system design are
% considered simultaneously. Manipulator link length and trajectory
% specification can be specified, and torque trajectory and energy
% consumption are computed based on this input. It was found that maximum
% torque and total energy consumption calculated using inverse dynamics
% agreed closely with results calculated using feedback linearization, so
% to simplify optimization problem solution an inverse dynamics approach
% was used, which reduces the control design vector to just the trajectory
% design.
%
% In the conference paper several cases are considered, each with its own
% manipulator task, manipulator design, and trajectory design. The
% specifications for each of these five cases are provided here, and can be
% explored by changing the case number variable (cn).
%
% This code was incorporated into a larger optimization project. The code
% presented here includes only the analysis portion of the code, no
% optimization.
%
% A video illustrating the motion of each of these five cases is available
% on YouTube:
%
% http://www.youtube.com/watch?v=OR7Y9-n5SjA
%
% Author: James T. Allison, Assistant Professor, University of Illinois at
% Urbana-Champaign
% Date: 4/10/12
clear;clc
% simulation parameters:
p.dt = 0.0005; % simulation step size
tf = 2; p.tf = tf; % final time
p.ploton = 0; % turn off additional plotting capabilities
p.ploton2 = 0;
p.Tallow = 210; % maximum torque requirement
% Case number:

3. cn = 4;
switch cn
case 1 % Nominal system design (sequential design, optimal control)
% Task A:
p.task = 1;
% plant design:
L = [0.6 0.6];
% trajectory design:
Xtraj = [0.18333 -0.083583 0.014569 0.14199]';
case 2 % Task A optimal co-design
% Task A:
p.task = 1;
% plant design:
L = [1.7715 1.63455];
% trajectory design:
Xtraj = [0.11281 0.120718 0.0502807 -0.43749]';
case 3 % Task B with Task A optimal design
% Task A:
p.task = 2;
% plant design:
L = [1.7715 1.63455];
% trajectory design:
Xtraj = [0.81030 0.99999 -1.09068 -0.0056275]';
case 4 % Task B co-design
% Task A:
p.task = 2;
% plant design:
L = [2.2797 1.13688];
% trajectory design:
Xtraj = [0.69136 1.5248 -0.75799 -0.061826]';
case 5 % Task B PLCD
% Task B:
p.task = 2;
% plant design:
L = [1.7715 1.100];
% trajectory design:

4. Xtraj = [0.69891 1.0943 -1.8420 0.310821]';
end
% plant design parameters:
mp = 20; % payload mass
E = 71.7e9; % Elastic modulus, Pa
delta_allow = 4e-6; % allowable deflection
t1 = 0.003; % link 1 wall thickness (m)
t2 = 0.002; % link 2 wall thickness (m)
rho = 2810; % Material density (kg/m^3) 7075 Aluminum
tau1_max = 140; % Joint 1 nominal torque
tau2_max = 80; % Joint 2 nominal torque
% intermediate plant calculations (link radius and mass):
l1 = L(1); l2 = L(2);
r1 = ((4*tau1_max*l1^2)/(3*pi*E*delta_allow) + t1^2)/(2*t1);
r2 = ((4*tau2_max*l2^2)/(3*pi*E*delta_allow) + t2^2)/(2*t2);
m1 = l1*rho*pi*(r1^2-(r1-t1)^2);
m2 = l2*rho*pi*(r2^2-(r2-t2)^2);
% extended plant design vector:
Xplant = [l1 l2 m1 m2 mp]';
% perform inverse dynamics calculations:
[minE,t,q1,q2,qd1,qd2,qdd1,qdd2,torque] = twolink(Xtraj,Xplant,p);
% save simulation data:
if exist('TwolinkData','file')
load TwolinkData
end
AD{cn}.t = t;
AD{cn}.q1 = q1;
AD{cn}.q2 = q2;
AD{cn}.qd1 = qd1;
AD{cn}.qd2 = qd2;
AD{cn}.qdd1 = qdd1;
AD{cn}.qdd2 = qdd2;
AD{cn}.L = L;
AD{cn}.xtraj = Xtraj;
AD{cn}.energy = minE;
AD{cn}.torque = torque;

