In this paper, we present our research on the control of a mobile robot for indoor reconnaissance missions. Based on previous work concerning our robot control architecture HARPIC, we have developed a man machine interface and software components that allow a human operator to control a robot at different levels of autonomy. This work aims at studying how a robot could be helpful in indoor
reconnaissance and surveillance missions in hostile environment. In
such missions, since a soldier faces many threats and must protect himself while looking around and holding his weapon, he cannot devote his attention to the teleoperation of the robot. Moreover, robots are not yet able to conduct complex missions in a fully autonomous mode. Thus, in a pragmatic way, we have built a software that allows dynamic swapping between control modes (manual, safeguarded and behavior-based) while automatically performing map building and localization of the robot. It also includes surveillance functions like movement detection and is designed for
multirobot extensions. We first describe the design of our agent-based robot control architecture and discuss the various ways to control and interact with a robot. The main modules and functionalities implementing those ideas in our architecture are
detailed. More precisely, we show how we combine manual controls,
obstacle avoidance, wall and corridor following, way point and planned travelling. Some experiments on a Pioneer robot equipped with various sensors are presented. Finally, we suggest some promising directions for the development of robots and user interfaces for hostile environment and discuss our planned future improvements.
In a previous presentation at AeroSense 2002, we described a methodology to assess the results of image processing algorithms for ill-structured road detection and tracking. In this paper, we present our first application of this methodology on sixedge detectors and a database counting about 20,000 images.
Our evaluation approach is based on the use of video image sequences, ground truth - reference results established by human experts - and assessment metrics which measure the quality of the image processing
results. We need a quantitative, comparative and repetitive evaluation of many algorithms in order to direct future developments.
The main scope of this paper consists in presenting the lessons learned from applying our methodology. More precisely, we describe the assessment metrics, the algorithms and the database. Then we describe how we manage to extract the qualities and weaknesses of each algorithm and to establish a global scoring. The insight we gain
for the definition of assessment metrics is also presented.
Finally, we suggest some promising directions for the development of road tracking algorithms and complementarities that must be sought after. To conclude, we describe future improvements for the database constitution, the assessment tools and the overall methodology.
In this paper, we present a methodology to assess the results of image processing algorithms for unstructured road edges detection. We aim at performing a quantitative, comparative and repetitive evaluation of numerous algorithms in order to direct our future developments in navigation algorithms for military unmanned vehicles. The main scope of this paper is the constitution of this database and the definition of the assessment metrics.
In this paper, we introduce an autonomous map-building technique for mobile robots, based on combinatorial maps. Existing representations of the environment traditionally fall into two distinct categories: metric or topological. Topological approaches are usually well-adapted to global planning and navigation tasks. However, metric maps are easier to read for a human operator and they are better suited to precise robot positioning. Among them, we can distinguish feature-based and area-based maps. Our model enables us to combine the orthogonal strengths of these various representations in a rather compact and efficient way, using an algebraic tool named combinatorial map. We propose a global framework to deal with topological and geometric uncertainties, and a whole strategy for the autonomous generation of 2D combinatorial maps of the environment. The main innovation lies in the way local free space is fused into the global model in order to correct both the position and the topology of obstacles. We extend the notion of discrete and regular occupancy grid to any kind of polygonal subdivision, with cells of variable shapes and dimensions. To conclude, we describe experiments conducted with a real-world robot moving about within a well-structured indoor environment.