Path Planning & Trajectory Optimization Using C++ & ROS in Bangalore

Robots are programmable machines that influence every aspect of a human's work and have a high potential to replace humans from performing a range of tasks. For example, it is becoming possible for computers to assist our daily driving. The topics include:

• Graph-Based Algorithms
• Breadth-First Search Algorithm
• Depth-First Search Algorithm

In this week, the students will learn about C-Space i.e., Configuration Space. C-space is the space that provides possible positions for the robot to move. The topics include:

• How to Use the Configuration Space?
• Representing Configuration Space as a Graph
• Planning using Visibility Graph
• Finding the Shortest Path.
• Dijkstra’s Algorithm, A*, Bellman-Ford Algorithm

In this week, the students will learn about sampling-based motion planning. This will solve the navigation queries. Instead of depending on the entire map of the C-space, the robot depends on the procedures that decide if the robot’s configuration is approaching an obstacle or not. The topics include

• Various Types of Rapidly Exploring Random Tree(RRT)
• Application of RRTs
• Path Planning using the RRT Algorithm
• Setting up the Ubuntu Environment

In this week, the students will learn about ROS. ROS is a robotics middleware that manages the complexity and heterogeneity of the hardware and applications. Also, it performs low-level device control, implementation of commonly-used functionality, message-passing between processes, and package management. The topics include:

• Setting up ROS
• Following Instructions on the ROS Website
• Adding ROS to the Docker Container
• Introduction to Cmake
• Programming using ROS
• Introduction to 3-D Visualization Tool - Rviz
• Difference between
  
  • ROS/RTOS
  • ROS1/ROS2
• DDS
• Middleware

In this week, the students will learn about motion planning with non-holonomic robots. Non-Holonomic robots are built in such a way that they only travel in one direction along a given axis. To put it in simple words, Non-Holonomic robots can only move forward, backward, or sideways. The topics for this week include:

• Path and Speed Planning
• Trajectory Representations
  
  • Splines
  • Clothoid
  • Bezier Curves
  • Polynomials
• Introduction to Frenet Frame
• Planning in Frenet Frame
• Boundary Value Constraint Problem and Methods
• Pointwise Constraint Problem and Methods

In this week, the students will learn about Mobile Robot collision detection. The robot will detect a collision and will change its trajectory to escape the contact as fast as possible and move away safely. The topics for this week include:

• Collision Detection for Static Obstacles
• Motion Prediction for Dynamic Obstacles
• Motion Prediction in Frenet Frame with Kalman Filters
• Collision Prediction for Dynamic Obstacles

In this week, the students will learn about Hierarchical Planning for Autonomous Robots. Hierarchical planning optimizes the global path and it requires only a considerable amount of time for the path replanning operations. The topics for this week include:

• Route Planning, A*, D*, D* lite
• HD Maps, SD Maps
• Behavior Planning - State Machines, Decision Tree, Behavior Tree, etc.
• Behavior and Motion Planning Integration

In this week, the students will learn about Trajectory Planning. Trajectory planning plays a major role in robotics and paves way for autonomous vehicles. It is basically the movement of robots from point A to point B by avoiding obstacles over time. The topics for this week include:

• Polynomial Planners
• Motion Planning with Differential Constraints
• Lattice Planners
• Collision Checking
• Trajectory Selection (Cost Functions)

The topics for this week include:

• Vehicle and Tire Model
• Optimal Control
• MPC Planners

In this week, the students will learn about planning in unstructured environments. Unstructured environments include off-roads, parking lots, etc. In such an environment, the robots should be able to identify the optimal path between the start and the goal path. So, for the robots to perform this, a suitable path planning algorithm is required. The topics for this week include:

• Unstructured Planner: Hybrid A*
• Parking Planner
• Automated Driving Open Research (ADORe)

In this week the students will learn about Reinforcement Learning for Planning. Basically, it is a machine learning method that has increased applications in robot path planning. The robot will explore its surrounding environment and learn using the trial and error process. The machine learning method has an advantage in path planning and requires less prior information. The topics for this week include:

• Machine Learning
• Markov Decision Process
• Policy Evaluation
• Value iteration
• Reinforcement Learning
• On/Off Policy, Model-based/Model-free Monte Carlo
• Bellman Optimality, SARSA
• Q-learning, Epsilon Greedy
• Decision Making for AVs

The topics for this week include:

• Overview of the Topics Learned
• Paper Review
• Non-Traditional Applications