Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

Bounded generativity: contextualising interdependencies between architecture, ecosystem, and environment in digital product innovation

Existing theorisation on digital product innovation remains predicated on a particular architectural form (modularity) and mode (unbounded generativity) of organising at scale participation of heterogeneous actors in an ecosystem. Despite the widely accepted role of product architectures in organising digital product innovation there has been limited academic engagement beyond the dynamics of modular design and its proximate context of the ecosystem. While contextualist research within information systems acknowledges the existence of wider systemic conditions underlying IS innovation, this has not received adequate attention within digital product innovation. This thesis builds on existing literature to understand the nature of interdependencies between the architecture, its proximate context of the ecosystem, and the distant context of the wider environment with the aim of developing a contextualised theory of digital product innovation for an alternative architectural form. To augment and extend existing theory, this research studies the design and development of an agent-based simulation model for forced displacement. It uses Kleine’s Choice Framework, adapted for this study, to understand how different conditions of possibility within the proximate and distant contexts shape operational and substantive choices within a digital product’s ongoing development. It follows a process research approach to unpack the sequence of events, its constituent elements, and causal trajectories over time. It is based on an in-depth case study constructed through year-long field work with the development team along with the study of associated documents and reports. The research contributes to the theory on digital product innovation by unpacking how this trilateral interdependency creates opportunity structures at different stages of the development process which shape and bound the generative potential of digital products. This thesis demonstrates how this occurs through complementary resource-relationship configurations which negotiate the systemic conditions of multiple environmental drivers and technical conditions of a hybrid digital architecture

Evolution from the ground up with Amee – From basic concepts to explorative modeling

Evolutionary theory has been the foundation of biological research for about a century now, yet over the past few decades, new discoveries and theoretical advances have rapidly transformed our understanding of the evolutionary process. Foremost among them are evolutionary developmental biology, epigenetic inheritance, and various forms of evolu- tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led to the conceptualization of an extended evolutionary synthesis. Starting from abstract principles rooted in complexity theory, this thesis aims to provide a unified conceptual understanding of any kind of evolution, biological or otherwise. This is used in the second part to develop Amee, an agent-based model that unifies development, niche construction, and phenotypic plasticity with natural selection based on a simulated ecology. Amee is implemented in Utopia, which allows performant, integrated implementation and simulation of arbitrary agent-based models. A phenomenological overview over Amee’s capabilities is provided, ranging from the evolution of ecospecies down to the evolution of metabolic networks and up to beyond-species-level biological organization, all of which emerges autonomously from the basic dynamics. The interaction of development, plasticity, and niche construction has been investigated, and it has been shown that while expected natural phenomena can, in principle, arise, the accessible simulation time and system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes