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reiniscimurs / IDLE-heuristic

A novel heuristics function for evaluating and selecting intermediate points in an autonomous exploration and mapping system where navigation is performed by a learned neural network.
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Integration of this Heuristic Function into GDAM_env.py #1

Open zahra370 opened 9 months ago

zahra370 commented 9 months ago

The heuristic function mentioned in the paper is designed to calculate the heuristic for each candidate point. However, in the provided code, fixed values are assumed for the goal and map information. I would like to inquire whether the origin value should vary for each candidate point.How can the heuristic function be incorporated into the GDAM_env.py ?

zahra370 commented 9 months ago

Apologies for any confusion I caused. I understand your IDLE code uses fixed origin and goal values, calculates d1 and d2 and I , distances in the heuristic function, and evaluates it for each point in a pre-defined map. However, I believe the paper suggests the heuristic should be calculated for each candidate point during the search, not just pre-defined points in the map. I would appreciate any suggestions you may have on integrating the IDLE heuristic within the GDAM framework.

reiniscimurs commented 9 months ago

The heuristic function is generally a callable function: https://github.com/reiniscimurs/IDLE-heuristic/blob/main/IDLE.py#L8-L21

You can calculate the value for every point "on-the-fly". All you need is to calculate the distance to origin and distance to goal, where the robot is always at the point of origin. These values can change over time and are not fixed.

Essentially, you would replace and integrate the aforementioned code in the heuristics function which is called on very evaluation of point value: https://github.com/reiniscimurs/GDAE/blob/main/Code/GDAM_env.py#L586-L592

Here, goal values are given and the distances should be calculated like: https://github.com/reiniscimurs/IDLE-heuristic/blob/main/IDLE.py#L62-L63

You simply would have to reca