Motion Planning & Mobile Robots

Finding collision-free paths. Graphs, configuration space, A*, RRT and PRM planners, and mobile robot navigation.

• Graph Theory & Networks — Graphs model networks of all kinds. Learn vertices and edges, directed, undirected and weighted graphs, degree, paths, cycles and connectivity, trees and spanning trees (Kruskal, Prim), shortest paths (Dijkstra), topological sorting, and network flow (max-flow/min-cut) — with a Python Dijkstra implementation.
• Motion Planning & Collision Avoidance — Finding collision-free paths automatically. Learn the configuration space (C-space) and C-obstacles, collision checking (bounding volumes, GJK, distance fields), complete planners (visibility graphs, cell decomposition), grid search (Dijkstra, A*), sampling-based planners (PRM, RRT, RRT-Connect, RRT*), path shortcutting and smoothing, artificial potential fields and local minima, trajectory optimisation, planning for manipulators with MoveIt, and task and motion planning — with NumPy code running an RRT in a 2-D configuration space around an obstacle.
• Mobile Robotics & Autonomous Navigation — Robots that move around. Learn locomotion types, differential-drive and other kinematic models, wheel odometry and its drift, sensors (encoders, IMU, lidar, cameras, GNSS), state estimation and sensor fusion, occupancy-grid mapping and SLAM, path planning (A*, Dijkstra, RRT), path following and obstacle avoidance, ROS 2 and the navigation stack, AGVs/AMRs in industry and safety — with Python for odometry and A* planning.