{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T12:55:27Z","timestamp":1777380927657,"version":"3.51.4"},"reference-count":36,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIS"],"published-print":{"date-parts":[[2023,9,5]]},"abstract":"<jats:p>Path planning algorithms determine the performance of the ambient intelligence navigation schemes in autonomous mobile robots. Sampling-based path planning algorithms are widely employed in autonomous mobile robot applications. RRT*, or Optimal Rapidly Exploring Random Trees, is a very effective sampling-based path planning algorithm. However, the RRT* solution converges slowly. This study proposes a directional random sampling-based RRT* path planning algorithm known as DR-RRT* to address the slow convergence issue. The novelty of the proposed method is that it reduces the search space by combining directional non-uniform sampling with uniform sampling. It employs a random selection approach to combine the non-uniform directional sampling method with uniform sampling. The proposed path planning algorithm is validated in three different environments with a map size of 384*384, and its performance is compared to two existing algorithms: RRT* and Informed RRT*. Validation is carried out utilizing a TurtleBot3 robot with the Gazebo Simulator and the Robotics Operating System (ROS) Melodic. The proposed DR-RRT* path planning algorithm is better than both RRT* and Informed RRT* in four performance measures: the number of nodes visited, the length of the path, the amount of time it takes, and the rate at which the path converges. The proposed DR-RRT* global path planning algorithm achieves a success rate of 100% in all three environments, and it is suited for use in all kinds of environments.<\/jats:p>","DOI":"10.3233\/ais-220292","type":"journal-article","created":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T11:43:26Z","timestamp":1674819806000},"page":"269-284","source":"Crossref","is-referenced-by-count":10,"title":["A novel directional sampling-based path planning algorithm for ambient intelligence navigation scheme in autonomous mobile robots"],"prefix":"10.1177","volume":"15","author":[{"given":"Sivasankar","family":"Ganesan","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India"}]},{"given":"Senthil Kumar","family":"Natarajan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi, India"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/AIS-220292_ref1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3233\/AIS-160413","article-title":"A mobile and interactive multiobjective urban tourist route planning system","volume":"9","author":"Ayala","year":"2017","journal-title":"J. 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