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In addition, a short-distance goal is established to help the robot efficiently reach the goal through a reward function that penalizes it for going away from the goal and rewards it for advancing towards it. The proposed model is tested on three state-of-the-art methods: collision avoidance with deep reinforcement learning (CADRL) , long short-term memory (LSTM-RL), and social attention with reinforcement learning (SARL). The suggested method is tested in the Gazebo simulator and the real world with a robot operating system (ROS) in three scenarios. The first scenario involves a robot attempting to reach a goal in free space. The second scenario uses static obstacles, and the third involves humans. The experimental results demonstrate that the model performs better than previous methods and leads to safe navigation in an efficient time.<\/jats:p>","DOI":"10.1007\/s40747-023-01216-y","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T07:02:12Z","timestamp":1692687732000},"page":"1149-1166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Dynamic warning zone and a short-distance goal for autonomous robot navigation using deep reinforcement learning"],"prefix":"10.1007","volume":"10","author":[{"given":"Estrella Elvia","family":"Montero","sequence":"first","affiliation":[]},{"given":"Husna","family":"Mutahira","sequence":"additional","affiliation":[]},{"given":"Nabih","family":"Pico","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3036-4660","authenticated-orcid":false,"given":"Mannan Saeed","family":"Muhammad","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,22]]},"reference":[{"key":"1216_CR1","doi-asserted-by":"publisher","first-page":"39830","DOI":"10.1109\/ACCESS.2020.2975643","volume":"8","author":"MB Alatise","year":"2020","unstructured":"Alatise MB, Hancke GP (2020) A review on challenges of autonomous mobile robot and sensor fusion methods. 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