Project Objectives

• Acting : Implemented motion control strategies, including PID controllers and trajectory-following algorithms for precise navigation.

• Sensing : Utilized sensors like quadrature encoders, a 2D LiDAR, an IMU, and a camera for environment perception and robot localization.

• Reasoning : Developed algorithms for Monte Carlo Localization (MCL), simultaneous localization and mapping (SLAM), and path planning.

Implementation Methodology

1. Low Level Control

   • Designed and tuned a PID controller for precise wheel speed control and body velocity adjustments.

   • Implemented an odometry module using encoder and IMU data to estimate the robot's position and orientation through dead reckoning.

   • Developed a motion controller to execute paths between waypoints, ensuring smooth transitions and maintaining trajectory accuracy.

     
     

2. Vision Stack

   • Calibrated the camera for intrinsic and extrinsic parameters, enabling accurate mapping between image and world coordinates.

   • Utilized AprilTags for obstacle detection, distance estimation, and visual servoing to align the robot with targets.

   
   

3. Simultaneous Localization & Mapping (SLAM)

   • Implemented an occupancy grid mapping algorithm using LiDAR data and ground-truth poses.

   • Developed a Monte Carlo Localization (MCL) module to estimate the robot’s pose within a known map using a particle filter.

   • Combined localization and mapping into a robust SLAM system, enabling real-time environment mapping and navigation.

     
     

4. Path Planning

   • Built an A* path planner to compute optimal paths through a mapped environment.

   • Implemented obstacle avoidance strategies and ensured smooth execution of planned paths using a motion controller.

   • Designed an exploration algorithm to autonomously navigate and map unknown environments by identifying unexplored frontiers.

     
     

5. Lifting Mechanism

   • Designed and prototyped a gripper mechanism to lift and place targets.

   • Integrated gripper control with the MBot's motion system to complete pick-and-place tasks.

   
   

Results & Challenges

• Maze Eploration : Successfully mapped a maze, returned to the starting pose, and achieved high accuracy in map quality.

• Warehouse Task : Completed the pick-and-place challenge by retrieving and placing multiple targets within time constraints.