5. save TwolinkData AD
% generate payload workspace trajectory using forward kinematics:
Xws = zeros(length(t),2);
for j=1:length(t)
Xws(j,:) = fwd_kin([q1(j) q2(j)]',L)';
end
% maximum torque magnitude for each joint for tf(i)
Tmax1 = max(abs(torque(:,1)));
Tmax2 = max(abs(torque(:,2)));
figure(1);clf;figure(2);clf;figure(3);clf;figure(4);clf;figure(5);clf
% plot workspace trajectory
figure(1);
plot(Xws(:,1),Xws(:,2),'k-','LineWidth',2); hold on
title('Workspace Trajectory')
% plot torque trajectories
figure(2);
plot(t,torque(:,1),'k-','LineWidth',2);hold on
title('Torque Trajectory Joint 1')
xlabel('time (s)');ylabel('tau_1 (N-m)')
figure(3);
plot(t,torque(:,2),'k-','LineWidth',2);hold on
title('Torque Trajectory Joint 2')
xlabel('time (s)');ylabel('tau_2 (N-m)')
% plot torque/speed curves
figure(4)
plot(qd1,torque(:,1),'k-','LineWidth',2); hold on
plot(qd1(1),torque(1,1),'ko','MarkerSize',10,...
'MarkerFaceColor',[.49 1 .63]);
figure(5)
plot(qd2,torque(:,2),'k-','LineWidth',2); hold on
plot(qd2(1),torque(1,2),'ko','MarkerSize',10,...
'MarkerFaceColor',[.49 1 .63]);
figure(4);
title('Torque-Speed Curve for Joint 1')
xlabel('speed (rad/s)')
ylabel('torque (Nm)')

6. figure(5);
title('Torque-Speed Curve for Joint 2')
xlabel('speed (rad/s)')
ylabel('torque (Nm)')
function [E,t1,q1,q2,qd1,qd2,qdd1,qdd2,torque,g]...
= twolink(Xtraj,Xplant,p)
% Simulate two-link manipulator using quintic trajectory and inverse
% dynamics to compute input torques and energy consumption. Designed for
% traj-only optimization with plant design passed in. No control parameters
% specified since inverse dynamics used.
tf = p.tf; % final time (assume t0 = 0)
dt = p.dt; % time step
ploton = p.ploton; % plotting flag
task = p.task; % task specification (1 = A, 2 = B)
Tallow = p.Tallow; % allowable joint torque
% Prepare trajectory parameters:
pI = Xtraj(1:2); vI = Xtraj(3:4);
% Prepare plant design vector:
X = Xplant(1:2); % link lengths (m)
% trajactory boundary conditions:
if task == 1
p0 = [0.1 0.1]';
v0 = [0 0.1]';
pf = [0.7 0.1]';
vf = [0 -0.1]';
elseif task == 2
p0 = [0.5 1.2]';
v0 = [-0.1 0]';
pf = [0.4 2.0]';
vf = [-0.1 0]';
end
% calculate trajectory:
traj = traj_quin(X,tf,p0,v0,pI,vI,pf,vf,p);
if ~isstruct(traj) % trajectory was infeasible (singular matrix)
E = 2000;
g = [2000 2000]';

7. torque = 0;t1=0;q1=0;q2=0;qd1=0;qd2=0;qdd1=0;qdd2=0;
warning('Trajectory infeasible')
return
end
% compute trajectory data
t1 = (0:dt:tf)'; nt = length(t1);
q1 = traj.traj_q1(t1);
q2 = traj.traj_q2(t1);
qd1 = traj.traj_qd1(t1);
qd2 = traj.traj_qd2(t1);
qdd1 = traj.traj_qdd1(t1);
qdd2 = traj.traj_qdd2(t1);
% compute required input torque using inverse dynamics:
u = zeros(nt,2);
for i=1:length(t1)
u(i,:) = inv_control([q1(i) q2(i)]',[qd1(i) qd2(i)]'...
,[qdd1(i) qdd2(i)]',Xplant)'; % size of u is [nt,2]
end
% calculate energy and power
q_2 = [q1, q2, qd1, qd2]; torque = u;
% net combined power
P = torque(:,1).*q_2(:,3).^2.*sign(q_2(:,3))...
+ torque(:,2).*q_2(:,4).^2.*sign(q_2(:,4));
% joint 1 power, assuming no regenerative braking
P1 = max(torque(:,1).*q_2(:,3).^2.*sign(q_2(:,3)),0);
% joint 2 power, assuming no regenerative braking
P2 = max(torque(:,2).*q_2(:,4).^2.*sign(q_2(:,4)),0);
% combined non-regenerative power
Pc = P1 + P2;
% total energy consumption
E = sum(Pc(2:end).*diff(t1));
if E > 1000
E = 1000; % penalty for infeasible designs
end
% compute torque constraint
Tmax1 = max(abs(torque(:,1)));

