Patricia Kristine Sheridan

Predictive Guidance-Based Navigation for Mobile Robots: A Novel Strategy for Target Interception on Realistic Terrains

This paper presents a novel online trajectory planning method for the autonomous robotic interception of mobile targets in the presence of dynamic obstacles. The objective is time-optimal position and velocity matching (also referred to as rendezvous) while traversing realistic terrains with uneven topologies. The primary novelty of the proposed interception method lies in its ability to minimize rendezvous time with the target, as well as energy consumption, by directly considering the dynamics of the obstacles and the target while accurately determining a feasible way to travel through the realistic terrain. This objective is achieved by computing rendezvous maneuvers using an advanced predictive guidance law. The method is designed to effectively cope with maneuvering targets/obstacles by predicting their future velocities and accelerations. Obstacle avoidance and terrain navigation are seamlessly integrated. Extensive simulation and experimental analyses, some of which are reported in this paper, have clearly demonstrated the time efficiency of the proposed rendezvous method.

On-line task allocation for the robotic interception of multiple targets in dynamic settings

This paper presents a novel on-line optimal task-allocation methodology for the interception of a group of mobile targets by a team of robotic pursuers. The mobile targets are highly maneuverable. A rolling-horizon concept is used to increase the depth of the online search for the optimal solution. Guidance theory is utilized to navigate each pursuer independently toward its assigned target. Extensive simulations and experiments have confirmed the proposed generic methodology to be efficient in determining on-line assignments for minimum total interception time.