Path-planning in 3D space using butterfly optimization algorithm

The Butterfly Optimization Algorithm is one of the most recent nature-inspired algorithms that mimic the butterflies' behavior in mating and finding food, for solving the global optimization problems. The algorithm utilizes the sense of butterflies of smelling for determining the location of nectar and find mates, which is based on the foraging strategy of those insects. This paper represents a method of using the BOA algorithm for solving the problem of path planning in three-dimensional space. The proposed method finds a path from a particular starting point to any chosen goal, where the generated final path is completely safe and collision-free. The algorithm is based on 3 phases: the initial phase, the iteration phase, and the final phase. The movement of butterflies is based on two search moves, one of them is Local random search; where the butterfly moves randomly within the swarm, and the other is Global search; where the butterfly moves towards the best-fitted butterfly in the current population. The proposed method in this paper is able to find a collision-free path from the start point to the goal in all of the presented test environments in proximately well performance and the results were computed in terms of execution time and path length

A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents

Pigeon Inspired Optimization (PIO) algorithm is gaining popularity since its development due to faster convergence ability with great efficiencies when compared with other bio-inspired algorithms. The navigation capability of homing pigeons has been precisely used in Pigeon Inspired Optimization algorithm and continuous advancement in existing algorithms is making it more suitable for complex optimization problems in various fields. The main theme of this survey paper is to introduce the basics of PIO along with technical advancements of PIO for the motion planning techniques of dynamic agents. The survey also comprises of findings and limitations of proposed work since its development to help the research scholar around the world for particular algorithm selection especially for motion planning. This survey might be extended up to application based in order to understand the importance of algorithm in future studies

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms

Swarm intelligence algorithms adaptation for various search spaces

U današnje vrijeme postoji mnogo algoritama inteligencije rojeva koji se uspiješno koriste za rešavanje raznih teških problema optimizacije. Zajednicki elementi svih ovih algoritama su operator za lokalnu pretragu (eksploataciju) oko prona enih obecavajucih rješenja i operator globalne pretrage (eksploracije) koji pomaže u bijegu iz lokalnih optimuma. Algoritmi inteligencije rojeva obicno se inicijalno testiraju na neogranicenim, ogranicenim ili visoko-dimenzionalnim skupovima standardnih test funkcija. Nadalje, mogu se poboljšati, prilagoditi, izmijeniti, hibridizirati, kombinirati s lokalnom pretragom. Konacna svrha je korištenje takve metaheuristike za optimizaciju problema iz stvarnog svijeta. Domeni rješenja odnosno prostori pretrage prakticnih teških problema optimizacije mogu biti razliciti. Rješenja mogu biti vektori iz skupa realnih brojeva, cijelih brojeva ali mogu biti i kompleksnije strukture. Algoritmi inteligencije rojeva moraju se prilagoditi za razlicite prostore pretrage što može biti jednostavno podešavanje parametera algoritma ili prilagodba za cjelobrojna rješenja jednostavnim zaokruživanjem dobivenih realnih rješenja ali za pojedine prostore pretrage potrebnao je skoro kompletno prepravljanja algoritma ukljucujuci i operatore ekploatacije i ekploracije zadržavajuci samo proces vo enja odnosno inteligenciju roja. U disertaciji je predstavljeno nekoliko algoritama inteligencije rojeva i njihova prilagodba za razlicite prostore pretrage i primjena na prakticne probleme. Ova disertacija ima za cilj analizirati i prilagoditi, u zavisnosti od funkcije cilja i prostora rješenja, algoritme inteligencije rojeva. Predmet disertacije ukljucuje sveobuhvatan pregled postojecih implementacija algoritama inteligencije rojeva. Disertacija tako er obuhvaca komparativnu analizu, prikaz slabosti i snaga jednih algoritama u odnosu na druge zajedno s istraživanjem prilagodbi algoritama inteligencije rojeva za razlicite prostore pretrage i njihova primjena na prakticne problem. Razmatrani su problemi sa realnim rješenjima kao što su optimizacija stroja potpornih vektora, grupiranje podataka, sa cijelobrojnim rješenjima kao što je slucaj problema segmentacije digitalnih slika i za probleme gdje su rješenja posebne strukture kao što su problemi planiranja putanje robota i triangulacije minimalne težine. Modificirani i prilago eni algoritmi inteligencije rojeva za razlicite prostore pretrage i primjenih na prakticne probleme testirani su na standardnim skupovima test podataka i uspore eni s drugim suvremenim metodama za rješavanje promatranih problema iz literature. Pokazane su uspješne prilagodbe algoritama inteligencije rojeva za razne prostore pretrage. Ovako prilago eni algoritmi su u svim slucajevima postigli bolje rezultate u usporedbi sa metodama iz literature, što dovodi do zakljucka da je moguce prilagoditi algoritme inteligencije rojeva za razne prostore pretrage ukljucujuci i kompleksne strukture i postici bolje rezultate u usporedbi sa metodama iz literature