OPEN: New Path-Planning Algorithm for Real-World Complex Environment

2016 ◽  
pp. 237-244 ◽  
Author(s):  
J. I. Olszewska ◽  
J. Toman
Keyword(s):  
Path Planning ◽  
Real World ◽  
Complex Environment ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV

2021 ◽  
Vol 11 (9) ◽  
pp. 3948
Author(s):  
Aye Aye Maw ◽  
Maxim Tyan ◽  
Tuan Anh Nguyen ◽  
Jae-Woo Lee
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Real World ◽  
Mission Planning ◽  
Planning System ◽  
Local Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

scholarly journals A Hybrid Path Planning Algorithm for Unmanned Surface Vehicles in Complex Environment With Dynamic Obstacles

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 126439-126449 ◽  
Author(s):  
Zheng Chen ◽  
Youming Zhang ◽  
Yougong Zhang ◽  
Yong Nie ◽  
Jianzhong Tang ◽  
...  
Keyword(s):  
Path Planning ◽  
Complex Environment ◽  
Unmanned Surface Vehicles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zhe Zhang ◽  
Jian Wu ◽  
Jiyang Dai ◽  
Cheng He
Keyword(s):  
Path Planning ◽  
Flight Path ◽  
Complex Environment ◽  
Air Defense ◽  
Penetration Ability ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Dual Base ◽  
Search Approach ◽  
Path Planning Algorithm

This paper presents the flight penetration path planning algorithm in a complex environment with Bogie or Bandit (BB) threats for stealth unmanned aerial vehicle (UAV). The emergence of rigorous air defense radar net necessitates efficient flight path planning and replanning for stealth UAV concerning survivability and penetration ability. We propose the improved A-Star algorithm based on the multiple step search approach to deal with this uprising problem. The objective is to achieve rapid penetration path planning for stealth UAV in a complex environment. Firstly, the combination of single-base radar, dual-base radar, and BB threats is adopted to different threat scenarios which are closer to the real combat environment. Besides, the multistep search strategy, the prediction technique, and path planning algorithm are developed for stealth UAV to deal with BB threats and achieve the penetration path replanning in complex scenarios. Moreover, the attitude angle information is integrated into the flight path which can meet real flight requirements for stealth UAV. The theoretical analysis and numerical results prove the validity of our method.

Improved RRT-Connect path planning algorithm for biped robot

2013 ◽  
Vol 33 (8) ◽  
pp. 2289-2292 ◽  
Author(s):  
Dongcheng MO ◽  
Guodong LIU
Keyword(s):  
Path Planning ◽  
Biped Robot ◽  
Planning Algorithm ◽  
Path Planning Algorithm
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