Upon mapping swarm robots search to particle swarm optimization (PSO), we extend PSO algorithm to model and control swarm robots for target search at an abstract level. At first, we analytically compare the characteristics of different versions of PSO to facilitate design of robots control algorithm by tailoring and transferring parallel asynchronous property of PSO into case of swarm search. N...

One of the main challenges in human-swarm interactions is the construction of suitable abstractions that make an entire robot team amenable to human control. For such abstractions to be useful, they need to scale gracefully as the number of robots increases. In this work, we consider the use of time-varying density functions to externally influence a robot swarm. Density functions abstract away...

An alternative to deploying a single robot of high complexity can be to utilize robot swarms comprising large numbers of identical, and much simpler, robots. Such swarms have been shown to be adaptable, fault-tolerant and widely applicable. However, designing individual robot algorithms to ensure effective and correct overall swarm behaviour is actually very difficult. While mechanisms for asse...

In This study, a new artificial fish-swarm optimization, to improve the foraging behavior of artificial fish swarm algorithm is closer to reality in order to let the fish foraging behavior, increase a look at the link (search) ambient, after examining environment, artificial fish can get more status information of the surrounding environment. Artificial fish screened from the information obtain...

While the robot is inmotion, path planning should follow the three aspects: (1) acquire the knowledge from its environmental conditions. (2) determine its position in the environment and (3) decision-making and execution to achieve its highest-order goals. The present research work aims to develop an efficient particle swarm optimizationbased path planner of an autonomous mobile robot. In this ...

This paper addresses the issue of swarm robots cooperative search. A swarm intelligence based algorithm, mechanical Particle Swarm Optimization (PSO), is first conducted which takes into account the robot mechanical properties and guiding the robots searching for a target. In order to avoid the robot localization and to avoid noise due to feedback and measurements, a new scheme which uses Extre...

A potential-based genetic algorithm is proposed for the motion planning of robot swarms. The proposed algorithm consists of a global path planner and a motion planner. The global path planning algorithm plans a trajectory, which the robot swarm should follow, within a Voronoi diagram of the free space. The motion planning algorithm is a genetic algorithm based on artificial potential models. Th...

This paper presents a new approach for cooperative search of a robot swarm. After modeling the robot, the mechanical Particle Swarm Optimization method is conducted based on physical robot properties. Benefiting from the effective localization and navigation by sensor data fusion, a mixed robot swarm which contains both simulated and real robots is then successfully used for searching a target ...

The reality gap is the discrepancy between simulation and reality—the same behavioural algorithm results in different robot swarm behaviours in simulation and in reality (with real robots). In this paper, we study the effect of the reality gap on the psychophysiological reactions of humans interacting with a robot swarm. We compare the psychophysiological reactions of 28 participants interactin...

Robotic swarms that take inspiration from nature are becoming a fascinating topic for multi-robot researchers. The aim is to control a large number of simple robots in order to solve common complex tasks. Due to the hardware complexities and cost of robot platforms, current research in swarm robotics is mostly performed by simulation software. The simulation of large numbers of these robots in ...