To Örebro University

Task and Motion Planning (TAMP) involves jointly addressing high-level task sequencing and low-level geometric motion planning. While early approaches treated these layers independently, it became clear that tight integration is essential: plans that are valid symbolically may fail geometrically during execution. As autonomous robots are increasingly deployed in complex, unstructured environments, addressing the combined TAMP problem in challenging domains has gained a lot of interest. One such domain gaining attention ove rthe past decade involves granular materials. Granular materials like sand, soil ,and grains exhibit both solid-like and fluid-like behavior, making their interaction with robotic systems highly uncertain and difficult to predict. For instance, the quantity of material a robot can scoop or transport depends on numerous unpredictable factors. This introduces unique challenges for planning and execution, as traditional strategies often struggle to adapt to suc hdynamic conditions. In practice, dealing with granular materials often requires blending pre-computed plans with online feedback mechanisms to remain effective.

To address these challenges, this thesis focuses on the Material Flow Planning Problem (MFPP), namely the problem of efficiently moving granular materials from one place to another. The MFPP lies at the intersection of symbolic reasoning and reliable real-world execution and requires addressing three core tasks: planning actions, navigating to target locations, and executing actions under uncertainty. This thesis proposes an integrated, multi-layered planning and execution framework to bridge abstraction levels, from symbolic to physical execution. Central to this is a formal definition of the MFPP that enables reasoning across symbolic and continuous domains. Based on this, this thesis introduces Athena, a hierarchical planning framework that combines symbolic planning with behavior trees for adaptive and fault-tolerant execution. In order to reach and manipulate the material within these planned tasks, this thesis introduces Navigo, a context-aware navigation system that dynamically selects path planning and control strategies based on real-time feedback from the environment (e.g., terrain, weather, and obstacles), enabling robust autonomous navigation in harsh conditions. Finally, this thesis introduces Atlantis, a modular, multi-layered simulation framework designed to test andiiievaluate planning and control strategies across varying levels of abstraction: from high-level task models to physics-based simulations and real-world trials. Atlantis supports systematic testing and development, helping close the gap between high-level AI planning and low-level robotic execution. Overall, this work offers a robust and adaptable approach to TAMP in domains involving granular materials, providing new methods for planning and execution for complex scenarios both in simulation and real-world applications.