Navigation in the absence of initial environmental information is a situation in which a robot is faced with the difficulty of traversing an unknown area for exploration with obtaining the environmental information simultaneously. Therefore, to complete and optimize the exploration efficiently, the robot needs an autonomous path-planning algorithm. This work proposes a new autonomous path-planning algorithm for exploration in an unknown environment based on paired frontiers, which we call internal and external frontiers algorithm (IEFA), that defines extended area for navigation of the mobile robot. For each exploration round, the robot defines external frontiers using the maximum range of sensors. Then, the robot generates internal frontiers, that is, pairs of external frontiers by varying the range of sensors. According to the size of each pair of frontiers, the algorithm generates the target point for robot navigation. The frontiers of internal layer are utilized as a main parameter for generation of next exploration point. We evaluated the proposed algorithm in simulation environments using the ROS toolbox of MATLAB and compared it with two previous exploration algorithms. From the experimental results, the proposed algorithm showed from 31% to 85% better performance in the path distance than previous algorithms.
To prevent crop damage from harmful birds, various repelling methods have been studied. However, harmful birds are still causing damage in the orchard by adapting to the repelling device according to their biological characteristics. This paper proposes a method called Anti-adaptive Harmful Birds Repelling (AHBR) that uses the model-free learning idea of the Reinforcement Learning (RL) approach to repell harmful birds that can effectively prevent bird adaptation problems. To prevent adaptation, the AHBR method uses a method of learning the bird's reaction to the available threat sounds and playing them in patterns that are difficult to adapt through the RL approach. We also proposed the Long-term and Shortterm (LaS) policy to meet the Markov assumptions that make RL difficult to implement in the real world. The LaS policy enable learning of the actual bird's reaction to the sound of a threat. The performance of the AHBR method was evaluated in a closed environment to experiment real harmful bird such as Brown-eared Bulbul, Great Tit, and Eurasian Magpie captured in orchards. Results obtained from the experiment showed that the AHBR method was on average 43.5% better than the threat sound patterns(One, Sequential, Reverse Sequential, Random) used in commercial products.INDEX TERMS Agricultural engineering. Machine learning. Intelligent systems. Automation. Antiadaptive repeller
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