Path Planning for the Marsupial double-UAVs System in Air-ground Collaborative Application

Ren, Shiwen; Chen, Yang; Ling, Xiong; Chen, Zhihuan; Chen, Mengqing

doi:10.23919/chicc.2018.8483087

Cited by 14 publications

(11 citation statements)

References 4 publications

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“…In order to verify the superiority of the dual UAVs vehicle-borne system, this paper compares it with the heterogeneous system composed of a single UAV and a UGV. 26,33 If the UGV carries only one UAV to perform the task, and other conditions are unchanged, the obtained path is as shown in Fig. 15, where the total time for completing the task is 7.7491 h, which is slightly increased compared with the dual-UAV model in this paper.…”

Section: Comparison and Analysismentioning

confidence: 85%

“…path i (33) where N is the number of targets and path i is the path length passed by the UAV when accessing the i-th target. (3) Cross operation: First, P i is arbitrarily determining two cross positions a and b (1 ≤ a, b ≤ N) for the extremum.…”

Section: Discussion: Sequence Optimizationmentioning

confidence: 99%

“…In the previous paper, 33 we analyzed the essence of the problem by introducing the decision variable defining the order of the takeoff and landing of two UAVs, and then established the movement constraints of the UGV and the UAVs respectively. By minimizing the total time required to access all targets, the path planning problem is formulated and solved by particle swarm optimization (PSO).…”

Section: Related Workmentioning

confidence: 99%

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

Chen¹,

Ren²,

Chen³

et al. 2019

Robotica

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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.

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Section: Comparison and Analysismentioning

confidence: 85%

Section: Discussion: Sequence Optimizationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Chen¹,

Ren²,

Chen³

et al. 2019

Robotica

Self Cite

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“…In this technique, to obtain the path and obstacles, sensors are used. For the routing and trajectory planning, the PSO algorithm is used in Ren et al and Zhang et al 99,100 for arriving at the destination in the minimum possible time. Learning models …”

Section: Routing Techniquesmentioning

confidence: 99%

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Sharma

Obaidat

Sharma

et al. 2020

Int J Communication

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SummaryRouting remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.

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“…The authors are with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo ON, N2L 3G1 Canada (barry. gilhuly@uwaterloo.ca, stephen.smith@uwaterloo.ca) while the ground vehicle acts simply as a mobile supply depot, providing support and resources to keep the UAVs flying [13], [8].…”

Section: Introductionmentioning

confidence: 99%

Robotic Coverage for Continuous Mapping Ahead of a Moving Vehicle

Gilhuly

Smith

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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In this paper we investigate the problem of using a UAV to provide current map information of the environment in front of a moving ground vehicle. We propose a simple coverage plan called a conformal lawn mower plan that enables a UAV to scan the route ahead of the ground vehicle. The plan requires only limited knowledge of the ground vehicle's future path. For a class of curvature-constrained ground vehicle paths, we show that the proposed plan requires a UAV velocity that is no more than twice the velocity required to cover the optimal plan. We also establish necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path in the curvature restricted set. In simulation, we validate the proposed plan, showing that the required velocity to provide coverage is strongly related to the curvature of the ground vehicle's path. Our results also illustrate the relationship between mapping requirements and the relative velocities of the UAV and ground vehicle.

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Path Planning for the Marsupial double-UAVs System in Air-ground Collaborative Application

Cited by 14 publications

References 4 publications

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

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

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Robotic Coverage for Continuous Mapping Ahead of a Moving Vehicle

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