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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Evolutionary optimization for risk-aware heterogeneous multi-agent path planning in uncertain environments.
Fatemeh Rekabi Bana1, Tomáš Krajník2, Farshad Arvin1
1Swarm and Computational Intelligence Laboratory (SwaCIL), Department of Computer Science, Durham University, Durham, United Kingdom.
Frontiers in Robotics and AI
|August 28, 2024
Summary
This study introduces a novel multi-agent path-planning approach for cooperative robots. The algorithm ensures safe, collision-free trajectories for teams exploring diverse environments, optimizing exploration and data collection.
Area of Science:
- Robotics
- Artificial Intelligence
- Control Systems
Background:
- Cooperative multi-agent systems enable miniature robots for data collection and interaction in various environments.
- Existing path-planning methods often struggle with collision avoidance and coordinated exploration for multiple agents.
Purpose of the Study:
- To propose a new multi-agent path-planning approach for generating collision-free trajectories.
- To enable cooperative robots to explore environments as a formation for efficient data collection and hazard detection.
Main Methods:
- Leveraging a risk-aware probabilistic roadmap algorithm for map generation.
- Employing node classification to define exploration regions.
- Utilizing a customized genetic algorithm for combinatorial optimization.
Main Results:
- The algorithm computes safe trajectories, minimizing travel distance and collision probability.
- It considers agents' dynamic behavior and environmental uncertainties.
- Performance is validated across different group sizes and benchmark scenarios.
Conclusions:
- The proposed optimization method demonstrates stable and convergent properties irrespective of group size.
- It facilitates coordinated exploration and reliable data acquisition for multi-agent systems.
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