Mobile robotics: autonomous exploration solution for AMR
Robotic exploration: the limits of remote operation
Preliminary stage of a mobile robot integration in an indoor environment, the mapping must allow the machine to acquire sufficient knowledge of the space in which it will evolve. The more precisely this mapping is carried out, the more efficiently and accurately the mobile robot will fulfill its tasks, whether in a tertiary or industrial environment.
Generally, this mapping is carried out by a teleoperator, who controls the robot and guides its trajectory using a controller. However, this remote operation phase has some limits:
- depending on the surface area, the time spent on mapping can be important. It takes about 30 minutes to map 500 m2;
- a human resource is required. The teleoperator must also be trained beforehand.
Thus, remotely operated mapping generates additional costs, that can be avoided and limited through autonomous exploration.
→ Find out more: what is autonomous exploration?
Mobile robot: the relevance of autonomous frontier-based exploration
To allow a mobile robotic solution to map its environment, an algorithm aimed at optimizing the robot’s trajectory has to be developed according to different autonomous exploration methodologies. The Awabot Intelligence team compared these methods to select the most relevant after studying different criteria.
→ Find out more: presentation and comparison of autonomous exploration methods
Among the methodologies studied, autonomous frontier based exploration stands out for its ease of implementation, widespread use and efficiency.
→ Find out more: zoom on the autonomous frontier-based exploration
The Awabot Intelligence team implemented and enhanced an autonomous frontier based exploration method, in a variety of simulation environments, then in a real environment.
- In simulation, the map obtained with autonomous exploration is complete, despite a less precise obstacle resolution than on the ground truth map.
- In a real environment represented by one of the Awabot Headquarter’s offices, the map obtained is complete too, but less precise than the maps obtained in simulation (expected result with real sensors)
Furthermore, experiments comparing Awabot Intelligence autonomous exploration method with teleoperators demonstrated the competitiveness of the algorithm.
Active SLAM: autonomous exploration and mapping solution for robots and mobile devices
The autonomous exploration solution implemented by Awabot Intelligence aims to obtain a complete map, without resorting to a teleoperator, and thus to promote autonomous decision-making by the robot.
This solution is based on:
- SLAM (Simultaneous Localization And Mapping): build a map and allow the robot to locate itself in it;
- navigation: going from point A to point B, taking the best path and avoiding obstacles;
- the exploration strategy: define the robot’s trajectory to observe its entire environment (autonomous frontier-based exploration).
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