Matebese, BBanda, MKUtete, S2012-10-312012-10-312012-10Matebese, B, Banda, MK and Utete, S. Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories. 4th CSIR Biennial Conference: Real problems relevant solutions, CSIR, Pretoria, 9-10 October 2012http://hdl.handle.net/10204/62544th CSIR Biennial Conference: Real problems relevant solutions, CSIR, Pretoria, 9-10 October 2012Sampling-based methods such as Rapidly-exploring Random Tree (RRT) have been successfully used in solving motion planning problems in high-dimensional and complex environments. The RRT algorithm is the most popular and has the ability to find a feasible solution faster than other algorithms. The drawback of using RRT is that, as the number of samples increases, the probability that the algorithm converges to a sub-optimal solution increases. Furthermore, the path generated by this algorithm is not smooth (tree form). The RRT-based methods will be discussed and simulations are given to evaluate the performance of the methods.enRapidly-exploring Random TreeRRTAutonomous mobile robotsSamplingRoboticsRRT*Conference PresentationMatebese, B., Banda, M., & Utete, S. (2012). Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories. http://hdl.handle.net/10204/6254Matebese, B, MK Banda, and S Utete. "Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories." (2012): http://hdl.handle.net/10204/6254Matebese B, Banda M, Utete S, Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories; 2012. http://hdl.handle.net/10204/6254 .TY - Conference Presentation AU - Matebese, B AU - Banda, MK AU - Utete, S AB - Sampling-based methods such as Rapidly-exploring Random Tree (RRT) have been successfully used in solving motion planning problems in high-dimensional and complex environments. The RRT algorithm is the most popular and has the ability to find a feasible solution faster than other algorithms. The drawback of using RRT is that, as the number of samples increases, the probability that the algorithm converges to a sub-optimal solution increases. Furthermore, the path generated by this algorithm is not smooth (tree form). The RRT-based methods will be discussed and simulations are given to evaluate the performance of the methods. DA - 2012-10 DB - ResearchSpace DP - CSIR KW - Rapidly-exploring Random Tree KW - RRT KW - Autonomous mobile robots KW - Sampling KW - Robotics KW - RRT* LK - https://researchspace.csir.co.za PY - 2012 T1 - Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories TI - Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories UR - http://hdl.handle.net/10204/6254 ER -