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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.

Particle filter is a sampling-based recursive Bayesian estimation algorithm, which is implemented in the stateEstimatorPF object. In this example, a remote-controlled car-like robot is being tracked in the outdoor environment. The robot pose measurement is provided by an on-board GPS, which is noisy. There are known motion commands sent to the robot, but the robot will not execute the exact commanded motion due to mechanical slack or model inaccuracy. This example will show how to use stateEstimatorPF to reduce the effects of noise in the measurement data and get a more accurate estimation of the pose of the robot. The kinematic model of a car-like robot is described by the following non-linear system. The particle filter is ideally suited for estimating the state of such kind of systems, as it can deal with the inherent non-linearities.

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 DDPG(심층 결정적 정책 경사법) 에이전트 (Reinforcement Learning Toolbox).