000			02215nam a22001577a 4500
082			_a629.8
100			_aKhan, Zahra _934408
245			_aRobot Motion planning using Deep Reinforcement Learning / _cZahra Khan
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a62p. _bSoft Copy _c30cm
500			_aEffective motion planning is needed for autonomous robots in challenging environments. Sampling-based algorithms sample random points in high-dimensional spaces but struggle to work well in complex environments due to slow convergence and inefficiencies. Improvements in Deep Reinforcement Learning (DRL) overcome this strategy through learning optimal policies from acting in the environment, reducing reliance on environmental data and faster rates of convergence. But DRL resorts to sparse reward functions to produce suboptimal paths and poor exploration. To advance beyond such shortcomings, we propose an active SLAM-sourced information reward function. SLAMweighted reward enhances navigation efficiency with richer environment perception and robustness in unexplored areas. It comprises distance-to-target and smoothness terms over trajectory that encourage reduced distances, reduced oscillation, and more stable robot performance. We use the Soft Actor-Critic (SAC) algorithm in our reward function, because it is best to perform well in new environments. Comparison experiments using Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) in cluttered and sparse environments demonstrated the superior performance of SAC. In sparse settings, SAC was 100% successful, performing 9.4% better than TD3 and 14.1% better than DDPG, and using 35% fewer steps than TD3 and 63% fewer than DDPG. In chaotic settings, SAC was 87.5% successful, performing 40.6% better than TD3 and 71.9% better than DDPG. These performances attest to the unprecedented effectiveness of the SAC algorithm in the direction of autonomous robots.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Fahad Iqbal _9130707
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/55263
942			_2ddc _cTHE
999			_c614873 _d614873