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A new algorithm based on Evolutionary Strategies is proposed for finding a robot manipulation path. Next scenario is considered: Given a learned Manipulation Path in the space of configurations, a real-time optimal path is calculated when mobile robot base is in a different position and orientation near to the original localization. The optimization problem is formulated as the minimization of the...
It is necessary to determine the robot's pose to localize a mobile robot in a known environment. If there is no information about the initial location, we are talking about global localization. In our previous work we solved this problem in a two-dimensional environment with an algorithm known as Evolutive Localization Filter (ELF). Based on evolutionary computation concepts, the proposed algorithm...
Global localization methods deal with the estimation of a mobile robot's pose assuming no prior state information about it and a complete a priori knowledge of the environment where the mobile robot is going to be localized. Most existent algorithms are based on the minimization of a L2-norm loss function. However, the use of a L1-norm offers some alternative advantages. The present work explores...
Path planning for autonomous robots is an essential capacity for these systems. One class of path planning algorithms use potential fields. However, some problems associated with these algorithms include (1) Trapping due to local minima, (2) No passage between closely spaced obstacles, and (3) Limit cycles. This paper presents a potential-field-based algorithm that does not exhibit the mentioned problems...
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