Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a simple, reactive method for multiple robots carrying out sequential low-visibility navigation in the presence of an observer. Initially, the robots have no map of the environment but know the locations of the observer and goal. They generate an occupancy grid representation of the environment which is modeled using potential fields with embedded task information. These fields are combined and navigation waypoints extracted. Each robot carries out its traverse independently and shares its experience with its successor. The experience information consists of the occupancy grid and a filtered version of the traveled path used to assist the subsequent robot to traverse a lor visibility path. This produces a robust and reactive solution for stealthy navigation since there is no global path planning and the robots are not committed to any particular path. Experiments in simulation and real outdoor environments substantiate the approach and demonstrate the benefits of sharing information in reducing cumulative visibility. The experiments also demonstrate the algorithm's versatility in taking advantage of an environment that changes beten robot traverses.
['Ashley Tews', 'Gaurav S. Sukhatme', 'Maja J. Matarić']
I am writing a research paper on a idea A multi-robot approach to stealthy navigation in the presence of an observer
[{"from": "human", "value": "I am writing a research paper on a idea A multi-robot approach to stealthy navigation in the presence of an observer"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a simple, reactive method for multiple robots carrying out sequential low-visibility navigation in the presence of an observer. Initially, the robots have no map of the environment but know the locations of the observer and goal. They generate an occupancy grid representation of the environment which is modeled using potential fields with embedded task information. These fields are combined and navigation waypoints extracted. Each robot carries out its traverse independently and shares its experience with its successor. The experience information consists of the occupancy grid and a filtered version of the traveled path used to assist the subsequent robot to traverse a lor visibility path. This produces a robust and reactive solution for stealthy navigation since there is no global path planning and the robots are not committed to any particular path. Experiments in simulation and real outdoor environments substantiate the approach and demonstrate the benefits of sharing information in reducing cumulative visibility. The experiments also demonstrate the algorithm's versatility in taking advantage of an environment that changes beten robot traverses."}]