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Self-organisation is the spontaneous emergence of spatio-temporal structures and patterns from the interaction of smaller individual units. Examples are found across many scales in very different systems and scientific disciplines, from physics, materials science and robotics to biology, geophysics and astronomy. Recent research has highlighted how self-organisation can be both mediated and controlled by confinement. Confinement is an action over a system that limits its units’ translational and rotational degrees of freedom, thus also influencing the system's phase space probability density; it can function as either a catalyst or inhibitor of self-organisation. Confinement can then become a means to actively steer the emergence or suppression of collective phenomena in space and time. Here, to provide a common framework and perspective for future research, we examine the role of confinement in the self-organisation of soft-matter systems and identify overarching scientific challenges that need to be addressed to harness its full scientific and technological potential in soft matter and related fields. By drawing analogies with other disciplines, this framework will accelerate a common deeper understanding of self-organisation and trigger the development of innovative strategies to steer it using confinement, with impact on, e.g. , the design of smarter materials, tissue engineering for biomedicine and in guiding active matter. 

Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures. 

We consider multi-robot service scenarios, where tasks appear at any time and in any location of the working area. A solution to such a service task problem requires finding a suitable task assignment and a collision-free trajectory for each robot of a multi-robot team. In cluttered environments, such as indoor spaces with hallways, those two problems are tightly coupled. We propose a decentralized algorithm for simultaneously solving both problems, called Hierarchical Task Assignment and Path Finding (HTAPF). HTAPF extends a previous bio-inspired Multi-Robot Task Allocation (MRTA) framework [1]. In this work, task allocation is performed on an arbitrarily deep hierarchy of work areas and is tightly coupled with a fully distributed version of the priority-based planning paradigm [12], using only broadcast communication. Specifically, priorities are assigned implicitly by the order in which data is received from nearby robots. No token passing procedure or specific schedule is in place ensuring robust execution also in the presence of limited probabilistic communication and robot failures.