# Bluetounge 2.0: Steering and Joint Control Software

## Bluetounge 2.0: Steering and Joint Control Software

Welcome back to the second article in our three part series on the BLUEtounge 2.0 Rover’s suspension and drive system. In our last post Chris wrote about the mechanical re-design of the system, and in this post we will look at how we designed the high level software architecture for this system. We will also look at some of the challenges we faced along the way.

## The System

The BLUEtounge 2.0 Rover has four independently controlled wheels, with the front two wheels also being able to steer. This was a big departure from BLUEtounge 1.0’s skid steer system, which used six wheels, and turned by having the wheels on one side of the rover spin in the opposite direction to those on the other side of the rover. The system was meant as a stepping stone towards a full swerve drive system on either BLUEtounge, or our next rover platform NUMBAT.

Furthermore the BLUEsat Off-World Robotics code base is based around the R.O.S (Robotics Operating System) framework. This framework provides a range of existing software and hardware integrations, and is based around the idea of many separate processes (referred to as nodes), that communicate over TCP based ROS ‘topics’ using data structures called ‘messages’.

That, along with the nature of BLUEsat as a student society placed some interesting requirements on our design:

• The system needed to be able to work with only two wheel modules being able to steer, but as much as possible the code needed to be reusable for a system with four such modules.
• The system needed to avoid being heavily tied to the motors and embedded systems used on BLUEtounge, as many of them would be changing for NUMBAT.
• Due to European Rover Challenge (ERC) requirements, the system needed to support user input, and be able to be controlled by an AI.

As a consequence of the above, and to avoid reinventing the wheel (no pun intended), the system needed to use standard ROS messages and conventions as much as possible. It also needed to be very modular to improve reusability.

## User Input

The user controls the rover’s speed and rotation using an xbox controller. After some investigation, our initial approach was to have one of the analogue sticks control the rover’s direction, whilst the other controlled its speed. This was primarily because we had found that using a single stick to control direction and speed was not very intuitive for the user.

As ROS joystick analogue inputs are treated as a range between -1 and 1 on two axes, the first version of the system simply used the up/down axis of the left stick as the magnitude applied to a unit vector formed by the position of right stick. The code looked a bit like this:


double magnitude = joy->axes[SPEED_STICK] * SPEED_CAP;
cmdVel.linear.x = joy->axes[DIRECTION_STICK_X] * magnitude;
cmdVel.linear.y = joy->axes[DIRECTION_STICK_Y] * magnitude * -1;


(Note: that all code in this article uses the ROS standard of x being forwards <-> backwards, and y being port <-> starboard)

This code produced a geometry_msgs::Twist message that was used by our steering system. However we found that this system had several problems:

• It was very difficult to do fine manoeuvring of the rover, because the range of slow speeds corresponded to too small an area on the joystick. However, since we could only control the power rather than the velocity of the motors, we couldn’t simply reduce the overall power of the rover as this would mean it was unable to traverse steep gradients.
• Physical deadzones on the joysticks meant that driving the rover could be somewhat jerky.
• The code above had a mathematical problem, where the rover’s max speed was higher whilst steering than could be achieved travelling in a straight line.
• Having a two axis direction control was unintuitive for the driver, and hard to control accurately.

In response to this one of our team members (Sean Thompson) developed a new control system that used only one axis for each stick. In this system the left stick was used for power, whilst the right stick was used for (port/starboard) steering.  The system also implemented dead zone and exponential scaling which allowed for better manoeuvring of the rover at low speeds, whilst still being able to utilise the rover’s full power.

Full source code for this implementation can be found here.

## Steering

The steering system for the rover allows the rover to rotate about a point on the line formed between the two rear wheels. In order to achieve this, each wheel must run at a separate speed and the two front wheels must have separate angles. The formulas used to determine these variables are displayed below.

$Latex formula$$Latex formula$

In order to accommodate this a software module was built that converted the velocity vector ($Latex formula$) discussed in the previous section, into the rotational velocities required for each of the wheel modules, and the angles needed for the front two wheels. The system would publish these values as ros messages in a form compatible with the standard ros_command module, enabling easier testing in ROS’s gazebo simulator and hopefully good compatibility with other ROS systems we might need to use in the future.

