Many emerging controls applications have seen increased operational complexity due to the deployment of embedded, networked systems that must interact with the physical environment. In order to manage this complexity, we design different control modes for each system and use motion description languages (MDL) to specify a sequence of these controllers to execute at run-time. Unfortunately, current MDL frameworks lose some of the important details (i.e. power, spatial, or communication capabilities) that affect the execution of the control modes. This work presents several computational tools that work towards closing MDL's specification-to-execution gap, which can result in undesirable behavior of complex systems at run-time. First, we develop the notion of an MDL compiler for control specifications with spatial, energy, and temporal constraints. We define a new MDL for networked systems and develop an algorithm that automatically generates a supervisor to prevent incorrect execution of the multi-agent MDL program. Additionally, we derive conditions for checking if an MDL program satisfies actuator constraints and develop an algorithm to insert new control modes that maintain actuator bounds during the execution of the MDL program. Finally, we design and implement a software architecture that facilitates the development of control applications for systems with power, actuator, sensing, and communication constraints.

Our research focuses on one of the more fundamental issues in multi-agent, mobile robotics: the formation control problem. The idea is to create controllers that cause robots to move into a predefined formation shape. This is a well studied problem for the scenario in which the robots know in advance to which point in the formation they are assigned. In our case, we assume this information is not given in advance, but must be determined dynamically. This thesis presents an algorithm that can be used by a network of mobile robots to simultaneously determine efficient robot assignments and formation pose for rotationally and translationally invariant formations. This allows simultaneous role assignment and formation sysnthesis without the need for additional control laws. The thesis begins by introducing some general concepts regarding multi-agent robotics. Next, previous work and background information specific to the formation control and assignment problems are reviewed. Then the proposed assignment al- gorithm for role assignment and formation control is introduced and its theoretical properties are examined. This is followed by a discussion of simulation results. Lastly, experimental results are presented based on the implementation of the assignment al- gorithm on actual robots.

The objective of this thesis is to generate a discrete alphabet of low-level robotic controllers rich enough to mimic the actions of high-level users using the robot for a specific task. This alphabet will be built through the analysis of various user data sets in a modified version of the motion description language, MDLe. It can then be used to mimic the actions of a future user attempting to perform the task by calling scaled versions of the controls in the alphabet, potentially reducing the amount of data required to be transmitted to the robot, with minimal error. In this thesis, theory is developed that will allow the construction of such an alphabet, as well as its use to mimic new actions. A MATLAB algorithm is then built to implement the theory. This is followed by an experiment in which various users drive a Khepera robot through different courses with a joystick. The thesis concludes by presenting results which suggest that a relatively small group of users can generate an alphabet capable of mimicking the actions of other users, while drastically reducing bandwidth.

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This dissertation presents the research, development and application of trajectory creation, obstacle avoidance and trajectory optimisation methods for an existing serial manipulator power line inspection robot (PLIR). The obstacle avoidance implementation allows the robot to navigate around an obstacle obstructing its navigation along the line. The algorithm generated end effector trajectory waypoints autonomously based on bounding box obstacle descriptions in Cartesian space, and connected them with a fifth order basis-spline end effector trajectory command. The trajectories were created taking into account the dynamic torque and velocity constraints of the robot while ignoring non-linearities. Performance was inspected and evaluated in a simulated workspace environment. The trajectory optimisation was designed to maximise the robot’s operating range, with constraints on the battery power supply, by minimising charge consumed during obstacle avoidance trajectories. The temporal components of the basis-spline trajectories were optimised by minimising a timeenergy type of cost function subject to the dynamic constraints of the robot. Cost function analyses are presented for a simple frictionless robot model based on the recursive Newton-Euler method, and for a more realistic model including viscous, Coulomb and static friction as well as gearbox backlash. It is shown that the Nelder-Mead simplex method was appropriate for optimisation. For representative trajectories that were studied, the optimiser was capable of finding global minima with satisfactory speed and accuracy in simulation. The validity of trajectory optimisation with regard to the cost function behaviour was confirmed. This was based on experiments carried out on the robot hardware in the laboratory, examining the predicted and actual actuator current profiles. The engineering design and implementation of hardware and software for the base station and on-board system is presented, together with the layout of the PLIR’s control system and PID (proportional-integral-derivative) controller design. Trajectory commands are sent from the base station to the robot via Wi-Fi for execution. Furthermore, live video feed from the robot can be sent to the ground station computer. Furthermore, high voltage testing of the PLIR showed that the engineering design of the robot and communication platform is robust. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2012.

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Technology today has progressed to the point that the true potential of robotics is beginning to be realized. However, programming robots to be robust across varied environments and objectives, in a way that is accessible and intuitive to most users, is still a difficult task. There remain a number of unmet needs. For example, many existing solutions today are proprietary, which makes widespread adoption of a single solution difficult to achieve. Also, most approaches are highly targeted to a specific implementation. But it is not clear that these approaches will generalize to a wider range of problems and applications. To address these issues, we define the Interaction Space, or the space created by the interaction between robots and humans. This space is used to classify relevant existing work, and to conceptualize these unmet needs. GTax, a knowledge transfer framework, is presented as a solution that is able to span the Interaction Space. The framework is based on SysML, a standard used in many different systems, which provides a formalized representation and verification. Through this work, we demonstrate that by generalizing across the Interaction Space, we can simplify robot programming and enable knowledge transfer between processes, systems and application domains.