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This paper examines pairwise collaborations in heterogeneous multi-robot systems. In particular, we focus on how individual robots, with different functionalities and dynamics, can enhance their resilience by forming collaborative arrangements that result in new capabilities. Control barrier functions are utilized as a mechanism to encode the safe operating regions of individual robots, with the idea being that a robot may be able to operate in new regions that it could not traverse alone by working with other robots. We explore answers to three questions: “Why should robots collaborate?”, “When should robots collaborate?”, and “How can robots collaborate?” To that end, we introduce the safely reachable set – capturing the regions that individual robots can reach safely, either with or without help, while considering their initial states and dynamics. We then describe the conditions under which a help-providing robot and a help-receiving robot can engage in collaboration. Next, we describe the pairwise collaboration framework, modeled through hybrid automata, to show how collaborations can be structured within a heterogeneous multi-robot team. Finally, we present case studies that are conducted on a team of mobile robots.

A brain-computer interface (BCI) is a system that allows a human operator to use only mental commands in controlling end effectors that interact with the world around them. Such a system consists of a measurement device to record the human user’s brain activity, which is then processed into commands that drive a system end effector. BCIs involve either invasive measurements which allow for high-complexity control but are generally infeasible, or noninvasive measurements which offer lower quality signals but are more practical to use. In general, BCI systems have not been developed that efficiently, robustly, and scalably perform high-complexity control while retaining the practicality of noninvasive measurements. Here we leverage recent results from feedback information theory to fill this gap by modeling BCIs as a communications system and deploying a human-implementable interaction algorithm for noninvasive control of a high-complexity robot swarm. We construct a scalable dictionary of robotic behaviors that can be searched simply and efficiently by a BCI user, as we demonstrate through a large-scale user study testing the feasibility of our interaction algorithm, a user test of the full BCI system on (virtual and real) robot swarms, and simulations that verify our results against theoretical models. Our results provide a proof of concept for how a large class of high-complexity effectors (even beyond robotics) can be effectively controlled by a BCI system with low-complexity and noisy inputs.

In robotic research and education, the cost in terms of money, expertise, and time required to instantiate and maintain robotic testbeds can prevent researchers and educators from including hardware based experimentation in their laboratories and classrooms. This results in robotic algorithms often being validated by low-fidelity simulation due to the complexity and computational demand required by high-fidelity simulators. Unfortunately, these simulation environments often neglect real world complexities, such as wheel slip, actuator dynamics, computation time, communication delays, and sensor noise. The Robotarium provides a solution to these problems by providing a state-of-the-art, multi-robot research facility to everyone around the world free of charge for academic and educational purposes. This paper discusses the remote usage of the testbed since its opening in 2017, details the testbeds design, and provides a brief tutorial on how to use it.

In this note we prove, under general conditions, that a class of swarms, based on the swarms of mating silkworm moths, are bounded and stable. Detailed proofs are given for systems with linear dynamics and the results can be generalized to any globally asymptotically stable system which is bounded-input bounded-output stable. In contrast to the classical theory of switched systems we will see that these systems are globally stable in the sense that the orbits are confined to a compact region.

In this paper, we present a robotic painting system whereby a team of mobile robots equipped with different color paints create pictorial compositions by leaving trails of color as they move throughout a canvas. We envision this system to be used by an external user who can control the concentration of different colors over the painting by specifying density maps associated with the desired colors over the painting domain, which may vary over time. The robots distribute themselves according to such color densities by means of a heterogeneous distributed coverage control paradigm, whereby only those robots equipped with the appropriate paint will track the corresponding color density function. The painting composition therefore arises as the integration of the motion trajectories of the robots, which lay paint as they move throughout the canvas tracking the color density functions. The proposed interactive painting system is evaluated on a team of mobile robots. Different experimental setups in terms of paint capabilities given to the robots highlight the effects and benefits of considering heterogeneous teams when the painting resources are limited.

Recently, significant gains have been made in our understanding of multi-robot systems, and such systems have been deployed in domains as diverse as precision agriculture, flexible manufacturing, environmental monitoring, search-and-rescue operations, and even swarming robotic toys. What has enabled these developments is a combination of technological advances in performance, price, and scale of the platforms themselves, and a new understanding of how the robots should be organized algorithmically. In this paper, we focus on the latter of these advances, with particular emphasis on decentralized control and coordination strategies as they pertain to multi-robot systems. The paper discusses a class of problems related to the assembly of preferable geometric shapes in a decentralized manner through the formulation of descent-based algorithms defined with respect to team-level performance costs.