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Type of addressPostal address
Postal codeYO10 5DD
CountryUnited Kingdom
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  • Electronic Engineering
    University of York
    YO10 5DD

Phone: (01904) 322393

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Dr. Mark Andrew Post

Lecturer in Autonomous Systems &Robotics

Areas of expertise

  • Robotics
  • Mechatronics
  • Space Systems
  • Embedded Systems
  • Artificial Intelligence

PhD opportunities

Self-organizing reconfigurable cellular robots created with semantically-aware autonomic elements Description: Machines created by humans are mostly centralized, heterogeneous entities that are designed for a specific purpose and to embody specific components. This is in contrast to nearly all organisms in nature, which have evolved from self-contained elements (cells) and are composed of large groups of specialized cells that together perform complex operations and communicate by pre-arranged ensemble signalling. There are many advantages to the cellular element approach, including resilience to localized damage, accelerated parallel adaptation, and the ability to self-replicate with relative simplicity using the same mechanisms for all elements. In this research project, the challenges of creating robots or other cyber-physical systems with cellular characteristics are explored.The concept of a self-contained and self-managing "autonomic element" is used as the basis for creating a distributed, fractionated architecture in which large numbers of similar and interconnected components manage their own functions and operation while providing services to other elements in the system in an optimized fashion. Within such a system, autonomic elements should be self-configuring, self-optimizing, self-healing, self-protecting, and self-aware as well as being aware of the context of the system that it is within. As an open, adaptive and context-aware system, this architecture uses semantic contexts for passing information that is interpreted and reasoned on by each element as required so that new functions and data can be added without changes to unrelated elements. Determining the physical and programmatic form that such an architecture should take is a key part of this project, and innovating on existing approaches to distributed systems theory is necessary to incorporate the adaptivity and resilience required for operation in an uncertain world. Ontological organization with base concepts is used for flexibility, queued lock-free communication with state estimation is used for reliability, and a modular approach to implementation on heterogeneous serially-interconnected microcontrollers can be a physical form. Using this architecture, a complete robotic system can be constructed with enhanced capabilities that arise from the cellular approach. For more details about this project, please contact Dr. Mark Andrew Post, email: Automated probabilistic programming and learning methods for robotic behaviours in an uncertain and changing world Description: Nearly all programmable computing systems are based by design on exact logic, under the assumption of a "perfect" mathematical world. By extension, logic used in robotics and other autonomous systems is also exact. However, all cyber-physical systems must interact with a world using sensors and actuators that by the very nature of this interaction, are not fundamentally exact or deterministic. Machine learning methods based on neural-inspired models have done much to allow machines to cope with inexact interactions with the world, but at the cost of explainability and adherence to desired models that we believe are considered "known" and need not be learned. This research project seeks to achieve a middle ground between exact computation and pure machine learning by applying a probabilistic programming paradigm to the design of robots and autonomous systems. "Programs" are created as semantic hierarchies such as a Bayesian network of random variable tensors, and tensor arithmetic is used to operate on "data" within these tensors to achieve desired behaviours. The programming model is therefore functional rather than procedural. Additional benefits of using tensor arithmetic to achieve behaviours include that operation is continuous rather than having a defined "start" and "end", and no segmentation faults or program errors are possible since all operations have a closed domain and range. A variety of programming methods can be implemented together for such a system. Expert knowledge can be encoded by a programmer writing a program as a hierarchy of functions or elements in SysML or AADL, abstract models of behaviour can be encoded by creating a structure of elements and data that reflects a real system, and "learning" programs is possible by creating structures from desired or historic data. In this project, methods of probabilistic programming such as these will be implemented with the goal of allowing robots and autonomous systems to be created with greater simplicity and efficiency for the challenges of dealing appropriately with harsh environments, uncertainty and variability. For more details about this project, please contact Dr. Mark Andrew Post, email: Autonomous cooperation of bio-inspired adaptable lightweight mobile robots Description: In challenging planetary environments such as Mars, a diverse range of terrains must be traversed to deliver sensing instruments and achieve scientific goals. Stringent constraints are be placed on the power use, payload mass, and terrain tolerances of the robots that must carry these instruments over long distances for future missions to other planets. A single type of robot and mobility system is in many cases not capable of satisfying all the necessary constraints while providing efficient mobility over sand, rocks, and sloped areas, much less in caves and liquid environments. This research focuses on the design, fabrication, and autonomous control of simple but dissimilar mobile robots for planetary use. Using the principles of wheeled tensegrity and superlight mobile structures, new types of mobile robot will be developed such that they are able to cooperate with each other to reach formerly inaccessible areas and complement each others’ strengths and weaknesses in harsh environments. Physical cooperation, combination movement, and novel mobility concepts are some potential methods by which to achieve advanced mobility on complex terrains under autonomous control. Due to the separation from controllers on Earth, these robots will need to make decisions together, adapt to their environment, and deal with unexpected situations in an appropriate manner fully autonomously and could as a group be capable of performing operations such as inspection, assembly/reconfiguration, and construction of future human habitats or other hardware systems. Group sensing and control can make use of software tools developed for space robotics, including the CDFF framework developed in the InFuse project. For more details about this project, please contact Dr. Mark Andrew Post, email:

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