A Memetic Path Planning Algorithm for Unmanned Air/Ground Vehicle Cooperative Detection Systems

2021 ◽  
pp. 1-14
Author(s):  
Jianqiang Li ◽  
Tao Sun ◽  
Xiaopeng Huang ◽  
Lijia Ma ◽  
Qiuzhen Lin ◽  
...  
Keyword(s):  
Path Planning ◽  
Ground Vehicle ◽  
Detection Systems ◽  
Cooperative Detection ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals Analysis of the Hybrid Global Path Planning Algorithm for Different Environments

2019 ◽  
Vol 20 (1) ◽  
pp. 1-11
Author(s):  
Paulius Skačkauskas ◽  
Edgar Sokolovskij
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Experimental Tests ◽  
Important Research ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Research Fields ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Planning Approaches

Abstract To achieve the overall goal of realising an efficient and advantageous participation of autonomous ground vehicles in the transport system as fast as possible, a lot of work is being done in different and specific research fields. One of the most important research fields, which has a large impact on safe autonomous ground vehicle realisation, is the development of path planning algorithms. Therefore, this work describes in detail the development and application of a hybrid path planning algorithm. The described algorithm is based on classical and heuristic path planning approaches and can be applied in unstructured and structured environments. The efficiency of the algorithm was investigated by applying the algorithm and executing theoretical and experimental tests. The theoretical and experimental tests were executed while optimising different complexity paths. Results analysis demonstrated that the described algorithm can generate a smooth, dynamically feasible and collision-free path.

scholarly journals Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401773665 ◽  
Author(s):  
Demim Fethi ◽  
Abdelkrim Nemra ◽  
Kahina Louadj ◽  
Mustapha Hamerlain
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Simultaneous Localization And Mapping ◽  
Variable Structure ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Localization And Mapping ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Smooth Variable Structure Filter

Among the huge number of functionalities that are required for autonomous navigation, the most important are localization, mapping, and path planning. In this article, investigation of the path planning problem of unmanned ground vehicle is based on optimal control theory and simultaneous localization and mapping. A new approach of optimal simultaneous localization, mapping, and path planning is proposed. Our approach is mainly affected by vehicle’s kinematics and environment constraints. Simultaneous localization, mapping, and path planning algorithm requires two main stages. First, the simultaneous localization and mapping algorithm depends on the robust smooth variable structure filter estimate accurate positions of the unmanned ground vehicle. Then, an optimal path is planned using the aforementioned positions. The aim of the simultaneous localization, mapping, and path planning algorithm is to find an optimal path planning using the Shooting and Bellman methods which minimizes the final time of the unmanned ground vehicle path tracking. The simultaneous localization, mapping, and path planning algorithm has been approved in simulation, experiments, and including real data employing the mobile robot Pioneer [Formula: see text]. The obtained results using smooth variable structure filter–simultaneous localization and mapping positions and the Bellman approach show path generation improvements in terms of accuracy, smoothness, and continuity compared to extended Kalman filter–simultaneous localization and mapping positions.

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.

Sign in / Sign up

Export Citation Format

Share Document