Biologically Inspired Multi-Robot Foraging (Demonstration)
نویسندگان
چکیده
In this demonstration we illustrate the direct usage of the principles of bee-inspired coordination in swarm intelligence on multi-robot systems. We present the first results of this implementation, where a subset of the bee algorithms are implemented on multiple turtlebot robots with the goal to simulate a food foraging application. For this we implemented means for locally detecting the location, speed and direction of the other robots using visual markers, applying collision avoidance algorithms and simulating local communication over wi-fi.
منابع مشابه
Biologically inspired confinement of multi-robot systems
Confinement of a group of mobile robots is of significant interest to the multi-agent robotics community. We develop confinement strategies through simple biological models; in particular, we draw inspiration from the foraging techniques used by bottlenose dolphins to catch fish. For a multi-agent system, we achieve the following goals: 1 provide an algorithm for one group of agents to perpetua...
متن کاملBee-inspired foraging in an embodied swarm (Demonstration)
We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation ...
متن کاملEvolving Error Tolerance in Biologically-Inspired iAnt Robots
Evolutionary algorithms can adapt the behavior of individuals to maximize the fitness of cooperative multi-agent teams. We use a genetic algorithm (GA) to optimize behavior in a team of simulated robots that mimic foraging ants, then transfer the evolved behaviors into physical iAnt robots. We introduce positional and resource detection error models into our simulation to characterize the empir...
متن کاملMO-LOST: adaptive ant trail untangling in multi-objective multi-colony robot foraging
In the context of large-population multi-objective robot foraging, we present a novel ant-inspired trail-following algorithm that is able to adaptively untangle multiple trails. The emergent result is often a set of short, non-intersecting trails that produce good system throughput due a good trade off between the dual goals of minimizing travel distance and spatial interference. Empirical simu...
متن کاملMaximum Sustainable Yield Problem for Robot Foraging and Construction System
We introduce the Maximum Sustainable Yield problem for a multi-robot foraging and construction system, inspired by the relationship between the natural resource growth and harvesting behaviors in an ecosystem. The resources spawn according to the logistic model and are vulnerable to overharvesting. The robots must maintain sustainability while maximizing productivity. The foraging robots harves...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014