8. Tmax2 = max(abs(torque(:,2)));
g(1) = Tmax1 - Tallow;
g(2) = Tmax2 - Tallow;
if ploton
% plot torque:
figure(gcf+1);clf
plot(t1,torque)
legend('tau_1','tau_2')
% plot torque/speed curves:
figure(gcf+1);clf
plot(q_2(:,3),torque(:,1),'ko-'); hold on
plot(q_2(1,3),torque(1,1),'ko','MarkerSize',10,...
'MarkerFaceColor',[.49 1 .63]);
plot(q_2(:,4),torque(:,2),'ro-');
plot(q_2(1,4),torque(1,2),'ro','MarkerSize',10,...
'MarkerFaceColor',[.49 1 .63]);
legend('q_1','q_1 start','q_2','q_2 start','Location','EastOutside')
xlabel('speed')
ylabel('torque')
% plot power
figure(gcf+1);clf
plot(t1,P,'k-'); hold on
plot(t1,P1,'rd-')
plot(t1,P2,'g*-')
plot(t1,Pc,'b+-')
legend('P','P_1','P_2','P_c')
end
function traj = traj_quin(x,tf,p0,v0,pI,vI,pf,vf,p)
% Quintic trajectory generation:
% Creates trajectory functions of time if trajectory is feasible, zeros
% otherwise.
ploton = p.ploton2;
% convert to configuration space:
q0 = inv_kin(p0,x);

9. qi = inv_kin(pI,x);
qdi = inv_vel(vI,pI,x);
if qdi == 0 % singular matrix detected
traj = 0;
return
end
qf = inv_kin(pf,x);
qd0 = inv_vel(v0,q0,x);
if qd0 == 0 % singular matrix detected
traj = 0;
return
end
qdf = inv_vel(vf,qf,x);
if qdf == 0 % singular matrix detected
traj = 0;
return
end
% solve for q1 trajectories
tI = tf/2;
A1 = [1 0 0 0 0 0
0 1 0 0 0 0
1 tI tI^2 tI^3 tI^4 tI^5
0 1 2*tI 3*tI^2 4*tI^3 5*tI^4
1 tf tf^2 tf^3 tf^4 tf^5
0 1 2*tf 3*tf^2 4*tf^3 5*tf^4];
b1 = [q0(1) qd0(1) qi(1) qdi(1) qf(1) qdf(1)]';
a1 = A1b1;
% solve for q2 trajectories
b2 = [q0(2) qd0(2) qi(2) qdi(2) qf(2) qdf(2)]';
a2 = A1b2;
% form trajectory functions
traj_q1 = @(t) a1(1)+a1(2)*t+a1(3)*t.^2+a1(4)*t.^3+a1(5)*t.^4+a1(6)*t.^5;
traj_q2 = @(t) a2(1)+a2(2)*t+a2(3)*t.^2+a2(4)*t.^3+a2(5)*t.^4+a2(6)*t.^5;
traj_qd1 = @(t) a1(2)+2*a1(3)*t+3*a1(4)*t.^2+4*a1(5)*t.^3+5*a1(6)*t.^4;
traj_qd2 = @(t) a2(2)+2*a2(3)*t+3*a2(4)*t.^2+4*a2(5)*t.^3+5*a2(6)*t.^4;
traj_qdd1 = @(t) 2*a1(3)+6*a1(4)*t+12*a1(5)*t.^2+20*a1(6)*t.^3;

