Study on Path Planning of Unmanned Vehicle Based on Kinematic and Dynamic Constraints

2017 ◽  
pp. 94-104
Author(s):  
Li Li ◽  
Benshan Zhong ◽  
Ziyan Geng
Keyword(s):  
Path Planning ◽  
Unmanned Vehicle ◽  
Dynamic Constraints

Path Planning for Vehicle-borne System Consisting of Multi Air–ground Robots

Robotica ◽  
2019 ◽  
Vol 38 (3) ◽  
pp. 493-511 ◽  
Author(s):  
Yang Chen ◽  
Shiwen Ren ◽  
Zhihuan Chen ◽  
Mengqing Chen ◽  
Huaiyu Wu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Maximum Speed ◽  
Planning Problem ◽  
Robot System ◽  
Ground Vehicle ◽  
Dynamic Constraints ◽  
Charging Time ◽  
Path Planning Problem ◽  
Flying Robots

SummaryThis paper considers the path planning problem for deployment and collection of a marsupial vehicle system which consists of a ground mobile robot and two aerial flying robots. The ground mobile robot, usually unmanned ground vehicle (UGV), as a carrier, is able to deploy and harvest the aerial flying robots, and each aerial flying robot, usually unmanned aerial vehicles (UAVs), takes off from and lands on the carrier. At the same time, owing to the limited duration in the air in one flight, UAVs should return to the ground mobile robot timely for its energy-saving and recharge. This work is motivated by cooperative search and reconnaissance missions in the field of heterogeneous robot system. Especially, some targets with given positions are assumed to be visited by any of the UAVs. For the cooperative path planning problem, this paper establishes a mathematical model to solve the path of two UAVs and UGV. Many real constraints including the maximum speed of two UAVs and UGV, the minimum charging time of two UAVs, the maximum hovering time of UAVs, and the dynamic constraints among UAVs and UGV are considered. The objective function is constructed by minimizing the time for completing the whole mission. Finally, the path planning problem of the robot system is transformed into a multi-constrained optimization problem, and then the particle swarm optimization algorithm is used to obtain the path planning results. Simulations and comparisons verify the feasibility and effectiveness of the proposed method.

scholarly journals Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 3 ◽  
Author(s):  
Chunhe Hu ◽  
Yu Xia ◽  
Junguo Zhang
Keyword(s):  
Path Planning ◽  
Nonlinear Problems ◽  
Mapping Method ◽  
Initial Population ◽  
Local Optimum ◽  
Planning Problem ◽  
Dynamic Constraints ◽  
Logistic Mapping ◽  
Global And Local ◽  
Better Than

Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

Path Planning Technology of Unmanned Vehicle Based on Improved Deep Reinforcement Learning

2021 ◽  
Author(s):  
Kai Zhang ◽  
Luhe Wang ◽  
Jinwen Hu ◽  
Zhao Xu ◽  
Chubing Guo
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Vehicle

scholarly journals Distributed Detect-and-Avoid for Multiple Unmanned Aerial Vehicles in National Air Space

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Mohammad Sarim ◽  
Mohammadreza Radmanesh ◽  
Matthew Dechering ◽  
Manish Kumar ◽  
Ravikumar Pragada ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Precision Agriculture ◽  
Traffic Management ◽  
Mixed Integer ◽  
Dynamic Constraints ◽  
Aerial Vehicles ◽  
Package Delivery ◽  
Feasible Solutions

Small unmanned aerial vehicles (UAVs) have the potential to revolutionize various applications in civilian domain such as disaster management, search and rescue operations, law enforcement, precision agriculture, and package delivery. As the number of such UAVs rise, a robust and reliable traffic management is needed for their integration in national airspace system (NAS) to enable real-time, reliable, and safe operation. Management of UAVs traffic in NAS becomes quite challenging due to issues such as real-time path planning of large number of UAVs, communication delays, operational uncertainties, failures, and noncooperating agents. In this work, we present a novel UAV traffic management (UTM) architecture that enables the integration of such UAVs in NAS. A combined A*–mixed integer linear programming (MILP)-based solution is presented for initial path planning of multiple UAVs with individual mission requirements and dynamic constraints. We also present a distributed detect-and-avoid (DAA) algorithm based on the concept of resource allocation using a market-based approach. The results demonstrate the scalability, optimality, and ability of the proposed approach to provide feasible solutions that are versatile in dynamic environments.

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