Create a map of an environment using range sensor readings and robot poses for a differential drive robot. You create a map from range sensor readings that are simulated using the rangeSensor object. The differentialDriveKinematics motion model simulates driving the robot around the room based on velocity commands. The rangeSensor gives range readings based on the pose of the robot as it follows the path.

Demonstrates how to compute an obstacle free path between two locations on a given map using the Probabilistic Roadmap (PRM) path planner. PRM path planner constructs a roadmap in the free space of a given map using randomly sampled nodes in the free space and connecting them with each other. Once the roadmap has been constructed, you can query for a path from a given start location to a given end location on the map.

Demonstrates how to execute an obstacle free path for a mobile robot between three locations on a given map. The robot is expected to visit the three locations in a warehouse: a charging station, loading station, and unloading location. The sequence in which these locations are visited is dictated by a scheduler. The scheduler gives each robot a goal pose to navigate to. The robot plans a path and uses a Pure Pursuit controller to follow the waypoints based on the current pose of the robot. The Differential Drive Kinematic Model block models the simplified kinematics, which takes the linear and angular velocities from the Pure Pursuit Controller. This example builds on top of the Plan Path for a Differential Robot in Simulink example.

Control and simulate multiple robots working in a warehouse facility or distribution center. The robots drive around the facility picking up packages and delivering them to stations for storing or processing. This example builds on top of the Execute Tasks for a Warehouse Robot example, which drives a single robot around the same facility.

Simulate a warehouse robot in Gazebo. Gazebo enabled you to simulate a mobile robot that uses a range sensor, while executing certain tasks in a simulated environment. This example details how to use a simulator to apply the Execute Tasks for A Warehouse Robot example, where a robot delivers packages in a warehouse scenario. The robot makes use of the simulated range sensor in Gazebo to detect possible collisions with a dynamic environment and avoid them.

An extension to the Simulate A Mobile Robot in a Warehouse using Gazebo example. The example shows to change the PRM path planner with an A* planner, and add a vector field histogram (VFH) algorithm to avoid obstacles.

Uses Deep Deterministic Policy Gradient (DDPG) based reinforcement learning to develop a strategy for a mobile robot to avoid obstacles. For a brief summary of the DDPG algorithm, see Deep Deterministic Policy Gradient Agents.