Static and Dynamic Path Planning Using Incremental Heuristic Search

TL;DR

This paper introduces an incremental heuristic search method for static and dynamic path planning in environments with static and moving obstacles, specifically tailored for car-like agents, improving trajectory feasibility and efficiency.

Contribution

It proposes a novel 3D search scheme in position and speed space for dynamic environments, addressing limitations of traditional configuration-time space approaches.

Findings

Efficiently produces trajectories respecting car dynamics.

The new scheme outperforms traditional methods in dynamic environments.

Trajectories are generated with bounded optimality.

Abstract

Path planning is an important component in any highly automated vehicle system. In this report, the general problem of path planning is considered first in partially known static environments where only static obstacles are present but the layout of the environment is changing as the agent acquires new information. Attention is then given to the problem of path planning in dynamic environments where there are moving obstacles in addition to the static ones. Specifically, a 2D car-like agent traversing in a 2D environment was considered. It was found that the traditional configuration-time space approach is unsuitable for producing trajectories consistent with the dynamic constraints of a car. A novel scheme is then suggested where the state space is 4D consisting of position, speed and time but the search is done in the 3D space composed by position and speed. Simulation tests shows that the new scheme is capable of efficiently producing trajectories respecting the dynamic constraint of a car-like agent with a bound on their optimality.