Self-Organized Multi-Camera Network for a Fast and Easy Deployment of Ubiquitous Robots in Unknown Environments

Abstract

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposal.

Particle filter robot localisation through robust fusion of laser, WiFi, compass, and a network of external cameras

“…It is very conservative, therefore it almost never produces a false positive (false robot detection). This comes at the cost of a lower true positive detection rate [6].…”

“…Our cameras use a robot detection algorithm based on active markers (LED lights) that we place on our robots [6]. This algorithm allows the cameras to detect multiple robots very robustly in crowded environments and in real time.…”

“…In this early work, the robot navigation depended completely on the cameras, but in our current paper the robot itself is able to estimate its position (therefore it can navigate autonomously). Moreover, in [6] the FOVs of neighbour cameras were required to overlap. This restriction was erased in [5], were the robot would construct local maps to connect cameras whose FOV would not overlap.…”

“…In [6] we developed an earlier version of the camera network used in this paper. These cameras were able to: (1) detect events that required the presence of robots, and (2) send movement commands to guide the robot towards these events throughout the network of cameras.…”

“…1) that detect and inform the robot about situations that require its presence (e.g. people that might need information about the event) [5,6]. This way, our tour guide robot is aware of situations that happen outside of its range of perception.…”