A Computational Approach for Near-Optimal Path Planning and Guidance for Systems with Nonholonomic Contraints

2010 ◽  
Author(s):  
Anil V. Rao ◽  
William W. Hager
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Computational Approach ◽  
Optimal Path Planning

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.

scholarly journals Optimal Path Planning for Unmanned Aircraft Target Observation Through Constrained Urban Environments

2019 ◽  
Vol 27 (3) ◽  
pp. 144-152 ◽  
Author(s):  
Michael D. Zollars ◽  
Richard G. Cobb ◽  
David J. Grymin
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Urban Environments ◽  
Unmanned Aircraft ◽  
Optimal Path Planning

Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment

2021 ◽  
pp. 095441002110073
Author(s):  
Zhe Zhang ◽  
Jian Wu ◽  
Jiyang Dai ◽  
Cheng He
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Optimal Path ◽  
Radar System ◽  
Planning Method ◽  
Optimal Path Planning ◽  
Aerial Vehicles ◽  
3D Network ◽  
Planning Scheme ◽  
Estimation Function

For stealth unmanned aerial vehicles (UAVs), path security and search efficiency of penetration paths are the two most important factors in performing missions. This article investigates an optimal penetration path planning method that simultaneously considers the principles of kinematics, the dynamic radar cross-section of stealth UAVs, and the network radar system. By introducing the radar threat estimation function and a 3D bidirectional sector multilayer variable step search strategy into the conventional A-Star algorithm, a modified A-Star algorithm was proposed which aims to satisfy waypoint accuracy and the algorithm searching efficiency. Next, using the proposed penetration path planning method, new waypoints were selected simultaneously which satisfy the attitude angle constraints and rank-K fusion criterion of the radar system. Furthermore, for comparative analysis of different algorithms, the conventional A-Star algorithm, bidirectional multilayer A-Star algorithm, and modified A-Star algorithm were utilized to settle the penetration path problem that UAVs experience under various threat scenarios. Finally, the simulation results indicate that the paths obtained by employing the modified algorithm have optimal path costs and higher safety in a 3D complex network radar environment, which show the effectiveness of the proposed path planning scheme.

Optimal path planning of a disinfection mobile robot against COVID-19 in a ROS-based research platform

2021 ◽  
Vol 16 (4) ◽  
pp. 405-417
Author(s):  
L. Banjanovic-Mehmedovic ◽  
I. Karabegovic ◽  
J. Jahic ◽  
M. Omercic
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Pso Algorithm ◽  
Optimal Path Planning ◽  
Localization And Mapping ◽  
Increasing Demand ◽  
Window Approach ◽  
Path Planner ◽  
Hardware Platforms ◽  
Reactive Collision

Due to COVID-19 pandemic, there is an increasing demand for mobile robots to substitute human in disinfection tasks. New generations of disinfection robots could be developed to navigate in high-risk, high-touch areas. Public spaces, such as airports, schools, malls, hospitals, workplaces and factories could benefit from robotic disinfection in terms of task accuracy, cost, and execution time. The aim of this work is to integrate and analyse the performance of Particle Swarm Optimization (PSO) algorithm, as global path planner, coupled with Dynamic Window Approach (DWA) for reactive collision avoidance using a ROS-based software prototyping tool. This paper introduces our solution – a SLAM (Simultaneous Localization and Mapping) and optimal path planning-based approach for performing autonomous indoor disinfection work. This ROS-based solution could be easily transferred to different hardware platforms to substitute human to conduct disinfection work in different real contaminated environments.

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