This paper presents an approach to accurate and scalable multiple-model (MM) state estimation for hybrid systems with intermittent, cyclical, multimodal dynamics. The approach consists of using discrete-state estimation to identify a system's dynamical and behavioral contexts and determine which motion models appropriately represent current dynamics and which individual and MM filters are appropriate for state estimation. Furthermore, the heirarchical structure of the dynamics is explicitly encoded, which enables detection not only of rapid transitions between motion models but of higher level behavioral transitions as well. This improves the accuracy and scalability of conventional MM state estimation, which is demonstrated experimentally on a mobile robot that exhibits fast-switching, multimodal dynamics.

In this paper we present our efforts to design a system for feeding back useful haptic information from a highly dynamic running robot to a remote operator using a haptic device. Without adding additional sensors, the legs of the robot are used as feelers to give the operator the capability to both explore and manipulate the robot's environment and to gather meaningful information about properties not captured by visual feedback like weight, movability and structure of an encountered object. We show the capabilities of the system in a user study with both trained and untrained operators.

This paper presents an approach to accurate and scalable multiple-model state estimation for hybrid systems with intermittent, multi-modal dynamics. The approach consists of using discrete-state estimation to identify a system’s behavioral context and determine which motion models appropriately represent current dynamics, and which multiple-model filters are appropriate for state estimation. This improves the accuracy and scalability of conventional multiple-model state estimation. This approach is validated experimentally on a mobile robot that exhibits multi-modal dynamics.

This paper proposes an approach to robust state estimation for mobile robots with intermittent dynamics. The approach consists of identifying the robot’s mode of operation by classifying the output of onboard sensors into mode-specific contexts. The underlying technique seeks to efficiently use available sensor information to enable accurate, high-bandwidth mode identification. Context classification is combined with multiple-model filtering in order to significantly improve the accuracy of state estimates for hybrid systems. This approach is validated in simulation and shown experimentally to produce accurate estimates on a jogging robot using low-cost sensors.

This paper presents preliminary development of autonomous sensor-guided behaviors for a six-legged dynamical robot (RHex). The behaviors represent the exteroceptive closed-loop locomotion strategies for this system. Simple motion models for RHex are described and used to deploy controllers for inertial straight line locomotion and visual line following. Results are experimentally validated on the robot.

Future space facilities that could power our planet and expand our horizons will differ vastly from the satellites and space stations familiar today. Characterized by their immense size and the difficulties of human construction in orbit, future space facilities will be assembled in part by robots. This paper profiles Skyworker, a prototype assembly, inspection, and maintenance (AIM) robot designed for large mass payload transport and assembly tasks. Skyworker is an attached mobile manipulator (AMM) capable of walking and working on the structure it is building.