A FLEXIBLE CROSS-ROBOT SOFTWARE FRAMEWORK FOR ROBOT CONTROL: FROM ON-BOARD TO CLOUD EXECUTION

Abstract

The widespread use of robotics in new application domains outside the industrial workplace settings requires robotic systems that demonstrate functionality far beyond that of industrial robotic machines. The implementation of these additional capabilities inevitably increases the complexity of the robotic platforms at the hardware, control and application software level. As a result the complexity of today's robots targeting new applications in partially unstructured environments has reached a noticeable extent, e.g. such robots typically consist of a large number of actuators, sensors, and processors communicating through several interfaces. These emerging applications involve complex tasks that also vary and have to be carried out within a partially unknown environment requiring advanced capabilities with respect to system autonomy and adaptability, which further increases the intricacy of the system software architecture creating an additional challenge on the development of robotic software. The selection among the available software middlewares is not an easy task for the research community as well as for companies interested in exploiting them. A software framework that targets to enable the above described applications, should provide Real-Time (RT) performance with minimum jitter at relatively high control frequency (e.g. 1 kHz) for the low-level, code reuse, hardware abstraction layer, interoperability with existing frameworks, robustness and small footprint. Moreover a robotic software middleware has to be able to assure cross-robot compatibility, in the sense that it should be possible to use it with any robot, without code modification, but only changing a set of configuration files. A software architecture with the above performance features would lead to the possibility of effectively running autonomous and semi-autonomous skills on-board the robot. This is essential for application domains such as logistics, robot companions in houses and offices and co-workers in industrial, manufacturing and construction spaces, which require Human-Robot Collaboration (HRC), either sharing the same workspace or in a remote teleoperation scenario. The architectures and limitations of the existing frameworks are first studied and discussed in this thesis. A novel software architecture for robotics called XBotCore is then introduced in details and its experimental validation on different robotic platforms, from industrial manipulators, to humanoids, to quadruped robots is demonstrated and discussed. Having developed the core of the software architecture, this thesis then introduces a state-of-the-art multimodal (verbal, motion, force) tele-interaction control framework. Motivated by biological findings, the framework is capable to deal with the uncertainty of the interaction forces by exploring an online autonomous impedance regulation principle to adapt to payload or interaction forces variations. In the experimental trials performed, the efficacy of the individual components of the framework was proofed, with focus on the autonomous impedance regulator for force interaction; moreover the overall multimodal tele-interaction framework was validated for a human-robot collaboration task scenario that involves carrying and transporting a heavy object with a humanoid robot. However, the limitation of the on-board computational power installed on the robotic systems represents a significant barrier for their deployment in emerging applications within human working and domestic spaces, which eventually compromises their application. An approach to address this issue is to exploit the cloud computing, cloud storage, and other Internet technologies to benefit from the powerful computational, storage, and communications resources of modern data centers. In this regard, the present thesis illustrates the XBotCloud framework, which extends the XBotCore by providing the tools and mechanisms to enable users and robots to securely exploit the

Date of Award	1 Aug 2020
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Simon Watson (Supervisor) & Barry Lennox (Supervisor)