This paper addresses the challenge of path planning for unmanned aerial vehicles (UAVs) in highly dynamic and uncertain environments, particularly focusing on impaired quadrotors with actuator failures. The authors develop multi-dimensional path planning algorithms that are adaptive, model-free, and constrained to handle actuator saturations, kinematic, and dynamic constraints. Two meta-heuristic algorithms, Multi-Dimensional Particle Swarm Optimization (M-PSO) and Multi-Dimensional Genetic Algorithm (M-GA), are constructed to optimize the minimum distance and minimum time cost functions. These algorithms generate control signals and plan paths for translational, rotational, and Euler angles. The performance of the algorithms is evaluated in both simulation and real-time experiments, demonstrating that the M-GA algorithm produces shorter minimum distance and minimum time paths under various constraints. Real-time experiments show that the quadrotor can follow the planned paths using the available maximum rotor speeds, even in the presence of actuator failures. The paper concludes with a discussion on future work, including the coordination of multiple quadrotors and the integration of other UAVs' information.This paper addresses the challenge of path planning for unmanned aerial vehicles (UAVs) in highly dynamic and uncertain environments, particularly focusing on impaired quadrotors with actuator failures. The authors develop multi-dimensional path planning algorithms that are adaptive, model-free, and constrained to handle actuator saturations, kinematic, and dynamic constraints. Two meta-heuristic algorithms, Multi-Dimensional Particle Swarm Optimization (M-PSO) and Multi-Dimensional Genetic Algorithm (M-GA), are constructed to optimize the minimum distance and minimum time cost functions. These algorithms generate control signals and plan paths for translational, rotational, and Euler angles. The performance of the algorithms is evaluated in both simulation and real-time experiments, demonstrating that the M-GA algorithm produces shorter minimum distance and minimum time paths under various constraints. Real-time experiments show that the quadrotor can follow the planned paths using the available maximum rotor speeds, even in the presence of actuator failures. The paper concludes with a discussion on future work, including the coordination of multiple quadrotors and the integration of other UAVs' information.