10. traj_qdd2 = @(t) 2*a2(3)+6*a2(4)*t+12*a2(5)*t.^2+20*a2(6)*t.^3;
% form output argument
traj.traj_q1 = traj_q1;
traj.traj_q2 = traj_q2;
traj.traj_qd1 = traj_qd1;
traj.traj_qd2 = traj_qd2;
traj.traj_qdd1 = traj_qdd1;
traj.traj_qdd2 = traj_qdd2;
% plot
if ploton
figure(1);clf
fplot(traj_q1,[0 tf]); hold on
set(findobj(gca, 'Type', 'Line'), 'LineWidth', 2,'Color', 'k');
fplot(traj_q2,[0 tf],'r-'); hold on
set(findobj(gca, 'Type', 'Line'), 'LineWidth', 2);
legend('q_1','q_2')
title('Position Trajectories')
% plot
figure(2);clf
fplot(traj_qd1,[0 tf]); hold on
set(findobj(gca, 'Type', 'Line'), 'LineWidth', 2,'Color', 'k');
fplot(traj_qd2,[0 tf],'r-'); hold on
set(findobj(gca, 'Type', 'Line'), 'LineWidth', 2);
legend('qd_1','qd_2')
title('Velocity Trajectories')
end
function u = inv_control(q,qd,qdd,X)
% Compute required input torque as a function of desired trajectory and the
% extended plant design vector.
% check vector orientation:
if isrow(q)
q = q';
end
if isrow(qd)

11. qd = qd';
end
if isrow(qdd)
qdd = qdd';
end
q1 = q(1);
q2 = q(2);
q1d = qd(1);
q2d = qd(2);
% physical constants:
g = 9.81; % gravitational constant
% manipulator parameters:
l1 = X(1); % link 1 length
l2 = X(2); % link 2 length
m1 = X(3); % link 1 mass
m2 = X(4); % link 2 mass
mp = X(5); % payload mass
% dependent parameters:
lc1 = l1/2; % link 1 cm location
lc2 = l2*(m2/2+mp)/(m2+mp);
% link 2 cm location
I1 = m1*l1^2/12; % link 1 mass moment of inertia
Ilink = m2*l2^2/12 + m2*(lc2-l2/2)^2;
Ipayload = mp*(l2-lc2)^2;
I2 = Ilink+Ipayload;% link 2 mass moment of inertia
% update total link masses for D(q) calculation:
m2 = m2 + mp;
% form inertia matrix:
D(1,1) = m1*lc1^2 + m2*(l1^2+lc2^2+2*l1*lc2*cos(q2)) + I1 + I2;
D(1,2) = m2*(lc2^2+l1*lc2*cos(q2)) + I2;
D(2,1) = D(1,2);
D(2,2) = m2*lc2^2 + I2;
% form damping matrix:
h = -m2*l1*lc2*sin(q2);
C(1,1) = h*q2d;
C(1,2) = h*q2d + h*q1d;

12. C(2,1) = -h*q1d;
% form gravity vector:
G(1,1) = (m1*lc1+m2*l1)*g*cos(q1) + m2*lc2*g*cos(q1+q2);
G(2,1) = m2*lc2*g*cos(q1+q2);
% compute required torque (outputs column vector)
u = D*qdd + C*qd + G;
function qd = inv_vel(xi,q,x)
% Given a velocity of the end effector in the workspace, calculate the
% velocity in the configuration space.
% configuration space coordinates:
q1 = q(1); % theta 1
q2 = q(2); % theta 2
% manipulator parameters:
l1 = x(1); % link 1 length
l2 = x(2); % link 2 length
% Jacobian:
J = [(-l1*sin(q1) - l2*sin(q1+q2)) (-l2*sin(q1+q2))
(l1*cos(q1) + l2*cos(q1+q2)) (l2*cos(q1+q2))];
% Configuration space velocity:
if cond(J) > 1000
qd = 0;
return
end
qd = Jxi;
function q = inv_kin(X,x)
% Given a position of the end effector in the workspace, calculate the
% position in the configuration space.
% workspace coordinates:
xc = X(1); % horizontal
yc = X(2); % vertical
% manipulator parameters:
l1 = x(1); % link 1 length
l2 = x(2); % link 2 length
D = (xc^2+yc^2-l1^2-l2^2)/(2*l1*l2);
% this assumes elbow down position
q2 = atan2(-sqrt(1-D^2),D);

13. q1 = atan2(yc,xc) - atan2(l2*sin(q2),l1+l2*cos(q2));
q = [q1 q2]';