We present an auction-based method for the allocation of tasks to a group of robots. The robots operate in a 2D environment for which they have a map. Tasks are locations in the map that have to be visited by the robots. Unexpected obstacles and other delays may prevent a robot from being able to complete its allocated tasks. Therefore tasks not yet achieved are rebid every time a robot accomplishes a task. This provides an opportunity to improve the allocation of the remaining tasks and to reduce the overall task completion time. We present experimental results that we have obtained in simulation using Player/Stage with this task allocation mechanism

Urban search and rescue (USAR) is the rescue of people trapped by disasters involving the collapse of man-made structures. Introducing robots to USAR has the potential to increase the speed and effectiveness of rescue operations considerably and make them safer for rescuers. Robots have been used in recent rescue operations, including the WTC terrorist attack, leading to the identification of several problems in robot-assisted search and rescue. This paper proposes a solution to one such problem that has been encountered repeatedly in the use of robots for USAR: the determination of the scale of unrecognizable objects in the camera views from the robot. We describe a procedure that would allow the extraction of size and orientation information using a laser pointer and camera mounted on a robot, and experiments that verify the effectiveness of this procedure have been shown.