Abstract
This work describes the performance of a DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm) algorithm applied to autonomous navigation in unknown static and dynamic terrestrial environments. The main aim was to validate the functionality and robustness of the DPNA-GA, with variations of genetic parameters including the crossover rate and population size. To this end, simulations were performed of static and dynamic environments, applying the different conditions. The simulation results showed satisfactory efficiency and robustness of the DPNA-GA technique, validating it for real applications involving mobile terrestrial robots.
Highlights
In most of the studies concerning Genetic Algorithms (GAs) encountered in the literature, global or local planning strategies are employed
Different to the studies cited above, the work described in Ref. [22] presents a navigation strategy called the Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm (DPNA-GA)
Even though navigation techniques with local planning provide suboptimal solutions, it can be seen from the results presented that in many cases it is possible to obtain routes very close to the optimum
Summary
In most of the studies concerning Genetic Algorithms (GAs) encountered in the literature, global or local planning strategies are employed. The first is that the size of the individual is variable and is a function of the length of the route (the greater the complexity of the environment, the greater the length) and the resolution of the grid associated with the displacement of the robot. This can significantly increase the spatial and temporal complexity of the GA, making it unviable for use in limited hardware systems such as microcontrollers (MCUs), digital signal processors (DSPs), and others. The third point is that these global strategies are better suited to static environments, due to the necessity of using external observation equipment for dynamic environments
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