Robert Grabowski

Heterogeneous Teams of Modular Robots for Mapping and Exploration

In this article, we present the design of a team of heterogeneous, centimeter-scale robots that collaborate to map and explore unknown environments. The robots, called Millibots, are configured from modular components that include sonar and IR sensors, camera, communication, computation, and mobility modules. Robots with different configurations use their special capabilities collaboratively to accomplish a given task. For mapping and exploration with multiple robots, it is critical to know the relative positions of each robot with respect to the others. We have developed a novel localization system that uses sonar-based distance measurements to determine the positions of all the robots in the group. With their positions known, we use an occupancy grid Bayesian mapping algorithm to combine the sensor data from multiple robots with different sensing modalities. Finally, we present the results of several mapping experiments conducted by a user-guided team of five robots operating in a room containing multiple obstacles.

Autonomous exploration via regions of interest

We describe a new paradigm for exploration of unknown spaces based on maximizing the understanding of obstacles rather than the exposure of free space. We look at the interaction between multiple sensor readings and how they combine to resolve obstacles. Taking a next best view approach, we generate an inverse sensor model that identifies regions in space where a new sensor reading has maximal utility with respect to increasing the resolution of that reading. Fusion of multiple models is exploited to generate regions of interest that direct exploration in such a way as to maximize the robots understanding of its space. These techniques are applied to a team of small robots called Millibots.

Localization techniques for a team of small robots

Knowledge of position in the context of its surrounding is necessary for robots to build maps and develop path plans. Limitations in odometry and the lack of a priori knowledge reduce the effectiveness of a single robot to retain a sense of position for any extended duration. The problem is only compounded when the scale of the robot is reduced. However, by employing multiple robots we can exploit their distributed nature to provide an external context in which to evaluate sensor readings for mapping and localization. We have designed a team of centimeter-sized robots that coordinate sensing and action to establish and maintain position as they move throughout space. By utilizing low-cost ultrasonic sensors, the team is able to measure the range between each robot pair. We pose these measurements in terms of a position likelihood and combine them to find a global solution that best maximizes the position likelihood of each robot. We also address a unique multipath interference mode that arises as a direct result of the reduced scale of the robot team. We present our experiences with localization and control of a small robot team.

Development and deployment of a line of sight virtual sensor for heterogeneous teams

For a team of cooperating robots, geometry plays a vital role in operation. Knowledge of line of sight to local obstacles and adjacent teammates is critical in both the movement and planning stages to avoid collisions, maintain formation and localize the team. However, determining if other robots are within the line of sight of one another is difficult with existing sensor platforms - especially as the scale of the robot is reduced. We describe a method of exploiting collective team information to generate a virtual sensor that provides line of sight determination, greater range and resolution and the ability to generalize local sensing. We develop this sensor and apply it to the control of a tightly coupled, resource-limited robot team called Millibots.

An enhanced occupancy map for exploration via pose separation

We develop a new occupancy map that respects the role of the sensor measurement bearing and how it relates to the resolution of the existing occupancy map. We borrow an idea from Konolige for recording and tracking, in an occupancy-like map, the bearing at which sensor readings originate with respect to a given cell. Our specific contribution is in the way we process the sensor pose information, which is the bearing of the sensor readings when it indicates the presence of an obstacle in a particular cell. For each cell in the occupancy map, we calculate the greatest separation of incident poses, and then store that information in a new two-dimensional array called a pose map. A cell in the pose map measures the quality of information contained in the corresponding cell of the occupancy map. We merge the new pose map with the existing map to generate an enhanced occupancy map. Exploration plans derived from the enhanced occupancy map are more efficient and complete in that they do not guide the robot around phantom obstacles nor incorrectly classify narrow openings as closed commonly found in conventional occupancy maps.