Unmanned Ground Vehicles (UGV) that possess agility, or the ability to quickly change directions without
a significant loss in speed, would have several advantages in field operations over conventional UGVs. The
agile UGVs would have greater maneuverability in cluttered environments and improved obstacle avoidance
capabilities. The UGVs would also be able to better recover from unwanted dynamic behaviors. This paper
presents a novel method of increasing UGV agility by actively altering the location of the vehicle's center of mass
during locomotion. This allows the vehicle to execute extreme dynamic maneuvers by controlling the normal
force acting on the wheels. A theoretical basis for this phenomenon is presented and experimental results are
shown that validate the approach.
Climbing animal's feet use combinations of interlocking and bonding mechanisms in a staggering array of designs. The most successful climbers' feet exhibit a complex hierarchy of varied mechanical structures at multiple scales, combining small appendages that generate shear or adhesive forces with compliant suspension systems that promote intimate contact with surfaces. Recent progress is presented in mechanical and materials design that integrates novel dry adhesive and microspine structures mounted on passively compliant suspensions into successively improved generations of feet targeted at the RiSE (Robots in Scansorial Environments) family of climbing robots. The current version can ascend 90° carpeted, cork covered and a growing range of stucco surfaces in the quasi-static regime. Specifications of a "public interface" for integrating a broad range of synthetic appendages into the foot assemblies are presented in the hopes of encouraging as large as possible a community of MEMs and Nanomaterials designers to submit adhesive or friction enhancing materials for operational tests using the robot.
Mobile robots have important applications in high speed, rough-terrain scenarios. In these scenarios, unexpected and hazardous situations can occur that require rapid hazard avoidance maneuvers. At high speeds, there is limited time to perform re-planning based on detailed vehicle and terrain models. Furthermore, detailed models often do not accurately predict the robot’s performance due to model parameter and sensor uncertainty. This paper presents a method for high speed hazard avoidance. The method is based on the concept of the trajectory space, which is a compact model-based representation of a robot’s dynamic performance limits in uneven, natural terrain. A Monte Carlo method for analyzing system performance despite model parameter uncertainty is briefly presented, and its integration with the trajectory space is discussed. Simulation results for the hazard avoidance algorithm are presented and demonstrate the effectiveness of the method.