A Modified Particle Swarm Optimization on Search Tasking

This paper proposes two extensions of Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO), respectively named as RPSO (Robotic PSO) and RDPSO (Robotic DPSO), so as to adapt these promising biologicalinspired techniques to the domain of multi-robot systems, by taking into account obstacle avoidance. These novel algorithms are demonstrated for groups of simulated robots performing a distributed exploration task. The concepts of social exclusion and social inclusion are used in the RDPSO algorithm as a "punish-reward" mechanism enhancing the ability to escape from local optima. Experimental results obtained in a simulated environment show that biological and sociological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g. search and rescue).

The 2010 IEEE International Conference on Information and Automation, 2010

As one of valid modeling and coordinated controlling tools, the particle swarm optimization algorithm can be extended for applying to task of swarm robotic search. To gain an insight into effects of key parameters on distributed swarm search, a series of simulations are conducted. In such control algorithm, main parameters including communication range, detection radius, and swarm size are paid close attentions. Similar to ideal "particles", swarm robots are modeled at an abstract level with the extended particle swarm optimization method. Also, individual robots are designed to be controlled under three-state finite state machine mechanism having SingleSearch, SwarmSearch, and Declaration states. Then, control strategy and algorithm are developed. The statistical results from simulating data indicate the validity of modeling approach. Further, the effects of three parameters on overhead and efficiency of system running come out in compared analysis both of different size swarms and of specific size swarm.

Archives of Computational Methods in Engineering

Swarm Intelligence (SI) is one of the research fields that has continuously attracted researcher attention in these last two decades. The flexibility and a well-known decentralized collective behavior of its algorithm make SI a suitable candidate to be implemented in the swarm robotics domain for real-world optimization problems such as target search tasks. Since the introduction of Particle Swarm Optimization (PSO) as a representation of the SI algorithm, it has been widely accepted and utilized especially in local and global search strategies. Because of its simplicity, effectiveness, and low computational cost, PSO has retained popularity notably in the swarm robotics domain, and many improvements have been proposed. Target search problems are one of the areas that have been continuously solved by PSO. This article set out to analyze and give the inside view of the existing literature on PSO strategies towards target search problems. Based on the procedure of PRISMA Statement review method, a systematic review identified 51 related research studies. After further analysis of these total 51 selected articles and consideration on the PSO components, target search components, and research field components, resulting in nine main elements related to the discussed topic. The elements are PSO variant, application field, PSO inertial weight function, PSO efficiency improvement, PSO termination criteria, target available, target mobility status, experiment framework, and environment complexity. Several recommendations, opinions, and perfectives on the discussed topic are presented. Finally, recommendations for future research in this domain are represented to support future developments.

Neurocomputing, 2016

In this paper, we study the problem of multi-robot target searching in unknown environments. For target searching, robots need an efficient method with respect to their limitations and characteristics of the workspace. Every robotic search algorithm has several constraints. Our goal is to propose a distributed algorithm based on Particle Swarm Optimization (PSO) for target searching which satisfies the beforementioned constraints. This extension of PSO is named A-RPSO (Adaptive Robotic PSO). A-RPSO acts as the controlling mechanism for robots. It is similar to PSO with two modifications: firstly it takes into account obstacle avoidance, secondly A-RPSO has a mechanism to escape from local optima. Various experimental results obtained in a simulated environment, show that A-RPSO is able to outperform other state of-theart techniques in target searching problems. The performance of A-RPSO is much more significant compared with other approaches in two distinctive states particularly: large environments and small number of robots.

IEEE Access, 2019

Applying swarm intelligence to actual swarm robotic systems is a challenging task especially with adequately consideration of corresponding practical constraints. Under the restrictions of the fieldof-view limited relative positioning, local sensing and communication, kinematic limitations as well as anti-collision issues, this paper presents a constrained particle swarm optimization (PSO) based collaborative searching method for robotic swarms. Besides, the proposed method follows the concept of evolution speed and a modified aggregation degree to determine the adaptive weights in the robotic PSO model. The modified aggregation degree is associated with the number of members in one's field-of-view. Unlike the traditional position update method, the proposed method updates the forward speed and angular velocity of the robot using the non-holonomic model to realize the motion control of each robot. The simulation results show that the proposed method has the potential for the practical implementation of collaborative searching tasks for robotic swarms in different types of environments.

2009 2nd Conference on Human System Interactions, 2009

Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots. This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The approach was developed and analyzed on multiple robot single and multiple target search. The approach was further enhanced by the multi-robot-multi-target search in noisy environments. The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target.

2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018

Multimedia Tools and Applications, 2021

Cooperation and synchronization of multi-robots is a major concern in robotics research field. Two autonomous robots are assumed to carry a stick and called as the twin robots. Different types of Particle Swarm Optimization (PSO) are analyzed for stick carrying task and a brief review of extension and enhancement of PSO is done to identify the parameters used. Path planning of twin robot is done with variants of PSO. Performance of each variant-applied twin is evaluated based on several parameters. These parameters are execution time, number of steps, number of turns, path travelled and path deviated. Fitness value of each twin is calculated in each algorithm to obtain the next position along the solution path. All the algorithms are executed and the pixels are plotted to represent the twin’s trajectory and the performance of PSO variants compared with Artificial Bee Colony Optimization (ABCO) and differential Evolutionary (DE) algorithm. It is observed that PSO variants outperforms...

One of the most widely used biomimicry algorithms is the Particle Swarm Optimization (PSO). Since its introduction in 1995, it has caught the attention of both researchers and academicians as a way of solving various optimization problems, such as in the fields of engineering and medicine, to computer image processing and mission critical operations. PSO has been widely applied in the field of swarm robotics, however, the trend of creating a new variant PSO for each swarm robotic project is alarming. We investigate the basic properties of PSO algorithms relevant to the implementation of swarm robotics and characterize the limitations that promote this trend to manifest. Experiments were conducted to investigate the convergence properties of three PSO variants (original PSO, SPSO and APSO) and the global optimum and local optimal of these PSO algorithms were determined. We were able to validate the existence of premature convergence in these PSO variants by comparing 16 functions implemented alongside the PSO variant. This highlighted the fundamental flaws in most variant PSOs, and signifies the importance of developing a more generalized PSO algorithm to support the implementation of swarm robotics. This is critical in curbing the influx of custom PSO and theoretically addresses the fundamental flaws of the existing PSO algorithm. ACM Reference Format: Dada Emmanuel Gbenga and Effirul Ikhwan Ramlan. 2016. Understanding the limitations of particle swarm algorithm for dynamic optimization tasks: A survey towards the singularity of PSO for swarm robotic applications.

Computer Engineering and Applications Journal, 2017

In this reseach, a PSO method is impelemented in searching a gas leakage. A swarm robot consisted of 3 agents, yelow, blue, and green, was used. The research was done in 2 type of experiments, i.e. in simulation and real expeiment. A Matlab is used as a simulation validation while for the real experiment, a 2 x 2 m arena is used. From the experiment, it can be concluded that a good performance of a swam can be achieved using PSO method.