This paper presents the development of a modular mobile robot platform for multiple purposes and its adaptation into a beach cleaner setup. The robot has a robust construction allowing it to endure several sorts of environments. In the presented configuration, the developed robot is able to autonomously collect cans using an excavator-like claw. In addition to detailing the system's design and construction, this paper presents the description of the developed embedded electronics modules, a motor closed-loop speed control system and the optical flow algorithms that allows the computer vision system to detect and avoid obstacles and track the cans to be collected.Keywords: Mobile robots, robotics, computer vision, optical flow, speed control.
RESUMEN
This paper examines the impact of different box merging strategies for sampling-based uncertainty estimation methods in object detection. Also, a comparison between the almost exclusively used softmax confidence scores and the predicted variances on the quality of the final predictions estimates is presented. The results suggest that estimated variances are a stronger predictor for the detection quality. However, variance-based merging strategies do not improve significantly over the confidence-based alternative for the given setup. In contrast, we show that different methods to estimate the uncertainty of the predictions have a significant influence on the quality of the ensembling outcome. Since mAP does not reward uncertainty estimates, such improvements were only noticeable on the resulting PDQ scores.
Safe navigation is one of the steps necessary for achieving autonomous control of robots. Among different algorithms that focus on robot navigation, Reinforcement Learning (and more specifically Deep Reinforcement Learning) has shown impressive results for controlling robots with complex and highdimensional state representations. However, when integrating methods to comply with safety requirements by means of constraint satisfaction in flat Reinforcement Learning policies, the system performance can be affected. In this paper, we propose a constrained Hierarchical Reinforcement Learning framework with a safety layer used to modify the low-level policy to achieve a safer operation of the robot. Results obtained in simulation show that the proposed method is better at retaining performance while keeping the system in a safe region when compared to a constrained flat model.
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