DRAFT PROPOSAL | Underground Mines Robots Training Project
Added on 2022-08-15
2 Pages386 Words10 Views
DRAFT PROPOSAL
Introduction
To come up with a viable prototype for the proposed underground mines robots
training project, the programmer will make use of inbuilt python libraries as well as
customized classes to meet the project needs. From the provided project definition
requirements, it is seems viable to make use of classical algorithmic challenges as
well as designing some novel ones to come up with an algorithm that can be used by
the robots.
Data structures
Corridors and Sections: For the prototype, a python list of dictionaries data
structure will be used to keep track of the status of the given sectors. Each element
of the will contain information about that sector in form of a dictionary.
Robots: Information about the robot will be tracked with a specially designed class.
This class will contain required functions and member variables that keep track of
the information about each robot.
Classical algorithms
Scheduling algorithm: Considering that the robots are operating in a predetermined
schedule, it appears prudent to look at existing scheduling algorithms that will ensure
all the tasks are executed in the most optimum time possible and at reasonable time
and space complexities. Unless the size of the scheduling queue is very small, then
at this level the Shortest Job First algorithm will be used. In a later stage, after the
prototype has been deployed, then the Round Robin algorithm can be tested and
results compared to that of Shortest Job First. Deepening on the results between the
two, the most optimum algorithm will be taken up.
Searching algorithm: Given the fact that the project involves tracking the position of
the robots, in the event that searching of these position is required, then the
selection sort will be used while developing the prototype. Once the prototype
passes the MVP (minimum viable product) threshold, then the performance of the
search can be improved to one with better time and space complexity, either linear
search or binary search.
Introduction
To come up with a viable prototype for the proposed underground mines robots
training project, the programmer will make use of inbuilt python libraries as well as
customized classes to meet the project needs. From the provided project definition
requirements, it is seems viable to make use of classical algorithmic challenges as
well as designing some novel ones to come up with an algorithm that can be used by
the robots.
Data structures
Corridors and Sections: For the prototype, a python list of dictionaries data
structure will be used to keep track of the status of the given sectors. Each element
of the will contain information about that sector in form of a dictionary.
Robots: Information about the robot will be tracked with a specially designed class.
This class will contain required functions and member variables that keep track of
the information about each robot.
Classical algorithms
Scheduling algorithm: Considering that the robots are operating in a predetermined
schedule, it appears prudent to look at existing scheduling algorithms that will ensure
all the tasks are executed in the most optimum time possible and at reasonable time
and space complexities. Unless the size of the scheduling queue is very small, then
at this level the Shortest Job First algorithm will be used. In a later stage, after the
prototype has been deployed, then the Round Robin algorithm can be tested and
results compared to that of Shortest Job First. Deepening on the results between the
two, the most optimum algorithm will be taken up.
Searching algorithm: Given the fact that the project involves tracking the position of
the robots, in the event that searching of these position is required, then the
selection sort will be used while developing the prototype. Once the prototype
passes the MVP (minimum viable product) threshold, then the performance of the
search can be improved to one with better time and space complexity, either linear
search or binary search.
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