Person:

Nagpal, Radhika

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few tens of robots. To address this issue, this paper presents Kilobot, a low-cost robot designed to make testing collective algorithms on hundreds or thousands of robots accessible to robotics researchers. To enable the possibility of large Kilobot collectives where the number of robots is an order of magnitude larger than the largest that exist today, each robot is made with only $14 worth of parts and takes 5 minutes to assemble. Furthermore, the robot design allows a single user to easily operate a large Kilobot collective, such as programming, powering on, and charging all robots, which would be difficult or impossible to do with many existing robotic systems.

Coordination within decentralized agent groups frequently requires reaching global consensus, but typical hierarchical approaches to reaching such decisions can be complex, slow, and not fault-tolerant. By contrast, recent studies have shown that in decentralized animal groups, a few individuals without privileged roles can guide the entire group to collective consensus on matters like travel direction. Inspired by these findings, we propose an implicit leadership algorithm for distributed multi-agent systems, which we prove reliably allows all agents to agree on a decision that can be determined by one or a few better-informed agents, through purely local sensing and interaction. The approach generalizes work on distributed consensus to cases where agents have different confidence levels in their preferred states. We present cases where informed agents share a common goal or have conflicting goals, and show how the number of informed agents and their confidence levels affects the consensus process. We further present an extension that allows for fast decision-making in a rapidly changing environment. Finally, we show how the framework can be applied to a diverse variety of applications, including mobile robot exploration, sensor network clock synchronization, and shape formation in modular robots.

We present a scalable approach to optimizing robot control policies for a target collective behavior in a spatially inhomogeneous robotic swarm. The approach can incorporate robot feedback to maintain system performance in an unknown environmental flow field. We consider systems in which the robots follow both deterministic and random motion and transition stochastically between tasks. Our methodology is based on an abstraction of the swarm to a macroscopic continuous model, whose dimensionality is independent of the population size, that describes the expected time evolution of swarm subpopulations over a discretization of the environment. We incorporate this model into a stochastic optimization method and map the optimized model parameters onto the robot motion and task transition control policies to achieve a desired global objective. We illustrate our methodology with a scenario in which the behaviors of a swarm of robotic bees are optimized for both uniform and nonuniform pollination of a blueberry field, including in the presence of an unknown wind.

We present an approach to designing scalable, decentralized control policies that produce a desired collective behavior in a spatially inhomogeneous robotic swarm that emulates a system of chemically reacting molecules. Our approach is based on abstracting the swarm to an advectiondiffusion-reaction partial differential equation model, which we solve numerically using smoothed particle hydrodynamics (SPH), a meshfree technique that is suitable for advectiondominated systems. The parameters of the macroscopic model are mapped onto the deterministic and random components of individual robot motion and the probabilities that determine stochastic robot task transitions. For very large swarms that are prohibitively expensive to simulate, the macroscopic model, which is independent of the population size, is a useful tool for synthesizing robot control policies with guarantees on performance in a top-down fashion. We illustrate our methodology by formulating a model of rabbiteye blueberry pollination by a swarm of robotic bees and using the macroscopic model to select control policies for efficient pollination.

The research goal of collective construction is to develop systems in which large numbers of autonomous robots build large-scale structures according to desired specifications. We present algorithms for TERMES, a multi-robot construction system inspired by the building activities of termites. The system takes as input a high-level representation of a desired structure, and provides rules for an arbitrary number of simple climbing robots to build that structure, using passive solid building blocks under conditions of gravity. These rules are decentralized, rely on local information and implicit coordination, and provably guarantee correct completion of the target structure. Robots build staircases of blocks (potentially removable as temporary scaffolds) that they can climb to build structures much larger than themselves.

A collective of robots can together complete a task that is beyond the capabilities of any of its individual robots. One property of a robotic collective that allows it to complete such a task is the shape of the collective. This paper presents Kilobot, a simple modular robot designed to work in a collective to self-assemble and self-heal that collective’s shape. In previous work, an algorithm is given that allows a simulated collective of robots to self-assemble and self-heal a desired shape, keeping the shape sized proportional to the number of robots in the collective. In this abstract, the current work of producing a robotic collective that can demonstrate that algorithm is presented.

Large collections of robots have the potential to perform tasks collectively using distributed control algorithms. These algorithms require communication between robots to allow the robots to coordinate their behavior and act as a collective. In this paper we describe two algorithms which allow coordination between robots, but do not require physical environment marks such as pheromones. Instead, these algorithms rely on simple, local, low bandwidth, direct communication between robots. We describe the algorithms and measure their performance in worlds with and without obstacles.

Modular robots can potentially assemble into a wide range of configurations to locomote in different environments. However, designing locomotion strategies for each configuration is often tedious and has generally relied on a priori known connection geometry. Here we present a framework for 2D modular robots made of square modules assembled with arbitrary geometry, which achieve collective and directed locomotion with no centralized controller. Individual modules communicate locally and provably achieve consensus in coordinating movement in a common travel direction. In experiments with simulations and hardware prototypes, we show that robots achieve effective locomotion, irrespective of the number of modules and their connectivity which can be highly asymmetric

Decentralized agent groups typically require complex mechanisms to accomplish coordinated tasks. In contrast, biological systems can achieve intelligent group behaviors with each agent performing simple sensing and actions. We summarize our recent papers on a biologically-inspired control framework for multi-agent tasks that is based on a simple and iterative control law. We theoretically analyze important aspects of this decentralized approach, such as the convergence and scalability, and further demonstrate how this approach applies to real-world applications with a diverse set of multi-agent applications. These results provide a deeper understanding of the contrast between centralized and decentralized algorithms in multi-agent tasks and autonomous robot control.