3 research outputs found
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
Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes
When an emergency occurs within a building, it may be initially safer to send autonomous mobile nodes, instead of human responders, to explore the area and identify hazards and victims. Exploring all the area in the minimum amount of time and reporting back interesting findings to the human personnel outside the building is an essential part of rescue operations. Our assumptions are that the area map is unknown, there is no existing network infrastructure, long-range wireless communication is unreliable and nodes are not location-aware. We take into account these limitations, and propose an architecture consisting of both mobile nodes (robots, called agents) and stationary nodes (inexpensive smart devices, called tags). As agents enter the emergency area, they sprinkle tags within the space to label the environment with states. By reading and updating the state of the local tags, agents are able to coordinate indirectly with each other, without relying on direct agent-to-agent communication. In addition, tags wirelessly exchange local information with nearby tags to further assist agents in their exploration task. Our simulation results show that the proposed algorithm, which exploits both tag-to-tag and agent-to-tag communication, outperforms previous algorithms that rely only on agent-to-tag communication
Self–organised multi agent system for search and rescue operations
Autonomous multi-agent systems perform inadequately in time critical missions, while they tend to
explore exhaustively each location of the field in one phase with out selecting the pertinent strategy. This
research aims to solve this problem by introducing a hierarchy of exploration strategies. Agents explore
an unknown search terrain with complex topology in multiple predefined stages by performing pertinent
strategies depending on their previous observations. Exploration inside unknown, cluttered, and confined
environments is one of the main challenges for search and rescue robots inside collapsed buildings. In
this regard we introduce our novel exploration algorithm for multi–agent system, that is able to perform
a fast, fair, and thorough search as well as solving the multi–agent traffic congestion.
Our simulations have been performed on different test environments in which the complexity of the
search field has been defined by fractal dimension of Brownian movements. The exploration stages are
depicted as defined arenas of National Institute of Standard and Technology (NIST). NIST introduced
three scenarios of progressive difficulty: yellow, orange, and red. The main concentration of this research
is on the red arena with the least structure and most challenging parts to robot nimbleness