A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots
Abstract
:1. Introduction
- We propose a communication network to organize a swarm of underwater robots using indirect communication. The network consists of a set of underwater communication nodes. There are various underwater navigation methods—such as Terrain-Referenced Navigation (TRN) [23], Database-Referenced Navigation (DBRN) [24] and Gravity Aided Navigation (GAN) [25]—for an underwater robot to periodically visit the nodes to exchange information and charge batteries if needed.
- We apply a pheromone-based controller to coordinate a swarm to monitor marine environment and search for static targets on the seafloor. The controller is composed of two layers: the layer of virtual pheromone and the layer of behavior laws. Virtual pheromone indicates the pheromone density in the area of interest (AOI). An algorithm is developed to map an AOI of a random shape to the virtual pheromone in the form of a matrix. Behavior laws are designed on top of the virtual pheromone, such that a swarm continuously monitors the environment. During the monitoring process, the swarm can also search for and report specific static targets, such as hazards or wreckage. Note that the controller is bio-inspired, and thus we do not prove the convergence of adopted algorithms.
- We introduce a swarm evolution scheme to improve the monitoring strategy by automatically adjusting the robots’ visiting period. Experimental results indicate that the choice of a visiting period affects a swarm’s performance. After adopting an evolution scheme, a swarm can achieve an acceptable performance by avoiding unfavorable cases.
2. Related Work
2.1. Underwater Communication
2.2. Pheromone-Inspired Robot Swarms
2.3. Comparison with Available Schemes
3. Problem Statement and Solution
3.1. Problem Statement and Underwater Robot Swarm with Indirect Communication
3.2. Virtual Pheromone-Based Controller
4. Pheromone Map
4.1. Mapping the AOI into the Pheromone Matrix
Algorithm 1 Mapping into P |
Input: The AOI: Output: The pheromone matrix:
|
4.2. Rules to Update Pheromone Matrix
Algorithm 2 Update pheromone matrix when visiting a communication node |
Input:, , Output:, ,
|
5. Environment Monitoring and Target Search
5.1. Behavior Law for Environment Monitoring and Target Search
5.2. The Relationship between and Performance
- Set initial values. For each robot, we assign a small initial value to . We also set , and .
- When a robot visits the communication network the time, calculate and update
5.3. Improve Performance by Using Global Information
Algorithm 3 Compress P into |
Input:P, , Output:
|
Algorithm 4 Get the next cell to visit according to pheromone matrix |
Input:, Output:
|
- Check local matrix and go to a random cell whose pheromone density is 0.
- If no cell in equals 0, move to the cell with the lowest element in .
6. Simulation and Real-World Experiment
6.1. Simulation
6.2. Real-World Experiment
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Marker | Description |
---|---|
(1) | Monitor the environment and search for a period of |
(2) | Reach the nearest communication node |
(3) | Finish exchanging data with the communication node |
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Li, G.; Chen, C.; Geng, C.; Li, M.; Xu, H.; Lin, Y. A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots. Sensors 2019, 19, 4089. https://doi.org/10.3390/s19194089
Li G, Chen C, Geng C, Li M, Xu H, Lin Y. A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots. Sensors. 2019; 19(19):4089. https://doi.org/10.3390/s19194089
Chicago/Turabian StyleLi, Guannan, Chao Chen, Chao Geng, Meng Li, Hongli Xu, and Yang Lin. 2019. "A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots" Sensors 19, no. 19: 4089. https://doi.org/10.3390/s19194089