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[Advanced Control Systems Group] [Autonomous Mobile Robotics Group]





Control of Multi-Robot Systems in Intelligent Spaces

Control of Multi-Robot Systems in Intelligent Spaces


Basic concept

Recent advances in computing, networking, sensor technology and robotics allows us to create a more convenient environment for humans. In that context the Intelligent Space was proposed as a space that has ubiquitous distributed sensory intelligence and actuators for manipulating the space. In that sense, intelligent space is an environment with distributed sensors (e.g. cameras, microphones, sonars) and actuators (e.g. mobile robots, manipulators), with the purpose of providing various advanced services to the space users. The sensors are used for the detection and tracking of objects and persons in the space and for receiving orders from space users, and the actuators are used for providing services to the users, both physical and informative (e.g. carrying or delivering loads).

In context of using mobile robot as an actuator, in numerous applications it could be more effective to use multiple simpler and cheaper mobile robots instead of one complex and expensive robot. There are many concepts in control of multi-robot systems, and we investigate the concept of multi-robot systems in intelligent spaces. In terms of control and coordination of higher number of mobile robots, the intelligent space offers several advantages compared to the use of independent autonomous robots: a) it is not necessary to build an environment map, b) in every time instance it is possible to uniquely determine the position of the robot, c) even in dynamic environment, it is possible to find globally optimal path, because the sensor system has always perception of the whole environment, d) the whole intelligence is located in the environment, instead in the robots, so that robots share common resources and this results with lower system price.

On the other hand, sensors, both stationary and mobile, provide information about the state of the space. For example, sensors are used to detect the location and pose of humans and position of robots in the space. The availability of this information enables the implementation of various personalized services. The information from external sensors can also be utilized in mobile robot control tasks, which gives an advantage in comparison with the standard approach with only onboard sensors. In this topic, our research is mainly concerned with global vision based robot position tracking algorithms, and also motion planning and motion control algorithms, with the goal of cooperative performing of more complex tasks.

Vision system for multiple mobile robots tracking

Our vision system consists of multiple distributed cameras fixed above the robots, which track robots poses, and one or multiple distributed computers connected via a high speed communication bus, which execute image processing algorithms as well as decision making and robot control algorithms (see figure).

To achieve good precision of robot pose measuring, we designed a special robot mark that consists of a color patch for detection, and a black-white marker for measurement. This enables us to use subpixel edge detection for measurement of robot pose. In this way we achieved outstanding measurement precision with position standard deviation of about 0.007 pixels i.e. 0.03 mm, and angle standard deviation of about 0.08 degrees. Moreover, algorithm is heavily optimized so that execution time to measure poses of 5 robots in global search mode is about 3 ms. Where possible, algorithm uses advantage of local search mode in which case the execution time to measure poses of 5 robots is only about 1.5 ms. Therefore, we are able to use high framerates (currently we use 80 fps), which enables reliable and precise control of robots.

For detailed description of this vision system see our paper "Robust and accurate global vision system for real time tracking of multiple mobile robots". (HTML, PDF)

Vision system for people tracking

We also investigate algorithms dedicated for tracking of humans inside intelligent spaces, with special emphasis on reliable human detection and robustness to illumination changes. Some initial results have been already accomplished in this field, and now we aim to improve detection robustness.


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