The following code was used to implement these equations:


const double turnAngle = atan2(velMsg->linear.y,velMsg->linear.x);
// we calculate the point about which the rover will rotate
// relative to the centre of our base_link transform (0,0 is the centre of the rover)
geometry_msgs::Vector3 rotationCentre;
// the x axis is in line with the rear wheels of the rover, as shown in the above diagram
rotationCentre.x = -HALF_ROVER_WIDTH_X;
// and the y position can be calculated by applying Pythagoras to the rotational radius of the rover (r_turn) and
// half the length of the rover
// omega_rover is then calculated by the magnitude of our velocity vector over the rotational radius
const double angularVelocity = fabs(sqrt(pow(velMsg->linear.x, 2) + pow(velMsg->linear.y, 2))) / rotationRadius;
//NOTE: if necessary this could be optimised
double closeBackR = fabs(rotationCentre.y - ROVER_CENTRE_2_WHEEL_Y);
double farBackR = fabs(rotationCentre.y + ROVER_CENTRE_2_WHEEL_Y);
double closeFrontR = sqrt(pow(closeBackR,2) + pow(FRONT_W_2_BACK_W_X,2));
double farFrontR = sqrt(pow(farBackR,2) + pow(FRONT_W_2_BACK_W_X,2));
//V = wr
double closeBackV = closeBackR * angularVelocity;
double farBackV = farBackR * angularVelocity;
double closeFrontV = closeFrontR * angularVelocity;
double farFrontV = farFrontR * angularVelocity;
//work out the front wheel angles
double closeFrontAng = DEG90-atan2(closeBackR,FRONT_W_2_BACK_W_X);
double farFrontAng = DEG90-atan2(farBackR,FRONT_W_2_BACK_W_X);
//if we are in reverse, we just want to go round the same circle in the opposite direction
if(velMsg->linear.x < 0) {
//flip all the motorVs
closeFrontV *=-1.0;
farFrontV *=-1.0;
farBackV *=-1.0;
closeBackV *=-1.0;
}
        //finally we flip the values if we want the rotational centre to be on the other side of the rover
if(0 <= turnAngle && turnAngle <= M_PI) {
output.frontLeftMotorV = closeFrontV;
output.backLeftMotorV = closeBackV;
output.frontRightMotorV = farFrontV;
output.backRightMotorV = farBackV;
output.frontLeftAng = closeFrontAng;
output.frontRightAng = farFrontAng;
ROS_INFO("right");
} else {
output.frontRightMotorV = -closeFrontV;
output.backRightMotorV = -closeBackV;
output.frontLeftMotorV = -farFrontV;
output.backLeftMotorV = -farBackV;
output.frontLeftAng = -farFrontAng;
output.frontRightAng = -closeFrontAng;
ROS_INFO("left");
}


Separating steering from the control of individual joints also had another important advantage, in that it significantly improved the testability and ease of calibration of the rover’s systems. Steering code could be tested to some extent in the gazebo simulator using existing plugins, whilst control of individual joints could be tested without the additional layer of abstraction provided by the steering system. It also allowed the joints to be calibrated in software (more on this in our next article).

## Joint Control System

In BLUEtounge 1.0, our joint control system consisted of many lines of duplicated code in the main loop of our serial driver node. This code took incoming joystick messages and converted them directly into pwm values to be sent through our embedded systems to the motors. This code was developed rapidly and was quite difficult to maintain, but with the addition of the feedback loops needed to develop our swerve drive, the need to provide valid transforms for 3d and automation purposes, and our desire to write code that could be easily moved to NUMBAT – a new solution was needed.

We took an object oriented approach to solving this problem. First a common JointController class was defined, this would be an abstract class that handled subscribing to the joints control topic, calling the joints update functions and providing a standard interface for use by our hardware driver (BoardControl in the diagram below) and transform publisher (part of JointsMonitor).  This class would be inherited by classes for each type of joint, where the control loop for that joint type could be implemented (For example the drive motors control algorithm was implemented in JointVelocityController, whilst the swerve motors where implemented in JointSpeedBasedPositionController).

