Coordination of movement and configuration in robotic swarms is a challenging endeavor. Deciding when and where each individual robot must move is a computationally complex problem. The challenge is further exacerbated by difficulties inherent to physical systems, such as measurement error and control dynamics. Thus, how to best determine the optimal path for each robot, when moving from one configuration to another, and how to best perform such determination and effect corresponding motion remains an open problem. In this paper, we show an algorithm for such coordination of robotic swarms. Our methods allow seamless transition from one configuration to another, leveraging geometric formulations that are mapped to the physical domain through appropriate control, localization, and mapping techniques. This paves the way for novel applications of robotic swarms by enabling more sophisticated distributed behaviors.

We still do not live in an Age where Equality, Diversity and Inclusion (EDI) is by default. Instead, bias and discrimination are part of the everyday. Moreover, inequality in income and wealth, which transfers into inequality in opportunity, is rising according to the 2019 UN Global Sustainable Development Report. Sadly, EDI manifests not just in what we see, hear and experience, but also in the judgments made against us. Moreover, this is not confined to the people who are responsible for creating negative experiences.

In large, distributed sensor web systems, allocating resources to complex user tasks presents significant challenges. Sensor web users and their desired tasks have differing importance in the sensor web, so designing a multi-agent framework to yield allocations that are both fair and efficient (high utility) is a challenging research problem. With complex, hierarchically-decomposable tasks, individual subtasks could potentially be assigned to a number of agents (e.g., when there is overlap in sensor or data processing capability among constituent sensor networks). Efficient allocation of subtasks within the proposed multi-agent framework presents additional challenges. Both of these research problems are further compounded by the dynamic nature of the sensor web, in which both desired tasks and resource availability change significantly with time and environmental conditions. This paper presents an overview of these research challenges and a solution approach employing broker agents in a novel variation of the contract net protocol (CNP) for fair and efficient allocation of complex tasks.