scholarly journals Grid Map Merging with Ant Colony Optimization for Multi-Robot Systems

2021 ◽  
Author(s):  
Heoncheol Lee
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Working Environment ◽  
Grid Map ◽  
Localization And Mapping ◽  
Robot Systems ◽  
The Individual ◽  
Merging Algorithms ◽  
Multi Robot ◽  
Map Merging

Multi-robot systems have recently been in the spotlight in terms of efficiency in performing tasks. However, if there is no map in the working environment, each robot must perform SLAM which simultaneously performs localization and mapping the surrounding environments. To operate the multi-robot systems efficiently, the individual maps should be accurately merged into a collective map. If the initial correspondences among the robots are unknown or uncertain, the map merging task becomes challenging. This chapter presents a new approach to accurately conducting grid map merging with the Ant Colony Optimization (ACO) which is one of the well-known sampling-based optimization algorithms. The presented method was tested with one of the existing grid map merging algorithms and showed that the accuracy of grid map merging was improved by the ACO.

scholarly journals A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6988
Author(s):  
Shuien Yu ◽  
Chunyun Fu ◽  
Amirali K. Gostar ◽  
Minghui Hu
Keyword(s):  
Multiple Robots ◽  
Working Mechanism ◽  
Topological Maps ◽  
Occupancy Grid ◽  
Localization And Mapping ◽  
Robot Systems ◽  
Feature Based ◽  
Occupancy Grid Maps ◽  
Multi Robot ◽  
Map Merging

When multiple robots are involved in the process of simultaneous localization and mapping (SLAM), a global map should be constructed by merging the local maps built by individual robots, so as to provide a better representation of the environment. Hence, the map-merging methods play a crucial rule in multi-robot systems and determine the performance of multi-robot SLAM. This paper looks into the key problem of map merging for multiple-ground-robot SLAM and reviews the typical map-merging methods for several important types of maps in SLAM applications: occupancy grid maps, feature-based maps, and topological maps. These map-merging approaches are classified based on their working mechanism or the type of features they deal with. The concepts and characteristics of these map-merging methods are elaborated in this review. The contents summarized in this paper provide insights and guidance for future multiple-ground-robot SLAM solutions.

scholarly journals Tomographic Feature-Based Map Merging for Multi-Robot Systems

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 107 ◽  
Author(s):  
Heoncheol Lee
Keyword(s):  
Gaussian Mixture Models ◽  
Search Space ◽  
Gaussian Mixture ◽  
Transformation Matrix ◽  
Robot Systems ◽  
Feature Based ◽  
The Individual ◽  
Merging Method ◽  
Multi Robot ◽  
Map Merging

Multi-robot systems require collective map information on surrounding environments to efficiently cooperate with one another on assigned tasks. This paper addresses the problem of grid map merging to obtain the collective map information in multi-robot systems with unknown initial poses. If inter-robot measurements are not available, the only way to merge the maps is to find and match the overlapping area between maps. This paper proposes a tomographic feature-based map merging method, which can be successfully conducted with relatively small overlapping areas. The first part of the proposed method is to estimate a map transformation matrix using the Radon transform which can extract tomographically salient features from individual grid maps. The second part is to determine the search space using Gaussian mixture models based on the estimated map transformation matrix. The final part is to optimize an objective function modeled from tomographic information within the determined search space. Evaluation results with various pairs of individual maps produced by simulations and experiments showed that the proposed method can merge the individual maps more accurately than other map merging methods.

A Multi-Robot System Using Mobile Agents with Ant Colony Clustering

2010 ◽  
pp. 174-192
Author(s):  
Yasushi Kambayashi ◽  
Yasuhiro Tsujimura ◽  
Hidemi Yamachi ◽  
Munehiro Takimoto
Keyword(s):  
Communication Networks ◽  
Mobile Agents ◽  
Software Agents ◽  
Ant Colony ◽  
Multiple Robots ◽  
Robot System ◽  
Mobile Software ◽  
Robot Systems ◽  
Ant Colony Clustering ◽  
Multi Robot

This chapter presents a framework using novel methods for controlling mobile multiple robots directed by mobile agents on a communication networks. Instead of physical movement of multiple robots, mobile software agents migrate from one robot to another so that the robots more efficiently complete their task. In some applications, it is desirable that multiple robots draw themselves together automatically. In order to avoid excessive energy consumption, we employ mobile software agents to locate robots scattered in a field, and cause them to autonomously determine their moving behaviors by using a clustering algorithm based on the Ant Colony Optimization (ACO) method. ACO is the swarm-intelligence-based method that exploits artificial stigmergy for the solution of combinatorial optimization problems. Preliminary experiments have provided a favorable result. Even though there is much room to improve the collaboration of multiple agents and ACO, the current results suggest a promising direction for the design of control mechanisms for multi-robot systems. In this chapter, we focus on the implementation of the controlling mechanism of the multi-robot system using mobile agents.

scholarly journals Swarm SLAM: Challenges and Perspectives

2021 ◽  
Vol 8 ◽  
Author(s):  
Miquel Kegeleirs ◽  
Giorgio Grisetti ◽  
Mauro Birattari
Keyword(s):  
Fault Tolerant ◽  
Point Of View ◽  
Decentralized System ◽  
Robot Swarm ◽  
Cost Constraints ◽  
Localization And Mapping ◽  
Robot Systems ◽  
The Moment ◽  
Hostile Environments ◽  
Multi Robot

A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints.

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