Reinforcement learning-based radar-evasive path planning: a comparative analysis

2021 ◽  
pp. 1-18
Author(s):  
R.U. Hameed ◽  
A. Maqsood ◽  
A.J. Hashmi ◽  
M.T. Saeed ◽  
R. Riaz
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Learning Algorithms ◽  
Optimal Path ◽  
Trust Region ◽  
Optimal Path Planning ◽  
Optimal Paths ◽  
Policy Gradient ◽  
Model Aircraft ◽  
Tracking Model

Abstract This paper discusses the utilisation of deep reinforcement learning algorithms to obtain optimal paths for an aircraft to avoid or minimise radar detection and tracking. A modular approach is adopted to formulate the problem, including the aircraft kinematics model, aircraft radar cross-section model and radar tracking model. A virtual environment is designed for single and multiple radar cases to obtain optimal paths. The optimal trajectories are generated through deep reinforcement learning in this study. Specifically, three algorithms, namely deep deterministic policy gradient, trust region policy optimisation and proximal policy optimisation, are used to find optimal paths for five test cases. The comparison is carried out based on six performance indicators. The investigation proves the importance of these reinforcement learning algorithms in optimal path planning. The results indicate that the proximal policy optimisation approach performed better for optimal paths in general.

Optimal Interceptions on Two-Dimensional Grids with Obstacles

2007 ◽  
Vol 61 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Ki Yin Chang ◽  
Gene Eu Jan ◽  
Chien-Min Su ◽  
Ian Parberry
Keyword(s):  
Path Planning ◽  
Linear Time ◽  
Optimal Path ◽  
Two Dimensional ◽  
Moving Targets ◽  
Digital Maps ◽  
Optimal Path Planning ◽  
Optimal Paths ◽  
Planning Methods ◽  
Multiple Moving Targets

This article presents efficient and practical methods for path planning of optimal interceptions on two-dimensional grids with obstacles, such as raster charts or non-distorted digital maps. The proposed methods search for optimal paths from sources to multiple moving-targets by a novel higher geometry wave propagation scheme in the grids, instead of the traditional vector scheme in the graphs. By introducing a time-matching scheme, the optimal interception paths from sources to all the moving-targets are obtained among the combinations with linear time and space complexities. Two optimal path planning methods for multiple one-to-one interceptions, the MIN-MAX and MIN-AVG, are applied to emulate the real routing.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

scholarly journals Time-optimal Path Planning of Multi-axis CNC Processes Using Variability of Orientation

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 324-329
Author(s):  
Frederik Wulle ◽  
Max Richter ◽  
Christoph Hinze ◽  
Alexander Verl
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Optimal Path Planning ◽  
Time Optimal

scholarly journals A CNN-based scheme for COVID-19 detection with emergency services provisions using an optimal path planning

2021 ◽  
Author(s):  
Ahmed Barnawi ◽  
Prateek Chhikara ◽  
Rajkumar Tekchandani ◽  
Neeraj Kumar ◽  
Mehrez Boulares
Keyword(s):  
Path Planning ◽  
Emergency Services ◽  
Optimal Path ◽  
Optimal Path Planning

scholarly journals COLREG-Compliant Optimal Path Planning for Real-Time Guidance and Control of Autonomous Ships

2021 ◽  
Vol 9 (4) ◽  
pp. 405
Author(s):  
Raphael Zaccone
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Open Water ◽  
Optimal Path Planning ◽  
Guidance And Control ◽  
Real Time Applications ◽  
And Control

While collisions and groundings still represent the most important source of accidents involving ships, autonomous vessels are a central topic in current research. When dealing with autonomous ships, collision avoidance and compliance with COLREG regulations are major vital points. However, most state-of-the-art literature focuses on offline path optimisation while neglecting many crucial aspects of dealing with real-time applications on vessels. In the framework of the proposed motion-planning, navigation and control architecture, this paper mainly focused on optimal path planning for marine vessels in the perspective of real-time applications. An RRT*-based optimal path-planning algorithm was proposed, and collision avoidance, compliance with COLREG regulations, path feasibility and optimality were discussed in detail. The proposed approach was then implemented and integrated with a guidance and control system. Tests on a high-fidelity simulation platform were carried out to assess the potential benefits brought to autonomous navigation. The tests featured real-time simulation, restricted and open-water navigation and dynamic scenarios with both moving and fixed obstacles.

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