Robot swarm democracy: the importance of informed individuals against zealots

In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.

Decentralized Path Planning for Multi-Objective Robot Swarm System

The multi-robot path planning aims to explore a set of non-colliding paths with the shortest sum of lengths for multiple robots. The most popular approach is to artificially decompose the map into discrete small grids before applying heuristic algorithms. To solve the path planning in continuous environments, we propose a decentralized two-stage algorithm to solve the path-planning problem, where the obstacle and inter-robot collisions are both considered. In the first stage, an obstacle- avoidance path-planning problem is mathematically developed by minimizing the travel length of each robot. Specifically, the obstacle-avoidance trajectories are generated by approximating the obstacles as convex-concave constraints. In the second stage, with the given trajectories, we formulate a quadratic programming (QP) problem for velocity control using the control barrier and Lyapunov function (CBF-CLF). In this way, the multi-robot collision avoidance as well as time efficiency are satisfied by adapting the velocities of robots. In sharp contrast to the conventional heuristic methods, path length, smoothness and safety are fully considered by mathematically formulating the optimization problems in continuous environments. Extensive experiments as well as computer simulations are conducted to validate the effectiveness of the proposed path-planning algorithm.

Research on optimization of manned robot swarm scheduling based on ant-sparrow algorithm

Considering the optimization problem of manned robot swarm scheduling in public environment, we constructed a demand-time-space-energy consumption scheduling model taking passenger waiting time and robot swarm energy consumption as optimization goals. This paper proposes an ant-sparrow algorithm based on the same number constraints colonies of ant and sparrow, which combines the advantages of ant colony algorithm great initial solution and the fast convergence speed of the sparrow search algorithm. After a limited number of initial iterations, the ant colony algorithm is transferred to the sparrow search algorithm. In order to increase the diversity of feasible solutions in the later stage of the ant-sparrow algorithm iteration, a divide-and-conquer strategy is introduced to divide the feasible solution sequence into the same small modules and solve them step by step. Applying it to the manned robot swarm scheduling service in the public environment, experiments show that the ant-sparrow algorithm introduced with a divide-and-conquer strategy can effectively improve the quality and convergence speed of feasible solutions.

Towards an integrated automatic design process for robot swarms

Background: The specification of missions to be accomplished by a robot swarm has been rarely discussed in the literature: designers do not follow any standardized processes or use any tool to precisely define a mission that must be accomplished. Methods: In this paper, we introduce a fully integrated design process that starts with the specification of a mission to be accomplished and terminates with the deployment of the robots in the target environment. We introduce Swarm Mission Language (SML), a textual language that allows swarm designers to specify missions. Using model-driven engineering techniques, we define a process that automatically transforms a mission specified in SML into a configuration setup for an optimization-based design method. Upon completion, the output of the optimization-based design method is an instance of control software that is eventually deployed on real robots. Results: We demonstrate the fully integrated process we propose on three different missions. Conclusions: We aim to show that in order to create reliable, maintainable and verifiable robot swarms, swarm designers need to follow standardised automatic design processes that will facilitate the design of control software in all stages of the development.

When less is more: Robot swarms adapt better to changes with constrained communication

To effectively perform collective monitoring of dynamic environments, a robot swarm needs to adapt to changes by processing the latest information and discarding outdated beliefs. We show that in a swarm composed of robots relying on local sensing, adaptation is better achieved if the robots have a shorter rather than longer communication range. This result is in contrast with the widespread belief that more communication links always improve the information exchange on a network. We tasked robots with reaching agreement on the best option currently available in their operating environment. We propose a variety of behaviors composed of reactive rules to process environmental and social information. Our study focuses on simple behaviors based on the voter model—a well-known minimal protocol to regulate social interactions—that can be implemented in minimalistic machines. Although different from each other, all behaviors confirm the general result: The ability of the swarm to adapt improves when robots have fewer communication links. The average number of links per robot reduces when the individual communication range or the robot density decreases. The analysis of the swarm dynamics via mean-field models suggests that our results generalize to other systems based on the voter model. Model predictions are confirmed by results of multiagent simulations and experiments with 50 Kilobot robots. Limiting the communication to a local neighborhood is a cheap decentralized solution to allow robot swarms to adapt to previously unknown information that is locally observed by a minority of the robots.

The design of self-organizing human–swarm intelligence

Human–swarm interaction is a frontier in the realms of swarm robotics and human-factors engineering. However, no holistic theory has been explicitly formulated that can inform how humans and robot swarms should interact through an interface while considering real-world demands, the relative capabilities of the components, as well as the desired joint-system behaviours. In this article, we apply a holistic perspective that we refer to as joint human–swarm loops, that is, a cybernetic system made of human, swarm and interface. We argue that a solution for human–swarm interaction should make the joint human–swarm loop an intelligent system that balances between centralized and decentralized control. The swarm-amplified human is suggested as a possible design that combines perspectives from swarm robotics, human-factors engineering and theoretical neuroscience to produce such a joint human–swarm loop. Essentially, it states that the robot swarm should be integrated into the human’s low-level nervous system function. This requires modelling both the robot swarm and the biological nervous system as self-organizing systems. We discuss multiple design implications that follow from the swarm-amplified human, including a computational experiment that shows how the robot swarm itself can be a self-organizing interface based on minimal computational logic.