# Calculating next position for 3DOF arm

Hi all, first, I have two versions of my question: 1) what is wrong with the way I am trying to calculate the Jacobian and then its pseudo inverse? 2) Does anyone have a good reference for how to solve for the next angles of each joint based on the velocity of the end effector and Jacobian?

Now some background: I am working on a hexapod and trying to design the inverse kinematics and path following for the individual legs. Because this will involve a lot of repletion/trial and error, I am designing a simulator to help me test my pathing so I don’t accidentally break something. I’ve laid the groundwork for a lot of it; drawing the robot, homogenous transform calculation, even calculating the Jacobian.

What I’m running into is trouble solving for the inverse Jacobian. The first time it’s calculated seems fine and the arm moves a little bit toward its target. The second time it’s calculated, the values go to extremes causing the arm to basically invert itself. The third and subsequent times, it seems fine. (See gif for example.)

I’ve reviewed formulas, not seeing any issue there. I’ve reviewed outputs and haven’t found anything out of the ordinary besides the pseudo inverse. And the visuals appear to make sense after the inversion of the arm.

I’ve done a lot of research for examples of inverse Jacobian math but can only find symbolic solutions.

Is there something I’m missing? Code provided below, I’ll mention this is in Ocatave/Matlab.

``````clc;
clear;
clearvars;

function H = Hg(th,a,r,d) % Creates homogenous transform
H = [cosd(th) -sind(th)*cosd(a) sind(th)*sind(a) r*cosd(th);
sind(th) cosd(th)*cosd(a) -cosd(th)*sind(a) r*sind(th);
0 sind(a) cosd(a) d;
0 0 0 1];
end

function drawLeg(t, xp, yp, zp, Body, CoxaS, FemurS, TibiaS, EndEfS)
CoxaV = [CoxaS(1), FemurS(1); CoxaS(2), FemurS(2); CoxaS(3), FemurS(3)];
FemurV = [FemurS(1), TibiaS(1); FemurS(2), TibiaS(2); FemurS(3), TibiaS(3)];
TibiaV = [TibiaS(1), EndEfS(1); TibiaS(2), EndEfS(2); TibiaS(3), EndEfS(3)];

plot3(xp, yp, zp, 'b.') %draw path
hold on
plot3(CoxaV(1,:), CoxaV(2,:), CoxaV(3,:), 'm', 'LineWidth', 3) %draw coxa
plot3(FemurV(1,:), FemurV(2,:), FemurV(3,:), 'g', 'LineWidth', 3) %draw femur
plot3(TibiaV(1,:), TibiaV(2,:), TibiaV(3,:), 'r', 'LineWidth', 3) %draw tibia
axis([Body(1)-5 Body(1)+5 Body(2)-5 Body(2)+7 Body(3)-5 Body(3)+5])
axis equal
%body centered graph to make sure it stays in view while walking
grid on
hold off
pause(t)
end

function [Th1, Th2, Th3] = stepLeg (t, Th1, Th2, Th3, x, y, z)
### DESIGN ROBOT ###
% 3DOF articulting arm
x00 = 0;
y00 = 0;
z00 = .5;

a0 = 2; %distance from body center to leg 1, UPDATE FROM CAD
a1 = .5; %length of coxa in cm aka base (joint 0) to joint 1 in z direction
a2 = 1; %length of femur
a3 = 1; %length of tibia

## DH Parameters ##
Th0 = 30; %UPDATE FROM CAD
A0 = 0;
A1 = 90;
A2 = 0;
A3 = 0;
r0 = a0;
r1 = 0;
r2 = a2;
r3 = a3;
d0 = 0;
d1 = a1;
d2 = 0;
d3 = 0;

## Build vectors for each segment ##
% Each line has an extra element to help the matrix math
Body = [x00; y00; z00; 1];
Coxa = [0; a1; 0; 1];
Femur = [a2; 0; 0; 1];
Tibia = [a3; 0; 0; 1];

## Generate all homogenous transforms ##
Hb0 = Hg(Th0, A0, r0, d0);
H01 = Hg(Th1, A1, r1, d1);
H12 = Hg(Th2, A2, r2, d2);
H23 = Hg(Th3, A3, r3, d3);
Hb1 = Hb0*H01;
Hb2 = Hb0*H01*H12;
Hb3 = Hb0*H01*H12*H23;

## Calculate starting locations for each segment ##
CoxaS = Hb0*Body;
FemurS = Hb0*H01*Coxa;
TibiaS = Hb0*H01*H12*Femur;
EndEfS = Hb0*H01*H12*H23*Tibia;

## Draw the leg ##
drawLeg(t, x, y, z, Body, CoxaS, FemurS, TibiaS, EndEfS)

### Now to give a target for the leg ###
## Generate Jacobian ##
# Rotation matrices #
Rbb = [1, 0, 0; 0, 1, 0; 0, 0, 1];
Rb0 = Hb0(1:3, 1:3); %use first three rows and first three columns
Rb1 = Hb1(1:3, 1:3);
Rb2 = Hb2(1:3, 1:3);

# Displacement vectors #
dbb = 0;
db0 = Hb0(1:3, 4); %use first three rows of fourth column
db1 = Hb1(1:3, 4);
db2 = Hb2(1:3, 4);
db3 = Hb3(1:3, 4);

# Joint rotation velocity matrices #
rbb = Rbb*[0;0;1];
rb0 = Rb0*[0;0;1];
rb1 = Rb1*[0;0;1];
rb2 = Rb2*[0;0;1];

# Create the culumns representing partial diffferentials for each joint #
%zeroth = [cross(rbb, db3-dbb); rbb] % not needed since it's a fixed joint
first = [cross(rb0, db3-db0); rb0];
second = [cross(rb1, db3-db1); rb1];
third = [cross(rb2, db3-db2); rb2];

# Concatinate into the jacobian #
fullJ = [first, second, third];
% only need the cartesian portion
J = fullJ(1:3, 1:3)
Jinv = pinv(J) %Print for troubleshooting

## Calculate the velocity of the end effector ##
EndEfS %Print for troubleshooting
TargetX = x(1) %Print for troubleshooting
TargetY = y(1) %Print for troubleshooting
TargetZ = z(1) %Print for troubleshooting
Err =[x(1); y(1); z(1); 0] - EndEfS %Print for troubleshooting
fullV = ([x(1); y(1); z(1); 0] - EndEfS)*t;% Calculate velocity of EE
V = fullV(1:3); % Removing the last element since it's trash info

# Calc new joint angles #
w = pinv(J)*V; % Calc joint velocities
delTh = w.'/t % Calc change in joint angles

Th0 = Th0; %this should never change?
Th1 = Th1 + delTh(1);
Th2 = Th2 + delTh(2);
Th3 = Th3 + delTh(3);
end

### TIME STEP ###
t = .001

### BUILD PATH ###
% test to determine how piecewise funcitons would work to create path.
% This square looks really nice for now
for i = 1:96
if i <= 24
x(i) = i/6;
y(i) = 3;
z(i) = 1;
elseif 24 < i  && i <= 48
x(i) = x(i-1);
z(i) = z(i-1)+1/6;
y(i) = 3;
elseif 48 < i && i <= 72
x(i) = x(i-1) - 1/6;
z(i) = z(i-1);
y(i) = 3;
elseif 72 < i && i <= 96
x(i) = x(i-1);
z(i) = z(i-1) - 1/6;
y(i) = 3;
end
end

### Set Up Initial Angles ###
% Starting angles
Th1 = 60; %UPDATE FROM CAD
Th2 = 0;
Th3 = 0;

### Time Loop ###
for i = 1:10
[Th1, Th2, Th3] = stepLeg(t, Th1, Th2, Th3, x, y, z)
end
``````

