Abstract
In this article are compared random waypoint motion models and Manhattan against a model of semicircular motion, analyzing the total distance traveled, the average total energy of robots’ agents and convergence time at 80% of exploration of the navigation area using as design methodology, design-based science. Agent's robots involved in the experiment show no cognitive and cooperative parameter, therefore, the results obtained in this article will allow extracting temporal and energy characteristics that allows exploring as much area in the shortest possible time, in order to get heuristics for future work in SLAM tasks
Highlights
A technological aspect present in terrain recognition systems and space deals with localization tasks and simultaneous mapping
In this article the use of the two most widespread motion stochastic models are analyzed. These are random waypoint motion model and Manhattan; along with a semicircular motion model, where the movement is performed in closed trajectories
The robot agent will move about 10cm/s, each of these movements will be equivalent to one unit in the NetLogo environment simulation
Summary
A technological aspect present in terrain recognition systems and space deals with localization tasks and simultaneous mapping. The motion model plays an important role since these models can define energy efficiency in SLAM tasks in decentralized and distributed environments, where disclosure is given in an ad hoc form. It is necessary to evaluate the movement characteristics of the robotic agents before starting SLAM tasks and adding intelligence and perception to robotic units To compare these motion models, based on the methodology of design-based science, a computational experiment series were designed. Within the methodology of design-based science it is suitable to meet at least two conditions: i) The methodology must be able to search through a range of solutions Out of those one or more are not biased by experience or intuition of the designer, creating “new alternatives” that were unknown. The output of the methodology allows observing the computational assessment of the experiment using visual analytics tools
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