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We consider the problem of controlling vehicles to visit multiple targets and obtain rewards associated with each target with the added requirement that two or more vehicles are required to be present in the vicinity of each target in order to collect the associated reward. The environment is uncertain and targets may appear or disappear in real time. In view of the problem complexity, we focus on...
Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can be difficult when completing complex tasks. Introducing models into the motion planning process can produce paths the vehicle can feasibly traverse. As a result, Sampling-Based...
This paper presents a novel mobile vehicle navigation algorithm based on the stability analysis of the model predictive control approach. The energy-shaping technique is performed with the navigation function to obtain a new virtual vehicle model that generates candidate feasible trajectories for the motion planner. Stability of the nonlinear model predictive control system is obtained by the passivity...
This paper addresses the problem of path generation and motion control for the autonomous maneuver of a farm vehicle with a trailed implement in headland. A reverse turn planner is firstly investigated, based on primitives connected together to easily generate the reference motion. Then, both steering and speed control algorithms are presented to accurately guide the vehicle-trailer system. They are...
This paper addresses the problem of path generation and motion control for the autonomous maneuvers of a farm vehicle in headland. A reverse turn planner is firstly investigated, based on primitives connected together to easily generate the reference motion. Then, both steering and speed control algorithms are presented to accurately guide the vehicle. They are based on a kinematic model extended...
Path planning algorithms that incorporate risk and uncertainty need to be able to predict the evolution of path-following error statistics for each candidate plan. We present an analytic method to predict the evolving error statistics of a holonomic vehicle following a reference trajectory in a planar environment. This method is faster than integrating the plant through time or performing a Monte...
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