Learning for Robot Navigation Overview

The process involving mapping, localisation, path planning, and motion commands for robot navigation.

Creating a representation of the environment for a robot to navigate.

Determining the robot's current position within a mapped environment.

The process of finding a route from the robot's current location to a goal location.

Applying low-level commands to move the robot along the planned path.

A method that allows robots to map unknown environments while determining their own location.

The ability of a robot to navigate and create a map of an unfamiliar area concurrently.

Navigating in a mapped space where the environment does not change significantly over time.

Navigation in structured environments that vary in structure and can change dynamically.

Navigation in diverse environments and terrains, often more static than indoor navigation.

Environments that change frequently, affecting how navigation occurs.

Environments that remain consistent over time, allowing for predictable navigation.

The necessity of navigating in crowded or busy environments with other individuals present.

The incorporation of learning into robot navigation by replacing traditional components with learned versions.

A learned strategy used in place of a local planner for robot navigation.

Forecasts made by a predictive model utilized for motion planning in robots.

An autonomous navigation system using self-supervised learning to improve navigation components.

Replacing the entire navigation process with components trained end-to-end.

A method for robots to navigate without human intervention, often in crowded environments.

A formal framework representing navigation tasks as states, actions, and rewards.

The overall state in navigation learning, combining various inputs like sensor data.

Different types of data used in navigation, including GPS, IMU, RGB cameras, and Lidars.

The process of learning specific parameters used within navigation components.

Different parts of the navigation system, like planners and controllers that can be individually learned.

The ability of a robot to identify and navigate around obstacles using various representations.

Finding a viable, collision-free path from the robot's current location to its destination.

Creating appropriate motion commands based on current sensor information.

A sequence of waypoints the robot should follow to reach its destination.

Specific points along a path that the robot must pass.

A mathematical representation of robot motion used in local planning.

Navigation that relies solely on geometric features of the environment.

Assumes that robots navigate on a flat surface, applicable in many cases.

Algorithms tailored specifically for individual robot types, limiting reusability.

Every navigation trial is treated independently, preventing learning from previous trials.

Difficulty in adjusting standard navigation frameworks for new environments.

Data collected by the robot's sensors to aid in local planning.

A trajectory that avoids obstacles while guiding the robot to its destination.

Mathematical methods used to determine paths and movements for robots.

Contextual information that aids in navigation but may be ignored by geometric methods.

The process of guiding a robot from one location to another using planning strategies.

Self-driving technology that allows vehicles to navigate without human input, using sensors and learning algorithms.

Roads that are subject to frequent changes due to traffic, weather, or obstacles, making navigation challenging.

Approaches that rely on mathematical models to navigate, which can struggle with complex real-world conditions.

Technology that enables vehicles to interpret visual information from the environment, crucial for safe navigation.

A subset of machine learning that processes large amounts of data to improve vehicle navigation tasks like detection and control.

Regulations that govern road usage, essential for autonomous vehicles to obey to ensure safety and compliance.

The sequence of processes involved in autonomous driving, including perception, decision-making, and control.

The capability of an autonomous vehicle to recognize and interpret its surroundings, crucial for safe navigation.