(2) Orokos KDL library has an online solver (link) that uses IK in cartesian/joint velocity space. It is intended to run at regular intervals and computes the difference between successive cartesian coordinates for both linear and rotational velocity (Twist) then converts the cartesian twist to joint-space (IK) and then integrates the joint-space angles. It can also enforce position and velocity limits in both cartesian and joint space at points along the algorithm.

However, I am currently trying to diagnose similar behavior as you describe. I get weird output where the leg doesn’t follow a straight path to the target and intermediate values may be erratic. I believe the issue comes from the function that computes differential rotational velocity. The linear velocity is easy and works, but the rotational diff() gives me weird output and computing rotational differential velocity seems to be a tricky problem. The diff() bug seems to fit my visual results too, my leg heads off at weird angles but since it’s an iterative loop it usually eventually finds the target but may encounter multiple hiccups along the way. I’m still working on verifying this before submitting a PR. Perhaps you have a similar issue? The other IK functions of Orocos KDL work fine and produce correct results but they do IK in normal position/first-order space, not the velocity one. Of course in a control loop this can have the problem of snapping to one of the alternative solutions so you’re right to chase the velocity based solver.

Thanks for your response, glad to know I’m not the only one having issues like this.

From your description, and my brief look at the Orkos solver, you’re also working with a general solution, yes?
From a lot of my reading, using a general solution is simply poor at differentiating when there are multiple solutions as the solver usually only outputs one. So yes, maybe we are having similar issues just at different stages but hard for me to say (i’m not calculating rotational differential at all).

I did a bit of research into quadratic programming as this seems to be a reliable way of adding constraints and solving for better solutions. However, from what I understand you have to solve for both the Jacobian and the Hessian which is computationally expensive and can slow things down. Might be worth you looking into it if you want to pursue a general solution.

As for me, I think I’m going to take a step back and just use trig for my solver. If multiple solutions arise I can use some simple logic to decide between the two forms. this also seems to be a fast and reliable way of solving for the joint space even if it’s not quite as elegant.

Agreed on the multiple solution issue but it is working if I keep within certain joint limits. Also, the solver takes in “guess” (previous solved values) so it tends to output the solution closest to the guess. If the control loop is updating fairly often which means the difference between guess and the new actual values is close then the solver output stays sane. This is with the pure position solver that I am using now. Switching to the velocity solver should be even better because we are mapping velocity of frames to joints and dV would be small over small dT.

With quadratic programming I assume you are referring to linear quadratic regulators LQR or model predictive control MPC? I was initially trying to use MPC and Croccodyl to get my biped to walk. It seems like a really good way to do control but I had issues with the cost functions, tuning and overall I am not well versed in this kind of mathematics to diagnose and solve these problems. I feel like I’ve learned some more since so I’d like to try it again.

Back in 2000, when I only had slow 8bit MCUs, I used forward kinematics (simple trig) to generate a CSV file of `joint => foot-xyz` tuples. I then fed this file into a neural net trainer but with the file’s inputs:outputs reversed so it was `foot-xyz => joint angles` thus inverting FK to IK. I then had a simple perceptron that could give me joint angles for any valid foot xyz and it computed fast on an 8bit MCU. It was a hexapod so I used the same perceptron for all 6 legs so the foot-xyz coordinate was local to the hip joint. To solve the multiple solution problem here the CSV file generator filtered out undesired joint angles using simple rules.