Koordinacija više robota za učinkovite pretraživanje prostora

This paper addresses the problem of exploration of an unknown environment by developing effective exploration strategies for a team of mobile robots equipped with continuously rotating 3D scanners. The main aim of the new strategies is to reduce the exploration time of unknown environment. Unlike most of other published works, to save time, the laser scanners rotate and scan the environment while robots are in motion. Furthermore, the new strategies are able to explore large outdoor environments as a considerable reduction of the required computations, especially those required for path planning, have been achieved. Moreover, another new exploration strategy has been developed so that robots continuously replan the order to visit the remaining unexplored areas according to the new data (i.e. updated map) collected by the robot in question or by the other team members. This new extension led to further enhancements over the above mentioned ones, but with slightly higher computational costs. Finally, to assess our new exploration strategies with different levels of environment complexity, new set of experiments were conducted in environments where obstacles are distributed according to the Hilbert curve. The results of these experiments show the effectiveness of the proposed technique to effectively distribute the robots over the environment. More importantly, we show how the optimal number of robots is related to the environment complexity.Ovaj članak istražuje problem pretraživanja nepoznatog prostora razvijanjem učinkovite strategije za tim mobilnih robota s rotirajućim 3D laserskim senzorom. Glavni cilj ove nove strategije je smanjenje vremena pretraživanja nepoznatog prostora. Za razliku od većine objavljenih radova, u ovome članku, radi smanjenja vremena, laserski senzori rotiraju i snimaju prostor dok su roboti još u pokretu. Predložene strategije, pošto se njima znatno smanjuje računska složenost, pogotovo za planiranje gibanja, omogućuju pretraživanje i vanjskih prostora prostora velikih dimenzija. Nadalje, razvijena je još jedna strategija pretraživanja koja omogućuje robotima da kontinuirano replaniraju poredak kojim će posjetiti ostatak neistraženog prostora, prema novim podacima (ažuriranoj karti) prikupljenim od njih samih ili drugih članova tima. Ovo novo proširenje nadalje unaprjeđuje performanse algoritma, ali uz nešto veću računsku složenost. Kako bi se u konačnici testirale nove strategije pretraživanja na prostorima različite složenosti, provedeni su eksperimenti s preprekama raspoređenim po Hilbertovoj krivulji. Rezultati eksperimenata pokazuju učinkovitost predložene metode u prostornom raspoređivanju robota. Od posebne je važnosti istaknuti da se u članku također istražuje odnos između broja robota i kompleksnosti prostora

Collective cluster-based map merging in multi robot SLAM

New challenges arise with multi-robotics, while information integration is among the most important problems need to be solved in this field. For mobile robots, information integration usually refers to map merging . Map merging is the process of combining partial maps constructed by individual robots in order to build a global map of the environment. Different approaches have been made toward solving map merging problem. Our method is based on transformational approach, in which the idea is to find regions of overlap between local maps and fuse them together using a set of transformations and similarity heuristic algorithms. The contribution of this work is an improvement made in the search space of candidate transformations. This was achieved by enforcing pair-wise partial localization technique over the local maps prior to any attempt to transform them. The experimental results show a noticeable improvement (15-20%) made in the overall mapping time using our technique

Energy-Efficient Indoor Search by Swarms of Simulated Flying Robots Without Global Information

Swarms of flying robots are a promising alternative to ground-based robots for search in indoor environments with advantages such as increased speed and the ability to fly above obstacles. However, there are numerous problems that must be surmounted including limitations in available sensory and on-board processing capabilities, and low flight endurance. This paper introduces a novel strategy to coordinate a swarm of flying robots for indoor exploration that significantly increases energy efficiency. The presented algorithm is fully distributed and scalable. It relies solely on local sensing and low-bandwidth communication, and does not require absolute positioning, localisation, or explicit world-models. It assumes that flying robots can temporarily attach to the ceiling, or land on the ground for efficient surveillance over extended periods of time. To further reduce energy consumption, the swarm is incrementally deployed by launching one robot at a time. Extensive simulation experiments demonstrate that increasing the time between consecutive robot launches significantly lowers energy consumption by reducing total swarm flight time, while also decreasing collision probability. As a trade-off, however, the search time increases with increased inter-launch periods. These effects are stronger in more complex environments. The proposed localisation-free strategy provides an energy efficient search behaviour adaptable to different environments or timing constraints

The multi-agent flood algorithm as an autonomous system for search and rescue applications

[no abstract

RFID-based exploration for large robot teams

Abstract — To coordinate a team of robots for exploration is a challenging problem, particularly in large areas as for example the devastated area after a disaster. This problem can generally be decomposed into task assignment and multi-robot path planning. In this paper, we address both problems jointly. This is possible because we reduce significantly the size of the search space by utilizing RFID tags as coordination points. The exploration approach consists of two parts: a stand-alone distributed local search and a global monitoring process which can be used to restart the local search in more convenient locations. Our results show that the local exploration works for large robot teams, particularly if there are limited computational resources. Experiments with the global approach showed that the number of conflicts can be reduced, and that the global coordination mechanism increases significantly the explored area. I