An Improved Probabilistic Roadmap Planning Method for Safe Indoor Flights of Unmanned Aerial Vehicles

Abstract

Unmanned aerial vehicles (UAVs) have been widely used in industry and daily life, where safety is the primary consideration, resulting in their use in open outdoor environments, which are wider than complex indoor environments. However, the demand is growing for deploying UAVs indoors for specific tasks such as inspection, supervision, transportation, and management. To broaden indoor applications while ensuring safety, the quadrotor is notable for its motion flexibility, particularly in the vertical direction. In this study, we developed an improved probabilistic roadmap (PRM) planning method for safe indoor flights based on the assumption of a quadrotor model UAV. First, to represent and model a 3D environment, we generated a reduced-dimensional map using a point cloud projection method. Second, to deploy UAV indoor missions and ensure safety, we improved the PRM planning method and obtained a collision-free flight path for the UAV. Lastly, to optimize the overall mission, we performed postprocessing optimization on the path, avoiding redundant flights. We conducted experiments to validate the effectiveness and efficiency of the proposed method on both desktop and onboard PC, in terms of path-finding success rate, planning time, and path length. The results showed that our method ensures safe indoor UAV flights while significantly improving computational efficiency.

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

“…In dynamic environments, such as urban air traffic management or disaster area rescue operations, dynamic PRM can update the UAVs' path planning in real-time according to environmental changes. This algorithm allows UAVs to adapt to emergencies, move obstacles, or change mission requirements, ensuring the safe and effective completion of tasks while maintaining coordination and communication between UAVs [15].…”

Control and Position Tracking for UAVs

“…They can be split into two subgroups: traditional and intelligent algorithms. Traditional algorithms are deterministic, which include Voronoi diagrams (VD) [8], the Dijkstra algorithm (DA) [9], rapid exploration random tree (RRT) [10], probabilistic road map (PRM) [11], Dubins curves (DC) [12], the Floyd algorithm (FA) [13], and the fast marching method (FMM) [14]. Intelligent algorithms are mostly bio-inspired heuristic algorithms and those based on machine learning.…”

A search and rescue robot search method based on flower pollination algorithm and Q-learning fusion algorithm

“…The robot can then search for the optimal path by maximizing the cumulative payoff. However, Q-learning algorithms have limitations: slow convergence and exploration-utilization dilemma problems [ 21 ]. To speed up the convergence, an improved Q-learning (IEGQL) algorithm is proposed in the literature [ 22 ] to address the shortcomings of the slow convergence of the traditional Q-learning algorithm to improve the efficiency in terms of path length and computational cost, in addition to a new mathematical model that provides optimal choices while ensuring fast convergence.…”