Path Planning in a Mobile Robot

Robotics is an area with vast prospects for industrial development that in a relatively short time could allow companies to improve production techniques, quality, and precision in many different processes. 

For some years now, mobile robots have been introduced into various productive areas. This interaction has increased the need to provide autonomy to robots, which can interact beyond 

Increasingly, robots must interact in environments where workspaces are not static, meaning that robots must have the tools to adequately perform dieir required tasks despite changes that can occur in cases where planned trajectories represent part of the solution. 

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Path planning has been approached in different applications, including robotic manipulators, mobile robots, and underwater robots, among others. Similarly, different methodologies have been developed for both static and d5mamic environments, which use different sensors for local and global planning of the workspace, as will be shown in the brief section review. 

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Bodhale, D., Afzulpurkar, N., Thanh, N. T. (2008, February). Path planning for a mobile robot in a dynamic environment. Paper presented at IEEE International Conference on Robotics and Biomimetics, 2008. Bangkok, Thailand. 

It may be possible to apply mathematical and logical operations, in this paper we are focused only on the application that modelises an environment and performs the path planning for a mobile robot. 

The design of the SURV-TRACK application raises several challenges. There aret5 ically three underlying questions that should be answered in the SURV-TRACK framework (1) How should the system components effectively communicate with each other (2) How can the tasks be efficiently subdivided among the different actors (mobile robots and sensor nodes) of the system (3) How can a mobile robot plan its path to accomplish its mission ... 

Sugihara, K., Smith, J. (1999). Genetic algorithms for adaptive planning of path and trajeetory of a mobile robot in 2D terrains. lEICE Transactions on Information and Systems, 82(1), 309-317. 

The path-planning problem can be done in both static and dynamic environments a static environment is imchanging, the start and goal positions are fixed, and obstacles do not change locations over time. However, in a dynamic environment, mobile robots are exposed to unexpected situations, including the locations of obstacles and the target, which may change overtime. 

Path Planning It aims at the construction of an obstacles-free path for the mobile robot from a starting state to a goal state, through several obstacles scattered in an environment. 

Bodhale, Afzulpurkar, and Thanh (2008) integrate potential fields and use Monte Carlo localization for navigation, obstacle avoidance, and mobile robot localization in a dynamic environment. The path planning algorithm is divided in two submodules, the first includes visibility graph with A search method and the second is the local planning using potential fields. 

Path planning is one of the most important tasks regarding intelligent control of an autonomous mobile robot (Tibaduiza, 2006). The autonomy of a robot is evaluated in terms of the intelligence the robot displays by taking a decision such as going from one point to another without colliding with the elements presented in the workspace. 

The hardware of the artificial vision system consists of an analog camera, a digitizer card, and a computer to process all the images captured from workspace. From the images, the information about the actual position of the mobile robot and the obstacles are obtained. The trajectory defined by the path planning method must be recalculated depending, for instance, on whether the mobile obstacle stops its execution. In the workspace, the stationary objects were identified with a blue color and the mobile obstacles with yellow. 

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