Quantum-Inspired Optimization Algorithms for Autonomous Drone Path Planning

Abstract

Drones that act on their own have now been crucial in various tasks including disaster management and environmental policing as well as in logistic and surveillance. Path planning is an important aspect that predetermines the success of the mission, its safety, and energy efficiency. Classical optimization algorithms like A, Dijkstra, or genetic algorithms have been very used, but these algorithms do not always cope with the high dimensional and dynamic environment, as well as uncertainty. Quantum-inspired optimization algorithms (QIOAs) offer a potentially more useful alternative to this, implementing a generalized form of quantum computing, including superposition, entanglement, and probabilistic state transitions, without quantum hardware. Such algorithms may search large spaces of solutions more effectively, escape local optima and produce near-optimal paths with complicated constraints. The study examines the implementation of the QIOAs in the autonomous drone path planning to augment the adaptability, strength, and the computational effectiveness in the actual world.The second paragraph demonstrates the potential of quantum-inspired strategies to enhance the flexibility, resilience, and the computational ability in the real world. Several frequent challenges faced by drones include moving obstacles, short battery life, no fly zones, and dynamic weather, and they need adaptive path planning strategies that have the ability to balance a number of constraints at a given time. Quantum-inspired evolutionary algorithms, quantum-behaved particle swarm optimization and quantum annealing-inspired, are QIOAs that make use of probabilistic operators to effectively search and exploit complex search spaces. This paper compares the effectiveness of the QIOAs to the classical methods with highlight of path optimality, computation time and avoiding collisions. The simulations show that quantum-inspired algorithms experience better path quality in cluttered, dynamic, and high dimensional environments, which provides a disruptive opportunity of autonomous drones in navigation.