1Comprehend the essential problems in motion planning for autonomous systems, including pathfinding, navigation, and collision avoidance.

2Articulate the challenges faced by robots in different environments, such as static, dynamic, and complex terrains.

3Implement and apply classical graph-based search algorithms (e.g., A*, Dijkstra's) to solve planning problems in robotic systems.

4Analyze the trade-offs between different algorithms with respect to computational complexity, optimality, and real-time performance in grid-based and continuous environments.

5Grasp the concepts behind sampling-based planning methods such as Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT).

6Design and implement basic versions of these algorithms in simulation environments, understanding their practical application in high-dimensional configuration spaces.

7Implement collision detection algorithms and integrate them into planning frameworks to ensure safe navigation in complex and cluttered environments.

8Develop an understanding of geometric representations used in collision detection and their limitations in real-world applications.

9Understand kinodynamic constraints (e.g., velocity, acceleration) and incorporate them into motion planning to ensure feasible trajectories for robots.

10Apply basic control and trajectory optimization techniques to produce smooth and dynamic motions in robotics systems.

11Understand the challenges and strategies in multi-agent motion planning, focusing on collision avoidance, path coordination, and reciprocal interactions.

12Implement simple multi-agent planning algorithms, such as Reciprocal Velocity Obstacles (RVO), in a dynamic multi-agent scenario.

13Gain hands-on experience using Robot Operating System (ROS) and other standard robotics frameworks to apply motion planning algorithms in simulation and real-world settings.

14Demonstrate the ability to prototype, test, and debug planning algorithms in simulated robotic systems.

15Understand the role of optimization in motion planning and apply basic optimization-based techniques for trajectory generation and path smoothing.

16Analyze how optimization techniques improve the efficiency and feasibility of motion planning in constrained environments.