Observability-based local path planning and collision avoidance for micro air vehicles using bearing-only measurements

2011 ◽  
Author(s):  
Huili Yu ◽  
Rajnikant Sharma ◽  
Randal W. Beard ◽  
Clark N. Taylor
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Micro Air Vehicles ◽  
Local Path Planning ◽  
Local Path ◽  
Air Vehicles

scholarly journals Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Guoqing Xia ◽  
Zhiwei Han ◽  
Bo Zhao ◽  
Xinwei Wang
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Optimization Problem ◽  
Quantum Particle ◽  
Swarm Optimization ◽  
Unmanned Surface Vehicle ◽  
Kinematic Constraints ◽  
Quantum Particle Swarm Optimization ◽  
Local Path Planning ◽  
Local Path

An unmanned surface vehicle (USV) plans its global path before the mission starts. When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision. This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance. The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle. According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision. Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA). In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space. The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV. Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value. MQPSO is primarily proposed to meet that requirement. In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined. Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.

scholarly journals A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1488
Author(s):  
Federico Peralta ◽  
Mario Arzamendia ◽  
Derlis Gregor ◽  
Daniel G. Reina ◽  
Sergio Toral
Keyword(s):  
Path Planning ◽  
Random Trees ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Planning Techniques ◽  
Advantages And Disadvantages ◽  
Autonomous Surface Vehicle ◽  
Local Path Planning ◽  
Custom Made ◽  
Local Path

Local path planning is important in the development of autonomous vehicles since it allows a vehicle to adapt their movements to dynamic environments, for instance, when obstacles are detected. This work presents an evaluation of the performance of different local path planning techniques for an Autonomous Surface Vehicle, using a custom-made simulator based on the open-source Robotarium framework. The conducted simulations allow to verify, compare and visualize the solutions of the different techniques. The selected techniques for evaluation include A*, Potential Fields (PF), Rapidly-Exploring Random Trees* (RRT*) and variations of the Fast Marching Method (FMM), along with a proposed new method called Updating the Fast Marching Square method (uFMS). The evaluation proposed in this work includes ways to summarize time and safety measures for local path planning techniques. The results in a Lake environment present the advantages and disadvantages of using each technique. The proposed uFMS and A* have been shown to achieve interesting performance in terms of processing time, distance travelled and security levels. Furthermore, the proposed uFMS algorithm is capable of generating smoother routes.

A joint global and local path planning optimization for UAV task scheduling towards crowd air monitoring

2021 ◽  
Vol 193 ◽  
pp. 107913
Author(s):  
Yuan Tang ◽  
Yiming Miao ◽  
Ahmed Barnawi ◽  
Bander Alzahrani ◽  
Reem Alotaibi ◽  
...  
Keyword(s):  
Path Planning ◽  
Task Scheduling ◽  
Air Monitoring ◽  
Local Path Planning ◽  
Planning Optimization ◽  
Local Path ◽  
Global And Local
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