Constrained Path Planning of Unmanned Vehicles

摘要

The application of unmanned system performing large-scale tasks, for instance, long-term surveillance/reconnaissance, large area sensing/mapping, and long distance materials handling is a relatively new and exciting topic. However, developing a practical system is still challenging due to complex models and hardware restriction. This manuscript explores various path planning missions from a more realistic perspective, such as point-to-point obstacle avoiding, multi-targets trajectory finding, informative motion planning, and multi-Hamiltonian Path Problem (mHPP) with two types of unmanned vehicles, Unmanned Ariel Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). These problems are formulated as classical optimization problems with constraints representing the environment and kinematic limitations, and then solved by proposed numerical or heuristic optimization approaches. The selected methods are used to handle nonlinear, discontinuous, and multi-objective formulations of the constrained mission planning problems. The feasibility and effectiveness of the proposed algorithms are inspected by the performance and comparison with other proposed methods in literature. The resulting simulations and experimental tests obtained from all the methods are demonstrated and discussed.