Robotic mapping

Robotic mapping is a discipline related to computer vision and cartography. The goal for an autonomous robot is to be able to construct (or use) a map (outdoor use) or floor plan (indoor use) and to localize itself and its recharging bases or beacons in it. Robotic mapping is that branch which deals with the study and application of ability to localize itself in a map / plan and sometimes to construct the map or floor plan by the autonomous robot. Evolutionarily shaped blind action may suffice to keep some animals alive. For some insects for example, the environment is not interpreted as a map, and they survive only with a triggered response. A slightly more elaborated navigation strategy dramatically enhances the capabilities of the robot. Cognitive maps enable planning capacities and use of current perceptions, memorized events, and expected consequences. The robot has two sources of information: the idiothetic and the allothetic sources. When in motion, a robot can use dead reckoning methods such as tracking the number of revolutions of its wheels; this corresponds to the idiothetic source and can give the absolute position of the robot, but it is subject to cumulative error which can grow quickly. The allothetic source corresponds the sensors of the robot, like a camera, a microphone, laser, lidar or sonar. The problem here is "perceptual aliasing". This means that two different places can be perceived as the same. For example, in a building, it is nearly impossible to determine a location solely with the visual information, because all the corridors may look the same. 3-dimensional models of a robot's environment can be generated using range imaging sensors or 3D scanners. The internal representation of the map can be "metric" or "topological": The metric framework is the most common for humans and considers a two-dimensional space in which it places the objects. The objects are placed with precise coordinates. This representation is very useful, but is sensitive to noise and it is difficult to calculate the distances precisely.

Inverse Reinforcement Learning of Pedestrian-Robot Coordination

Aude Billard, David Julian Gonon

We apply inverse reinforcement learning (IRL) with a novel cost feature to the problem of robot navigation in human crowds. Consistent with prior empirical work on pedestrian behavior, the feature anticipates collisions between agents. We efficiently learn ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Avoidance of Concave Obstacles Through Rotation of Nonlinear Dynamics

Learning to make mixed-integer robot motion planning easy

Inverse Reinforcement Learning of Pedestrian-Robot Coordination

Learning to make mixed-integer robot motion planning easy

Inverse Reinforcement Learning of Pedestrian-Robot Coordination

Avoidance of Concave Obstacles Through Rotation of Nonlinear Dynamics