Path Planning for Autonomous Systems Design: A Focus Genetic Algorithm for Complex Environments

Abstract

Abstract Path planning has been a hot research topic in robotics and is a vital functionality for autonomous systems. As the time complexity of traditional path planning algorithms grows rapidly with the complexity of the problem, evolutionary algorithms are widely applied for their near-optimal solutions. However, evolutionary algorithms can be trapped in a local optimum or converge to infeasible solutions, especially for large search spaces. As the problem scale increases, evolutionary algorithms often cannot find feasible solutions with random exploration, making it extremely challenging to solve long-range path-planning problems in environments with obstacles of various shapes and sizes. For long-range path planning of an autonomous ship, the current downsampling map approach may result in the disappearance of rivers and make the problem unsolvable. This paper introduces a novel area-based collision assessment method for genetic algorithm (GA) that can always converge to feasible solutions with various waypoints in large-scale and obstacle-filled environments. Waypoint-based crossover and mutation operators are developed to allow GA to modify the length of the solution during planning. To avoid the premature problem of GA, the mutation process is replaced by a self-improving process to let the algorithm focus the operations on any potential solutions before discarding them in the selection process. The case studies show that the proposed GA-focus algorithm converges faster than RRT* and can be applied to various large-scale and challenging problems filled with obstacles of different shapes and sizes, and find high-quality solutions.