A computer-implemented method of sensor planning for acquiring samples via an apparatus including one or more sensors, the computer-implemented method comprising: defining, by one or more computing devices, an area of interest; identifying, by the one or more computing devices, one or more sensing parameters for the one or more sensors; determining, by the one or more computing devices, one or more sampling combinations for acquiring a plurality of samples of the area of interest by the one or more sensors based at least in part on the one or more sensing parameters, comprising: determining, by one or more computing devices, a combination of overlap exponentials based at least on a reinforcement learning algorithm, wherein an overlap exponential is a factor by which overlap between the sample and a previous sample is encouraged; calculating, by one or more computing devices, a score function for the one or more sampling combinations based at least on a total area covered by the one or more sensors, an overlap perimeter, and the combination of overlap exponentials, wherein an overlap perimeter is a quantity of the sample that is redundant relative to a previous sample; and selecting, by one or more computing devices, the sampling combination corresponding to a maximum score function; and providing, by the one or more computing devices, one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination. defining, by one or more computing devices, an area of interest; identifying, by the one or more computing devices, one or more sensing parameters for the one or more sensors; determining, by the one or more computing devices, one or more sampling combinations for acquiring a plurality of samples of the area of interest by the one or more sensors based at least in part on the one or more sensing parameters, comprising: determining, by one or more computing devices, a combination of overlap exponentials based at least on a reinforcement learning algorithm, wherein an overlap exponential is a factor by which overlap between the sample and a previous sample is encouraged; calculating, by one or more computing devices, a score function for the one or more sampling combinations based at least on a total area covered by the one or more sensors, an overlap perimeter, and the combination of overlap exponentials, wherein an overlap perimeter is a quantity of the sample that is redundant relative to a previous sample; and selecting, by one or more computing devices, the sampling combination corresponding to a maximum score function; and providing, by the one or more computing devices, one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination. determining, by one or more computing devices, a combination of overlap exponentials based at least on a reinforcement learning algorithm, wherein an overlap exponential is a factor by which overlap between the sample and a previous sample is encouraged; calculating, by one or more computing devices, a score function for the one or more sampling combinations based at least on a total area covered by the one or more sensors, an overlap perimeter, and the combination of overlap exponentials, wherein an overlap perimeter is a quantity of the sample that is redundant relative to a previous sample; and selecting, by one or more computing devices, the sampling combination corresponding to a maximum score function; and providing, by the one or more computing devices, one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination.