George Kantor

A dynamically stable single-wheeled mobile robot with inverse mouse-ball drive

Multi-wheel statically-stable mobile robots tall enough to interact meaningfully with people must have low centers of gravity, wide bases of support, and low accelerations to avoid tipping over. These conditions present a number of performance limitations. Accordingly, we are developing an inverse of this type of mobile robot that is the height, width, and weight of a person, having a high center of gravity, that balances dynamically on a single spherical wheel. Unlike balancing 2-wheel platforms which must turn before driving in some direction, the single-wheel robot can move directly in any direction. We present the overall design, actuator mechanism based on an inverse mouse-ball drive, control system, and initial results including dynamic balancing, station keeping, and point-to-point motion

Real-Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels

We describe a simultaneous localization and mapping (SLAM) method for a hovering underwater vehicle that will explore underwater caves and tunnels, a true three-dimensional (3D) environment. Our method consists of a Rao-Blackwellized particle filter with a 3D evidence grid map representation. We describe a procedure for dynamically adjusting the number of particles to provide real-time performance. We also describe how we adjust the particle filter prediction step to accommodate sensor degradation or failure. We present an efficient octree data structure that makes it feasible to maintain the hundreds of maps needed by the particle filter to accurately model large environments. This octree structure can exploit spatial locality and temporal shared ancestry between particles to reduce the processing and storage requirements. To test our SLAM method, we utilize data collected with manually deployed sonar mapping vehicles in the Wakulla Springs cave system in Florida and the Sistema Zacato´n in Mexico, as well as data collected by the DEPTHX vehicle in the test tank at the Austin Applied Research Laboratory. We demonstrate our mapping and localization approach with these real-world datasets.

Preliminary results in range-only localization and mapping

This paper presents methods of localization using cooperating landmarks (beacons) that provide the ability to measure range only. Recent advances in radio frequency technology make it possible to measure range between inexpensive beacons and a transponder Such a method has tremendous benefit since line of sight is not required between the beacons and the transponder and because the data association problem can be completely avoided. If the positions of the beacons are known, measurements from multiple beacons can be combined using probability grids to provide an accurate estimate of robot location. This estimate can be improved by using Monte Carlo techniques and Kalman filters to incorporate odometry data. Similar methods can be used to solve the simultaneous localization and mapping problem (SLAM) when beacon locations are uncertain. Experimental results are presented for robot localization. Tracking and SLAM algorithms are demonstrated in simulation.

Range-only SLAM for robots operating cooperatively with sensor networks

A mobile robot we have developed is equipped with sensors to measure range to landmarks and can simultaneously localize itself as well as locate the landmarks. This modality is useful in those cases where environmental conditions preclude measurement of bearing (typically done optically) to landmarks. Here we extend the paradigm to consider the case where the landmarks (nodes of a sensor network) are able to measure range to each other. We show how the two capabilities are complimentary in being able to achieve a map of the landmarks and to provide localization for the moving robot. We present recent results with experiments on a robot operating in a randomly arranged network of nodes that can communicate via radio and range to each other using sonar. We find that incorporation of inter-node measurements helps reduce drift in positioning as well as leads to faster convergence of the map of the nodes. We find that addition of a mobile node makes the SLAM feasible in a sparsely connected network of nodes

Distributed Search and Rescue with Robot and Sensor Teams

We develop a network of distributed mobile sensor systems as a solution to the emergency response problem. The mobile sensors are inside a building and they form a connected ad-hoc network. We discuss cooperative localization algorithms for these nodes. The sensors collect temperature data and run a distributed algorithm to assemble a temperature gradient. The mobile nodes are controlled to navigate using this temperature gradient. We also discuss how such networks can assist human users to find an exit. We have conducted an experiment to at a facility used to train firefighters to understand the environment and to test component technology. Results from experiments at this facility as well as simulations are presented here.

State transition, balancing, station keeping, and yaw control for a dynamically stable single spherical wheel mobile robot

Unlike statically stable wheeled mobile robots, dynamically stable mobile robots can have higher centers of gravity, smaller bases of support and can be tall and thin resembling the shape of an adult human. This paper concerns the ballbot mobile robot, which balances dynamically on a single spherical wheel. The ballbot is omni-directional and can also rotate about its vertical axis (yaw motion). It uses a triad of legs to remain statically stable when powered off. This paper presents the evolved design with a four-motor inverse mouse-ball drive, yaw drive, leg drive, control system, and results including dynamic balancing, station keeping, yaw motion while balancing, and automatic transition between statically stable and dynamically stable states.

Trajectory planning and control of an underactuated dynamically stable single spherical wheeled mobile robot

The ballbot is a dynamically stable mobile robot that moves on a single spherical wheel and is capable of omnidirectional movement. The ballbot is an underactuated system with nonholonomic dynamic constraints. The authors propose an offline trajectory planning algorithm that provides a class of parametric trajectories to the unactuated joint in order to reach desired static configurations of the system with regard to the dynamic constraint. The parameters of the trajectories are obtained using optimization techniques. A feedback controller is proposed that ensures accurate trajectory tracking. The trajectory planning algorithm and tracking controller are validated experimentally. The authors also extend the offline trajectory planning algorithm to a generalized case of motion between non-static configurations.

One Is Enough

We postulate that multi-wheel statically-stable mobile robots for operation in human environments are an evolutionary dead end. Robots of this class tall enough to interact meaningfully with people must have low centers of gravity, overly wide bases of support, and very low accelerations to avoid tipping over. Accordingly, we are developing an inverse of this type of mobile robot that is the height, width, and weight of a person, having a high center of gravity, that balances dynamically on a single spherical wheel. Unlike balancing 2-wheel platforms which must turn before driving in some direction, the single-wheel robot can move directly in any direction. We present the overall design, actuator mechanism based on an inverse mouse-ball drive, control system, and initial results including dynamic balancing, station keeping, and point-to-point motion.

Experimental results in range-only localization with radio

We present an early experimental result toward solving the localization problem with range-only sensors. We perform an experiment in which a mobile robot localizes using dead reckoning and range measurements to stationary radio-frequency beacons in its environment, incorporating the range measurements into the position estimate using a Kalman filter. This data set involves over 20,000 range readings to surveyed beacons while a robot moved continuously over a path for nearly 1 hour. Careful groundtruth accurate to a few centimeters was recorded during this motion. We show the improvement of the robots position estimate over dead reckoning even when the range readings are very noisy. We extend this approach to the problem of simultaneous localization and mapping (SLAM), localizing both the robot and tag positions from noisy initial estimates.

Hierarchical simultaneous localization and mapping

This paper presents a novel method of combining topological and feature-based mapping strategies to create a hierarchical approach to simultaneous localization and mapping (SLAM). More than simply running both processes in parallel, we use the topological mapping procedure to organize local feature-based methods. The result is an autonomous exploration and mapping strategy that scales well to large environments and higher dimensions while confronting the issue of obstacle avoidance. We have obtained successful results of our approach in an area spanning 5000 square meters.