NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-rl:"Navigation")'

Fundamentals of aerospace navigation and guidance /

By Kabamba, Pierre T.,. . xv, 316 pages : 27 cm.. 9781107070943 (hardback)

Place Hold on Fundamentals of aerospace navigation and guidance /

Traffic Signal Control using Reinforcement Learning /

By Umer Jamil, Qazi . . 90p. ; 30cm..

Place Hold on Traffic Signal Control using Reinforcement Learning /

RL based Differential Drive Primitive Policy for Transfer Learning /

By Shahid, Mahrukh . . 52p. , To ensure the steady navigation for robot stable controls are the basic unit and control values selection is highly environment dependent. Adding Generalization to system is the key to reusability of control parameters to ensure adaptability in robots to perform with sophistication, in the environments about which they have no prior knowledge, for this Reinforcement Leaning (RL) based control systems are promising. However, tuning appropriate parameters to train RL algorithm is a challenge. Therefore, we designed a continuous reward function to minimizing the sparsity and stabilizes the policy convergence, to attain control generalization for differential drive robot. We Implemented Twin Delayed Deep Deterministic Policy Gradient-TD3 on Open-AI Gym Race Car. System was trained to achieve smart primitive control policy, moving forward in the direction of goal by maintaining an appropriate distance from walls to avoid collisions. Resulting policy was tested on unseen environments and observed precisely performing results. Upon comparative analysis of TD3 with DDPG, TD3 policy outperformed the DDPG policy in both training and testing phase, proving TD3 to be resource efficient and stable. 30cm.

Place Hold on RL based Differential Drive Primitive Policy for Transfer Learning /

Addressing the Problem of Sloshing in a Liquid Carrying Mobile Robot through Artificial Intelligence /

By Khan, Khubaib Haider . . 90p. , Liquid sloshing presents a critical challenge for mobile robots tasked with transporting partially filled containers, as it destabilizes motion, reduces accuracy, and increases the risk of spillage. Traditional passive and active control methods—such as baffles, PID, and LQR—are limited in adaptability and computational efficiency when faced with nonlinear, time-varying fluid–structure interactions. This thesis addresses these challenges by formulating slosh suppression as a reinforcement learning problem, leveraging the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm integrated with a surrogate ball-in-container analogy in the Webots simulator. The surrogate transforms complex liquid dynamics into a tractable cart-pole inspired system, enabling efficient training of robust control policies without reliance on high-fidelity but computationally expensive CFD models. A custom simulation–learning pipeline was developed, coupling Webots with a PyTorchbased TD3 agent via JSON-based communication. State and action spaces were defined using the ball’s displacement ratio and robot velocity, while a multi-component reward function balanced slosh minimization, forward progress, and smooth energy-efficient motion. Training results demonstrated that the TD3 agent learned to achieve stable, sloshfree navigation over a 10 m trajectory, outperforming traditional controllers and earlier RL variants in stability and adaptability. Results shows that the Model Learns well after 600 Episodes attaining optimal velocity of 1.25 – 0.5 m/s keeping ball within 2 mm limit, maximizing the reward to around 1180 at 1700 Episodes, eventually reducing sloshing by 65 % during Training phase. While, during Test phase robot is tested for 0 to 4 m/s velocity with control and no control scenarios, showing the results RL algorithm suppress the ball displacement (liquid slosh) by approximately 45 %. The study validates reinforcement learning as a viable paradigm for real-time liquid slosh suppression in robotics, offering superior robustness and scalability. Contributions include an AI-driven control framework for slosh suppression, integrationxvii of surrogate modeling with DRL for real-time feasibility, and a reward design framework encoding domain-specific stability objectives. For Further Future working it is recommended to include extending to real liquid models and hardware validation, adopting symmetric action spaces and advanced RL methods, and integrating slosh control with autonomous navigation in dynamic environments. 30cm.

Place Hold on Addressing the Problem of Sloshing in a Liquid Carrying Mobile Robot through Artificial Intelligence /