Robotics & Autonomous Systems — Perception, Planning, Control, and SLAM
A technical overview of modern robotics: sensor modalities, perception pipelines, simultaneous localization and mapping (SLAM), motion planning, control loops, autonomy stacks, ROS, and verification & safety.
Sensing & Perception
Robots integrate IMUs, LiDAR, cameras, RGB-D sensors, and proprioceptive encoders. Sensor fusion (EKF/UKF, factor graphs) produces robust state estimates for localization and mapping.
SLAM and Mapping
SLAM constructs a map while simultaneously estimating robot pose. Approaches include EKF-SLAM, Graph-SLAM (pose graph optimization), and particle-filter based methods; modern systems use lidar/camera fusion and loop-closure detection using bag-of-words or learned embeddings.
State Estimator / SLAM
Map & Trajectory
Motion Planning & Control
Planning algorithms produce collision-free trajectories: sampling-based (RRT*, PRM), optimization-based (CHOMP, TrajOpt), and search-based methods. Controls implement tracking via PID, LQR, model predictive control (MPC), or adaptive controllers for dynamic tasks.
Autonomy Stack & ROS
Robotic stacks include perception, state estimation, planning, control, and behavior layers. ROS/ROS2 provide middleware for messaging, componentization, and simulation (Gazebo, Ignition). Verification and safety require simulation-in-the-loop, formal methods for critical behaviors, and runtime monitors.
Applications
- Autonomous vehicles and ADAS
- Logistics and warehouse automation
- Inspection drones and industrial robotics
- Assistive robots and teleoperation
References
- S. Thrun, W. Burgard, D. Fox, Probabilistic Robotics, MIT Press, 2005.
- O. Khatib et al., ROS/ROS2 documentation and community tutorials.
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