Path-planning with waiting in spatiotemporally-varying threat fields

Area of Science:

• Robotics
• Artificial Intelligence
• Computational Geometry

Background:

• Optimal path planning is crucial for vehicle navigation in dynamic environments.
• Incorporating waiting (hovering, parking, loitering) into path planning complicates computations but may reduce costs.
• Time-varying spatial fields introduce complexities in determining traversal costs.

Purpose of the Study:

• To analyze the trade-off between computational complexity and cost reduction when allowing waiting in path planning.
• To identify conditions in threat fields where waiting offers no cost benefit.
• To investigate the efficiency of vehicle-centric multiresolution grids for path planning with waiting.

Main Methods:

• Path planning algorithms were evaluated on uniform grids and vehicle-centric multiresolution grids.
• Numerical studies were conducted using time-varying spatial threat fields.
• Wavelet-based multiresolution decomposition was employed for grid evaluation.

Main Results:

• Specific threat field characteristics were identified that favor paths involving waiting.
• A local condition was established to determine when waiting provides no cost reduction.
• This condition enables pruning of search trees, significantly reducing computation time without substantial suboptimality.
• Vehicle-centric multiresolution grids minimize the computational overhead of allowing waiting.

Conclusions:

• Allowing waiting in path planning can be computationally intensive but offers potential cost savings.
• Identifying conditions that preclude waiting benefits optimizes path planning efficiency.
• Vehicle-centric multiresolution grids effectively manage the computational cost associated with waiting in path planning.