System Overview of Layer 2 78 

This section provides a system overview of Layer 2 of the dynamic teleoperation VRUI as shown in Figure 28. This layer is responsible for auto- categorising individual robots into commonly used categories using kinematic information obtained from the URDF available in ROS. Although the proposed robot auto-categorisation system described in Layer 2 uses a trained ANN, other classification techniques, such as support vector machine (SVM) or a fuzzy neural network (FNN), could also be used.

Figure 28. Layer 2 system diagram

Categorisation using an ANN requires training with a set of known samples from each category. This training process determines the accuracy of the ANN, and its success hinges on its ability to determine a correlation between

its input and corresponding output. In this case, varying numbers of kinematic samples for each robot category are required. While ROS supports over one hundred robots this number is insufficient to create a well-trained ANN. To overcome this challenge, this work used several samples from each robot using a series of different poses. For each sample pose, the kinematic information containing the position and rotation values of the joints are assigned as inputs and the target robot category represents the output (Figure 29).

Figure 29. Overview of the proposed robot auto-categorisation system Note: Uses robot kinematic information to categorise robots into commonly

used categories

4.2.1. Extracting Robot Kinematic Data in ROS

ROS has been rapidly adopted since its initial release in 2007 and currently supports over 100 different robots from major developers, such as NASA [257], Willow Garage [242], Rethink Robotics [258], PAL Robotics [259], and Aldebaran [260]. The ROS community maintains several robot-independent ROS packages that provide a vast array of functionalities, such as MoveIt! [261] for motion planning and mobile manipulation; RViz [136], which provides sensor visualisation; and Rosbridge [235], which is an interface for external communication in the ROS environment.

ROS provides URDF and SRDF, which are used to provide descriptive information about individual ROS robots. URDF and SRDF use the extensible mark-up language (XML) that contains 7 and 12 XML elements, respectively. Link, joint, sensor, transmission, Gazebo, model, and model state are the seven elements used in the URDF to describe the kinematic, dynamic, collision, visual, and Gazebo simulator properties [250] of a robot. However, URDF has limitations, such as insufficient capability to describe a serial chain of joints that typically represent manipulators or provide end-effector information. The SRDF was developed to overcome such shortcomings by defining the group, group state, chain, and end-effectors using XML elements. The remaining SRDF XML elements include robot, link, joint, virtual joint, disable collisions, passive joint, sphere, and link sphere estimation. The extra elements in the SRDF make it ideal for motion control applications, as demonstrated in its implementation in the ROS MoveIt! mobile manipulation package [261].

In this thesis, the URDF is used to obtain kinematic data from each of the selected ROS robots in a given pose and used as a single sample to train the designed ANN. The ANN is required to determine the category of a robot based on its kinematic structure, as captured from its URDF information. When a robot is connected to a ROS environment, it typically provides the ROS parameter server its URDF file, this can then be used in several packages, such as RViz [136], to identify its current state. The URDF is used to obtain sample poses for the ANN by searching for active joint types under joint elements, such as revolute or prismatic. Once all active joints are found, they can be traversed to obtain the kinematic information of the robot using each joint’s position and

rotation information. As the robot moves through a set of random poses, each pose that is captured represents a sample for training the proposed ANN.

4.2.2. Robot Category Definitions

Roboticists categorise robots using commonly known terminology in the field, such as UAV, UGV, manipulator, and humanoid robots. These categories are abstract in nature and have been naturally introduced over time. They were developed so the robotic community could have a common terminology for easy reference to robot groups. For example, manipulator refers to a group of robots that function like an arm, while UAV describes a group of robots that can fly. Six common robot categories, including simple definitions, are listed in Table 5 with Figure 30 illustrating the typical kinematic configurations.

Table 5. Commonly used robot category definitions

Common Robot Categories

UAV Flying robot that includes quadcopters, hexacopters, and octocopters [88, 262, 263]

UGV Mobile robot that does not contain any manipulators and uses either wheels or special tracks to navigate its terrain. [31, 264]

Manipulator Replicates an arm represented by a chain of joints between its base and end effector. [265]

Mobile Manipulator

A mobile manipulator is any robot that has at least one manipulator and can move around its environment using a mobile base. [266, 267]

Torso

A torso robot typically replicates the upper half of a human body. It includes more than one manipulator and does not have the capability to navigate its environment through the likes of a mobile base. [268]

Humanoid A humanoid robot contains at least two arms, two legs, and a head similar to a human being; it may also consist of a waist joint. [269] Although no standard exists to provide a clear definition for these categories, the categories are considered common knowledge within the robotics field and provide a quick way to communicate the capabilities of a robot. These categories are typically used by roboticists to convey information about robots, yet no approach that allows computers to accurately categorise a robot into these commonly known groups currently exists. The capability of a computerised system to auto-categorise robots using common human terminology could benefit teleoperation applications, especially that for robot teams.

Figure 30. Kinematic configuration of six robot categories

Note: Created using RViz via the URDF standard available in ROS (a) ABB IRB2400 manipulator, (b) AR Drone UAV, (c) Husky UGV, (d) Motoman

SDA10D Torso, (e) Husky and UR5 mobile manipulator and (f) NAO humanoid

As previously discussed, one of the main functions of teleoperators when teleoperating multiple robots is switching control between individual robots. If the robot team is heterogeneous, then identifying the category of a robot quickly could be challenging and is likely to impede performance because of the time required for the teleoperator to identify the capabilities of a selected robot. This situation is one of the challenges that the dynamic teleoperation VRUI hopes to overcome by automatically identifying the category of the robot, such that the system can present a VRUI that best suits the currently selected robot. If the robot category in known, then the system can provide a set of common teleoperation controls that are related to the identified category. For example, all UAV/UGV could be provided the same set of flight/driving controls. Such an approach would reduce the overall complexity of the teleoperation system and the required amount of operator training due to the use of the same set of controls being used for different robots of the same category. Another benefit could also be achieved by providing a symbolic or text-based representation or within the VRUI that refers to the robot category currently under control. This approach would enable teleoperators to quickly identify the capabilities of a robot when switching between robots.