Motion and manipulation

Artificial Intelligence is heavily used in robotics.  Localisation is how a robot knows its location and maps its environment. When given a small, static, and visible environment, this is easy; however, dynamic environments, such as (in endoscopy) the interior of a patient’s breathing body, pose a greater challenge.

Motion planning is the process of breaking down a movement task into “primitives” such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object. Robots can learn from experience how to move efficiently despite the presence of friction and gear slippage.



Robot localisation denotes the robot’s ability to establish its own position and orientation within the frame of reference. Path planning is effectively an extension of localisation, in that it requires the determination of the robot’s current position and a position of a goal location, both within the same frame of reference or coordinates. Map building can be in the shape of a metric map or any notation describing locations in the robot frame of reference.

robot localisation

For any mobile device, the ability to navigate in its environment is important. Avoiding dangerous situations such as collisions and unsafe conditions (temperature, radiation, exposure to weather, etc.) comes first, but if the robot has a purpose that relates to specific places in the robot environment, it must find those places. This page is an overview of the skill of navigation and tries to identify the basic blocks of a robot navigation system, types of navigation systems, and closer look at its related building components.

Robot navigation means the robot’s ability to determine its own position in its frame of reference and then to plan a path towards some goal location. In order to navigate in its environment, the robot or any other mobility device requires representation, i.e. a map of the environment and the ability to interpret that representation.

Navigation can be defined as the combination of the three fundamental competences:

  1. Self-localisation
  2. Path planning
  3. Map-building and map interpretation

Some robot navigation systems use simultaneous localization and mapping to generate 3D reconstructions of their surroundings.


Vision-based navigation

Vision-based navigation or optical navigation uses computer vision algorithms and optical sensors, including laser-based range finder and photometric cameras using CCD arrays, to extract the visual features required to the localization in the surrounding environment. However, there are a range of techniques for navigation and localization using vision information, the main components of each technique are:

  • representations of the environment
  • sensing models
  • localisation algorithms.

In order to give an overview of vision-based navigation and its techniques, we classify these techniques under indoor navigation and outdoor navigation.


Indoor navigation

The easiest way of making a robot go to a goal location is simply to guide it to this location. This guidance can be done in different ways: burying an inductive loop or magnets in the floor, painting lines on the floor, or by placing beacons, markers, bar codes etc. in the environment. Such Automated Guided Vehicles (AGVs) are used in industrial scenarios for transportation tasks. Indoor Navigation of Robots are possible by IMU based indoor positioning devices.

There are a very wider variety of indoor navigation systems. The basic reference of indoor and outdoor navigation systems is “Vision for mobile robot navigation: a survey” by Guilherme N. DeSouza and Avinash C. Kak.


Autonomous Flight Controllers

Typical Open Source Autonomous Flight Controllers have the ability to fly in full automatic mode and perform the following operations;

  • Take off from the ground and fly to a defined altitude
  • Fly to one or more waypoints
  • Orbit around a designated point
  • Return to the launch position
  • Descend at a specified speed and land the aircraft

The onboard flight controller relies on GPS for navigation and stabilised flight, and often employ additional Satellite-based augmentation systems (SBAS) and altitude (barometric pressure) sensor.


Inertial navigation

Some navigation systems for airborne robots are based on inertial sensors.

Acoustic navigation

Autonomous underwater vehicles can be guided by underwater acoustic positioning systems. Navigation systems using sonar have also been developed.

Radio navigation

Robots can also determine their positions using radio navigation.


Motion Planning

Motion planning, also path planning (also known as the navigation problem or the piano mover’s problem) is a computational problem to find a sequence of valid configurations that moves the object from the source to destination. The term is used in computational geometry, computer animation, robotics and computer games.

For example, consider navigating a mobile robot inside a building to a distant waypoint. It should execute this task while avoiding walls and not falling down stairs. A motion planning algorithm would take a description of these tasks as input, and produce the speed and turning commands sent to the robot’s wheels. Motion planning algorithms might address robots with a larger number of joints (e.g., industrial manipulators), more complex tasks (e.g. manipulation of objects), different constraints (e.g., a car that can only drive forward), and uncertainty (e.g. imperfect models of the environment or robot).

Motion planning has several robotics applications, such as autonomy, automation, and robot design in CAD software, as well as applications in other fields, such as animating digital characters, video game, architectural design, robotic surgery, and the study of biological molecules.

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