Using Path Planning Algorithms and Digital Twin Simulators to Collect Synthetic Training Dataset for Drone Autonomous Navigation

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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Path-planning algorithms for public transportation systems

ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585) ◽

10.1109/itsc.2001.948809 ◽

2002 ◽

Cited By ~ 1

Author(s):

Chao-Lin Liu ◽

Tun-Wen Pai ◽

Chun-Tien Chang ◽

Chang-Ming Hsieh

Keyword(s):

Path Planning ◽

Public Transportation ◽

Transportation Systems ◽

Planning Algorithms ◽

Public Transportation Systems

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Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study

Unmanned Systems ◽

10.1142/s2301385018400022 ◽

2018 ◽

Vol 06 (02) ◽

pp. 95-118 ◽

Cited By ~ 41

Author(s):

Mohammadreza Radmanesh ◽

Manish Kumar ◽

Paul H. Guentert ◽

Mohammad Sarim

Keyword(s):

Path Planning ◽

Comparative Study ◽

Operating Conditions ◽

Computational Time ◽

Safe Operation ◽

Sense And Avoid ◽

Planning Algorithms ◽

And Control ◽

Global And Local ◽

Further Development

Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need further development for efficient application to various scenarios. One key issue is the “Sense and Avoid” capability, currently of immense interest to researchers. Such a capability is required for safe operation of UAVs in civilian domain. For autonomous decision making and control of UAVs, several path-planning and navigation algorithms have been proposed. This is a challenging task to be carried out in a 3D environment, especially while accounting for sensor noise, uncertainties in operating conditions, and real-time applicability. Heuristic and non-heuristic or exact techniques are the two solution methodologies that categorize path-planning algorithms. The aim of this paper is to carry out a comprehensive and comparative study of existing UAV path-planning algorithms for both methods. Three different obstacle scenarios test the performance of each algorithm. We have compared the computational time and solution optimality, and tested each algorithm with variations in the availability of global and local obstacle information.

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Real-Time Path Planning Based on Harmonic Functions under a Proper Generalized Decomposition-Based Framework

Sensors ◽

10.3390/s21123943 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3943

Author(s):

Nicolas Montés ◽

Francisco Chinesta ◽

Marta C. Mora ◽

Antonio Falcó ◽

Lucia Hilario ◽

...

Keyword(s):

Path Planning ◽

Real Time ◽

Harmonic Functions ◽

Autonomous Navigation ◽

Numerical Technique ◽

Computational Cost ◽

Main Idea ◽

Main Property ◽

Proper Generalized Decomposition ◽

Computational Vademecum

This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property of the proposed technique is that the computational cost is negligible in real-time, even if the robot is disturbed or the goal is changed. The main idea of the method is the off-line generation, for a given environment, of the whole set of paths from any start and goal configurations of a mobile robot, namely the computational vademecum, derived from a harmonic potential field in order to use it on-line for decision-making purposes. Up until now, the resolution of the Laplace or Poisson equations has been based on traditional numerical techniques unfeasible for real-time calculation. This drawback has prevented the extensive use of harmonic functions in autonomous navigation, despite their powerful properties. The numerical technique that reverses this situation is the Proper Generalized Decomposition. To demonstrate and validate the properties of the PGD-vademecum in a potential-guided path planning framework, both real and simulated implementations have been developed. Simulated scenarios, such as an L-Shaped corridor and a benchmark bug trap, are used, and a real navigation of a LEGO®MINDSTORMS robot running in static environments with variable start and goal configurations is shown. This device has been selected due to its computational and memory-restricted capabilities, and it is a good example of how its properties could help the development of social robots.

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