Khan, Zahra creator Supervisor : Dr. Fahad Iqbal text monographic 62p. Soft Copy 30cm Zahra Khan Effective 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. MS Robotics and Intelligent Machine Engineering 629.8 http://10.250.8.41:8080/xmlui/handle/123456789/55263 http://10.250.8.41:8080/xmlui/handle/123456789/55263