Boyoon Jung

Staying alive: a docking station for autonomous robot recharging

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.

Adaptive Teams of Autonomous Aerial and Ground Robots for Situational Awareness

This is a preprint of an article accepted for publication in the Journal of Field Robotics, copyright 2007. Journal of Field Robotics 24(11), 991–1014 (2007)

Real-time Motion Tracking from a Mobile Robot

A mobile robot needs to perceive the motions of external objects to perform tasks successfully in a dynamic environment. We propose a set of algorithms for multiple motion tracking from a mobile robot equipped with a monocular camera and a laser rangefinder. The key challenges are 1. to compensate the ego-motion of the robot for external motion detection, and 2. to cope with transient and structural noise for robust motion tracking. In our algorithms, the robot ego-motion is directly estimated using corresponding feature sets in two consecutive images, and the position and velocity of a moving object is estimated in image space using multiple particle filters. The estimates are fused with the depth information from the laser rangefinder to estimate the partial 3D position. The proposed algorithms have been tested with various configurations in outdoor environments. The algorithms were deployed on three different platforms; it was shown that various type of ego-motion were successfully eliminated and the particle filters were able to track motions robustly. The real-time capability of the tracking algorithm was demonstrated by integrating it into a robot control loop.

Cooperative Multi-robot Target Tracking

Target tracking performance can be improved by using multiple robot trackers, but this requires a coordinated motion strategy among the robots. We propose an algorithm based on treating the densities of robots and targets as properties of the environment in which they are embedded. By suitably manipulating these densities a control law for each robot is proposed. The proposed algorithm has been tested through intensive simulations and a realrobot experiment. First, two different versions of the approach were evaluated by studying the performance change as the communication range among robots varies. The results showed that our treatment of the coordination problem is effective and efficient. Second, the developed system was tested on two Segway RMP robots, and the behaviors of the robots in a cooperative tracking experiment provide evidence that the proposed method controls multiple robots appropriately according to the target distribution change.

A generalized region-based approach for multi-target tracking in outdoor environments

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.

Cooperative tracking using mobile robots and environment-embedded, networked sensors

We study the target tracking problem using multiple, environment-embedded, stationary sensors and mobile robots. An architecture for robot motion coordination is presented which exploits a shared topological map of the environment. The stationary sensors and robots maintain region-based density estimates which are used to guide the robots to parts of the environment where unobserved targets may be present. Experiments in simulation show that the region-based approach works better than a naive target following approach when the number of targets in the environment is high.

PVS: A system for large scale outdoor perception performance evaluation

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.

Robot box-pushing with environment-embedded sensors

We address the problem of detecting and pushing stationary objects in a planar environment by using an environment-embedded sensor network and a simple mobile robot. The stationary sensors are used to detect pushable objects. The robot pushes a detected object based on pose information obtained from the sensors over a wireless network. We show that the accuracy of the push operation is correlated to the number of sensors used to determine object pose.