We report a new trajectory tracking and balancing control algorithm for an autonomous motorcycle. Building on the existing modeling work of a bicycle, the new dynamic model of the autonomous motorcycle considers the bicycle caster angle and captures the steering effect on the vehicle tracking and balancing. The trajectory tracking control takes an external/internal model decomposition approach. A nonlinear controller is designed to handle the vehicle balancing. The motorcycle balancing is guaranteed by the system internal equilibria calculation and by the trajectory and system dynamics requirements. The proposed control system is validated by numerical simulations, and is based on a real prototype motorcycle system

We report our development of a vision-based motion planning system for an autonomous motorcycle designed for desert terrain, where uniform road surface and lane markings are not present. The motion planning is based on a vision vector space (V2-Space), which is an unitary vector set that represents local collision-free directions in the image coordinate system, V2-Space is constructed by extracting the vectors based on the similarity of adjacent pixels, which captures both the color information and the directional information from prior vehicle tire tracks and pedestrian footsteps. We report how V2-Space is constructed to reduce the impact of varying lighting conditions in outdoor environments. We also show how V2-Space can be used to incorporate vehicle kinematic, dynamic, and time-delay constraints in motion planning to fit the highly dynamic requirements of the motorcycle. The combined algorithm of the V2-Space construction and the motion planning runs in O(n) time, where n is the number of pixels in the captured image. Experiments show that our algorithm outputs correct robot motion commands more than 90% of the time

We describe a networked teleoperation system that allows groups of participants to collaboratively explore live remote environments. Participants collaborate using a spatial dynamic voting (SDV) interface that allows them to vote on a sequence of images via a network such as the Internet. The SDV interface runs on each client computer and communicates with a central server that collects, displays, and analyzes time sequences of spatial votes. The results are conveyed to the "tele-actor", a skilled human with cameras and microphones who navigates and performs actions in the remote environment. This paper formulates analysis in terms of spatial interest functions and consensus regions, and presents system architecture, interface, and algorithms for processing voting data.

Internet-based "online robots" now provide public access to remote locations such as museums and laboratories. The Tele-Actor is a collaborative online teleoperation system for distance learning that allows many students to simultaneously share control of a single mobile resource. Our goal is to preserve the educational advantages of field trips without the drawbacks of group travel. We propose the "spatial dynamic voting" (SDV) interface for multiple operator single robot (MOSR) teleoperation. The SDV collects, displays, and analyzes a sequence of spatial votes from multiple online operators at their Internet browsers. The votes drive the motion of a single mobile robot or human "Tele-Actor". The paper describes Version 3.0 of the system architecture, SDV interface, algorithms for automated goal selection, and metrics for collaboration and leadership. We report results from a July 2001 field test with 56 remote users.