Multiple UAVs path planning algorithms: a comparative study

Sathyaraj, Beulah M.; Jain, Lakhmi C.; Finn, Anthony; Drake, Samuel Picton

doi:10.1007/s10700-008-9035-0

Cited by 117 publications

(62 citation statements)

References 13 publications

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“…This heuristic is used to prune the non optimal trajectories from the exploring tree. Some authors [26], [21], [25], [9], [10] have applied this AI path planning method for gliders moving through an uniform grid of ocean currents. • CTS-A*: This method [7] is a variant of the classic A* where the time between two consecutive surfacings is kept constant.…”

Section: A Alternative Methodsmentioning

confidence: 99%

Path planning for underwater gliders using iterative optimization

Isern-González

Hernández-Sosa

Fernandez-Perdomo

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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Underwater gliders constitute a technology in active development, which has proven very promising in Ocean Research because of its relative low cost and long mission range. Due to their low surge speed, however, gliders are strongly affected by ocean currents, making path planning a crucial tool for this type of vehicles. In this work, we present a novel path planning algorithm for gliders based on iterative optimization that shows promising results in realistic simulations. This method reflects accurately the vehicle operation pattern and exhibits a better performance when compared with alternative approaches that are compared in this paper.

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Section: A Alternative Methodsmentioning

confidence: 99%

Path planning for underwater gliders using iterative optimization

Isern-González

Hernández-Sosa

Fernandez-Perdomo

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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“…In [16] a decentralized extremum-seeking control algorithm for nonholonomic vehicles to form a communication chain is presented. In [17] path planning and path finding algorithms for multiple UAVs are studied. The performance of the algorithms, Dijkstra's algorithm, Bellman Ford's algorithm, Floyd Warshall's algorithm and the A algorithm, are compared.…”

Section: Previous Work and Available Technologymentioning

confidence: 99%

Path Planning for UAVs Under Communication Constraints Using SPLAT! and MILP

Grøtli

Johansen

2011

J Intell Robot Syst

102

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We will in this paper address the problem of offline path planning for Unmanned Aerial Vehicles (UAVs). Our goal is to find paths that meet mission objectives, are safe with respect to collision and grounding, fuel efficient and satisfy criteria for communication. Due to the many nonconvex constraints of the problem, Mixed Integer Linear Programming (MILP) will be used in finding the path. Approximate communication constraints and terrain avoidance constraints are used in the MILP formulation. To achieve more accurate prediction of the ability to communicate, the path is then analyzed in the radio propagation toolbox SPLAT!, and if the UAVs are not able to communicate according to design criteria for bandwidth, constraints are modified in the optimization problem in an iterative manner. The approach is exemplified with the following setup: The path of two UAVs are planned so they can serve as relay nodes between a target without line of sight to the base station.

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“…Multi-robot systems are a major research topic in robotics due to their intrinsic robustness and versatility [52]. Designing, testing and deploying in the real world a large number of aerial robots is a concrete possibility due to the recent technological advances.…”

Section: Introductionmentioning

confidence: 99%

Multi Aerial Robot Planning

Sebbane

2014

Intelligent Systems, Control and Automation: Science and Engineering

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Multi-robot systems are a major research topic in robotics. Designing, testing and deploying in the real world a large number of aerial robots is a concrete possibility due to the recent technological advances. The first section of this chapter treats the different aspects of cooperation in a multi-agent systems. A cooperative control should be designed in terms of the available feedback information. A cascade-type guidance law is proposed, followed by consensus approach and flocking behavior. Since information flow over the network changes over time, cooperative control must react accordingly but ensure group cooperative behavior which is the major issue in analysis and synthesis. Connectivity and convergence of formations are also studied. Team approach is followed by deterministic decision making. Plans may be required for a team of aerial robots to plan for sensing, plan for action or plan for communication. Distributed receding horizon control as well as conflict resolution, artificial potentials and symbolic planning are thus analyzed. Then, association with limited communications is studied, followed by genetic algorithms and game theory reasoning. Next, multi-agent decision making under uncertainty is considered, formulating the Bayesian decentralized team decision problem, with and without explicit communication. Algorithms for optimal planning are then introduced as well as for task allocation and distributed chance constrained task allocation. Finally, some case studies are presented such as reconnaissance mission that can be defined as the road search problem or the general vehicle routing problem. Then, an approach is considered to coordinate a group of aerial robots without a central supervision, by using only local interactions between the robots. The third case is the optimization of perimeter patrol operation. If an aerial robot must be close from a location to monitor it correctly and the number of aerial robots does not allow covering each site simultaneously, a path planning problem arises. Finally stochastic strategies for surveillance are presented.Y.

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Multiple UAVs path planning algorithms: a comparative study

Cited by 117 publications

References 13 publications

Path planning for underwater gliders using iterative optimization

Path planning for underwater gliders using iterative optimization

Path Planning for UAVs Under Communication Constraints Using SPLAT! and MILP

Multi Aerial Robot Planning

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