3 research outputs found
Swarm robotics: Cooperative navigation in unknown environments
Swarm Robotics is garnering attention in the robotics field due to its substantial benefits. It has been proven to outperform most other robotic approaches in many applications such as military, space exploration and disaster search and rescue missions. It is inspired by the behavior of swarms of social insects such as ants and bees. It consists of a number of robots with limited capabilities and restricted local sensing. When deployed, individual robots behave according to local sensing until the emergence of a global behavior where they, as a swarm, can accomplish missions individuals cannot. In this research, we propose a novel exploration and navigation method based on a combination of Probabilistic Finite Sate Machine (PFSM), Robotic Darwinian Particle Swarm Optimization (RDPSO) and Depth First Search (DFS). We use V-REP Simulator to test our approach. We are also implementing our own cost effective swarm robot platform, AntBOT, as a proof of concept for future experimentation. We prove that our proposed method will yield excellent navigation solution in optimal time when compared to methods using either PFSM only or RDPSO only. In fact, our method is proved to produce 40% more success rate along with an exploration speed of 1.4x other methods. After exploration, robots can navigate the environment forming a Mobile Ad-hoc Network (MANET) and using the graph of robots as network nodes
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Distributed intelligent systems for a swarm of robots
Area exploration is a task where a robot tries to gain information about an unknown environment. Exploring an unknown area is a challenging task for a group of robots as no pre-made map exists, leading to setting a suitable swarm formation compatible with the area to be explored. Having a suitable swarm formation allows the swarm to preserve the overall exploration time, by distributing sub-tasks for each robot, and collecting relevant data. Current swarm formations such as biologically inspired formations or Probabilistic RoadMap (PRM) tend to have a fixed shape, where robots are positioned in a fixed location point within the swarm, preventing the swarm from adjusting its formation to adapt to the unknown area, thus, are not suitable to explore unknown areas. One needs a more flexible formation, where each robot can change its position within the swarm. Consequently, this research aims to build a distributed robotic swarm formation using fractals.
Fractals have the properties of self-similarity, allowing for an equal distribution of the robots, and recursiveness, allowing for a gradual expansion of a swarm formation. Utilising the properties of fractals allow for a robotic swarm to develop a fractal as a swarm formation. Additionally, changing the parameters of each fractal formation, such as a number of branches, will provide the swarm with the flexibility to adjust the fractal formation and to continue exploring an unknown area. In order to determine both advantages and disadvantages of using fractals as a swarm formation, the first step is to classify each selected fractal into either a line or curve-based formation class to distinguish the similarities and differences in each fractal’s behaviour. The second step is to implement the growth rule of each fractal formation using robots to explore an unknown area. The last step is to study the effect of changing the parameters of the of implemented fractal formations toward exploring unknown areas.
The research’s outcome shows that using fractals as a swarm formation achieved near the amount of area covered by a traditional exploration method, such as PRM, with 88% less use of robots. Furthermore, fractal formations balances between the number of robots used, and the amount of area covered as each fractal uses only the robots needed to develop specific iterations. The effect of changing the parameters of a fractal formation increases the chance of covering more areas
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A review and implementation of swarm pattern formation and transformation models
Purpose: The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need for mathematical foundations in swarm systems. Design/methodology/approach: The work is separated out as inspirations, applications, definitions, challenges and classifications of pattern formation in swarm systems based on recent literature. Further, the work proposes a mathematical model for swarm pattern formation and transformation. Findings: A swarm pattern formation model based on mathematical foundations and macroscopic primitives is proposed. A formal definition for swarm pattern transformation and four special cases of transformation are introduced. Two general methods for transforming patterns are investigated and a comparison of the two methods is presented. The validity of the proposed models, and the feasibility of the methods investigated are confirmed on the Traer Physics and Processing environment. Originality/value: This paper helps in understanding the limitations of existing research in pattern formation and the lack of mathematical foundations for swarm systems. The mathematical model and transformation methods introduce two key concepts, namely macroscopic primitives and a mathematical model. The exercise of implementing the proposed models on physics simulator is novel