Asset Protection in Escorting using Multi-Robot Systems

Swarm robotics is a field dedicated to the study of the design and development of certain multi-robot systems. Often times, these groups prove to be more beneficial than a single complex robot as swarms typically provide a more robust and potentially more efficient solution. One such case is the task of escorting a specified target while addressing any potential threats discovered in the environment. In this work, a control algorithm for a high volume, decentralized, homogeneous robot swarm was developed based upon a technique commonly used to model incompressible fluids known as Smoothed Particle Hydrodynamics (SPH). This proposed solution to the asset protection problem was tested against a more commonly accepted method for robot navigation known as potential fields. An alternate algorithm was developed based on this technique and manipulated to perform the same basic duty of asset protection. Both algorithms were tested in simulation using ARGoS as an environment and Swarmanoid’s Footbots as robot models. Five experiments were run in order to examine the functionality of both of these algorithms in relation to formation control and the protection of a mobile asset from mobile threats. The results proved the proposed SPH based algorithm comparable to the potential fields based method while minimizing the escape window and having a slightly higher response rate to introduced threats. These results hint that the concept of using fluid models for control of high volume swarms should further be explored and seriously considered as a potential solution to the asset protection problem

Brief Review on Formation Control of Swarm Robot

This paper presented review formation control ofswarm robot. Recently the problems formation control of swarmrobots has attracted much attention, and several formationcontrol schemes were proposed based on various strategies. Theformation control strategies to solved these problem on swarmrobots, with considering regulation concept in control theory.Swarm intelligence algorithms takes the full of advantages of thefeature of swarm robotics, and provides a great solution forproblem formation control on swarm robots

Quantifying Robotic Swarm Coverage

In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. This work fills that gap by introducing an error metric that provides a quantitative measure of coverage for use with any control scheme. The proposed error metric is continuously sensitive to changes in the swarm distribution, unlike commonly used discretization methods. We analyze the theoretical and computational properties of the error metric and propose two benchmarks to which error metric values can be compared. The first uses the realizable extrema of the error metric to compute the relative error of an observed swarm distribution. We also show that the error metric extrema can be used to help choose the swarm size and effective radius of each robot required to achieve a desired level of coverage. The second benchmark compares the observed distribution of error metric values to the probability density function of the error metric when robot positions are randomly sampled from the target distribution. We demonstrate the utility of this benchmark in assessing the performance of stochastic control algorithms. We prove that the error metric obeys a central limit theorem, develop a streamlined method for performing computations, and place the standard statistical tests used here on a firm theoretical footing. We provide rigorous theoretical development, computational methodologies, numerical examples, and MATLAB code for both benchmarks.Comment: To appear in Springer series Lecture Notes in Electrical Engineering (LNEE). This book contribution is an extension of our ICINCO 2018 conference paper arXiv:1806.02488. 27 pages, 8 figures, 2 table

Comparative Analysis Multi-Robot Formation Control Modeling Using Fuzzy Logic Type 2 – Particle Swarm Optimization

Multi-robot is a robotic system consisting of several robots that are interconnected and can communicate and collaborate with each other to complete a goal. With physical similarities, they have two controlled wheels and one free wheel that moves at the same speed. In this Problem, there is a main problem remaining in controlling the movement of the multi robot formation in searching the target. It occurs because the robots have to create dynamic geometric shapes towards the target. In its movement, it requires a control system in order to move the position as desired. For multi-robot movement formations, they have their own predetermined trajectories which are relatively constant in varying speeds and accelerations even in sudden stops. Based on these weaknesses, the robots must be able to avoid obstacles and reach the target. This research used Fuzzy Logic type 2 – Particle Swarm Optimization algorithm which was compared with Fuzzy Logic type 2 – Modified Particle Swarm Optimization and Fuzzy Logic type 2 – Dynamic Particle Swarm Optimization. Based on the experiments that had been carried out in each environment, it was found that Fuzzy Logic type 2 - Modified Particle Swarm Optimization had better iteration, time and resource and also smoother robot movement than Fuzzy Logic type 2 – Particle Swarm Optimization and Fuzzy Logic Type 2 - Dynamic Particle Swarm Optimization