In addition a JointMonitor class was implemented, this class stored a list of joints and published debugging and transform information at set increments. This was a significant improvement in readability from our previous ROS_INFO based system as it allowed us to quickly monitor the joints we wanted. The main grunt of this class was done in the endCycle function, which was called after the commands had been sent to the embedded system. It looked like this:


// the function takes in the time the data was last updated by the embedded system
// we treat this as the end of the cycle
void JointsMonitor::endCycle(ros::Time endTime) {
cycleEnd = endTime;
owr_messages::board statusMsg;
ros::Time estimateTime = endTime;
int i,j;
// currentStateMessage is a transform message, we publish of all the joints
currentStateMessage.velocity.resize(joints.size());
currentStateMessage.position.resize(joints.size());
currentStateMessage.effort.resize(joints.size());
currentStateMessage.name.resize(joints.size());
// we look through each joint and estimate its transform for a few intervals in the future
// this improves our accuracy as our embedded system didn't update fast enough
for(i =0; i < numEstimates; i++, estimateTime+=updateInterval) {
j =0;
for(std::vector<JointController*>::iterator it = joints.begin(); it != joints.end(); ++it, j++) {
jointInfo info = (*it)->extrapolateStatus(cycleStart, estimateTime);
publish_joint(info.jointName, info.position, info.velocity, info.effort, j);
}
statesPub.publish(currentStateMessage);
}
// we also publish debugging information for each joint
// this tells the operator where we think the joint is
// how fast we think it is moving what PWM value we want it to be at.
for(std::vector<JointController*>::iterator it = joints.begin(); it != joints.end(); ++it, j++) {
jointInfo info = (*it)-&amp;amp;gt;extrapolateStatus(cycleStart, endTime);
owr_messages::pwm pwmMsg;
pwmMsg.joint = info.jointName;
pwmMsg.pwm = info.pwm;
pwmMsg.currentVel = info.velocity;
pwmMsg.currentPos = info.position;
pwmMsg.targetPos = info.targetPos;
statusMsg.joints.push_back(pwmMsg);
}
debugPub.publish(statusMsg);
}


Overall this system proved to be extremely useful, it allowed us to easily adjust code for all motors of a given type and reuse code when new components where added. In addition the standardised interface allowed us to quickly debug problems (of which there where many), and easily add new functionality. One instance where this came in handy was with our lidar gimbal, the initial code to control this joint was designed to be used by our autonomous navigation system, but we discovered for some tasks it was extremely useful to mount a camera on top and use the gimbal to control the angle of the camera. Due to the existing standard interface it was easy to add code to our joystick system to enable this, and we didn’t need to make any major changes to our main loop which would have been risky that close to the competition.

## Conclusion

Whilst time consuming to implement and somewhat complex this system enabled us to have a much more manageable code base. This was achieved by splitting the code into separate ROS nodes that supported standard interfaces, and using an OO model for implementing our joint control. As a result it is likely that this system will be used on our next rover (NUMBAT), even though the underlying hardware and the way we communicate with our embedded systems will be changing significantly.

Next in this series you will hear from Simon Ireland on the embedded systems we needed to develop to get position feedback for a number of these joints, and some of the problems we faced.

Code in this article was developed for BLUEsat UNSW with contributions from Harry J.E Day, Simon Ireland and Sean Thompson, based on the BLUEtounge 1.0 steering and control code by Steph McArthur, Harry J.E Day, and Sam Scheding. Additional assistance in review and algorithm design was provided by Chris Squire, Chris Miller, Yiwei Han, Helena Kertesz, and Sebastian Holzapfel. Full source code for the BLUEtounge 2.0 rover as deployed at the European Rover Challenge 2016, as well as a full list of contributors, can be found on github.

##### Author: Harry J.E Day

Harry J.E Day is a long term member of BLUEsat, with a keen interest in robotics and distributed systems. Harry has been involved with BLUEsat UNSW for four years, primarily as part of the Off-World Robotics Division including two and a half as the rover software team lead, as well as a year as the society's secretary. Harry studied B Computer Science at UNSW Sydney.