Lost target search or Minimum Time Search (MTS) is an interesting theoretical and practical problem appeared after the II world war in the Operation Research field.
We have a lost target (e.g., a person in the sea) and we want to find it in the minimum time possible using sensing platforms such as air vehicles or sea vehicles. Thus, the question is the following:
Which actions have to perform the sensing agents to find the lost target?
This code shows the probabilistic and information theoretic greedy solution for solving the MTS.
Just a few lines of code enables any sensing platform to improve its autonomous behaviour. The solution assumes the following:
- The object searched is static
- The sensor in the vehicle is a range sensor
The intial knowledge is a rough estimation of the place that the target will be (defined by a gaussian distribution).
This is a simplified version of the discrete Minimum Time Search (MTS) solution using probabilistic cost functions Implemented from:
Lanillos, P. (2013): Minimum time search of moving targets in uncertain environments. Ph.D. Dissertation, Universidad Complutense de Madrid.
To run it simply execute the function.
bug fix: any size grid visualization
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