This paper describes the motivation, design and implementation of a Perception Validation System (PVS), a system for measuring the outdoor perception performance of an autonomous vehicle. The PVS relies on using large amounts of real world data and ground truth information to quantify performance aspects such as the rate of false positive or false negative detections of an obstacle detection system. Our system relies on a relational database infrastructure to achieve a high degree of flexibility in the type of analyses it can support. We discuss the main steps required for going from raw data to numerical estimates describing the performance of the perception system, including the generation of ground truth information and the safe speed metric we found to be most useful for comparing the perception system's outputs to the ground truth data. We present results illustrating some of the analyses that can be completed using the Perception Validation System.

We propose a generalized region-based approach to multi-target tracking, which is applicable to structured and unstructured environments. In this approach each robot constructs virtual regions based on the latest tracking information from other robots. Without pre-partitioned region information, each robot independently estimates the most urgent region that needs to be visited. The idea is for robots to coarsely estimate where the targets are present, and to navigate there. A multi-robot system to track moving objects outdoors has been designed using this approach in order to validate the idea. The performance of the individual motion tracker; and the cooperative tracking behaviors is evaluated through experiments with different robot bases (a helicopter, a Segway RMP, and a Pioneer) and in simulation. Experimental results indicate that robots are able to distribute themselves appropriately in response to target movement.

Autonomous mobile robots are constrained in their long-term functionality due to a limited on-board power supply. Typically, rechargeable batteries are utilized that may only provide a few hours of peak usage before recharging is necessary. Recharging requires a robot to be taken offline, and attached to a battery charger via human intervention. This is unacceptable in environments where long-term autonomous capabilities are necessary. We present a method to provide long-term autonomy by implementing autonomous recharging. A recharging station design is presented, consisting of a stationary docking station and a docking mechanism mounted to a Pioneer 2DX robot. The docking station and robot docking mechanism are designed to work together, providing a mechanical and electrical connection between the charging system and the robot. Algorithms are implemented to monitor the battery voltage and control the docking procedure, as well as account for any errors that may occur. Initial experiments that demonstrate the validity of the approach and design are presented.

This paper addresses the problem of tracking multiple targets using a network of communicating robots and stationary sensors. We introduce a region-based approach which controls robot deployment at two levels. A coarse deployment controller distributes robots across regions using a topological map and density estimates, and a target-following controller attempts to maximize the number of tracked targets within a region. A behavior-based system is presented implementing the region-based approach. Intensive simulations were performed to investigate the correlation between our approach and the degree of occlusion in the environment. The region-based approach shows better performance than a 'naive' local-following strategy when the environment has significant occlusion. We performed real-robot experiments to validate the system. These experiments open up a new line of research, which suggests that an optimal ratio of robots to stationary sensors may exist for a given environment with certain occlusion characteristics.