Unmanned aerial vehicle dynamic path planning in an uncertain environment

Yao, Min; Zhao, Min

doi:10.1017/s0263574714000514

Cited by 26 publications

(12 citation statements)

References 18 publications

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“…In reality, such obstacles normally have unpredictable movement patterns, which requires a path planning algorithm to be integrated with advanced sensors and a prediction algorithm. In Yao et al 95 , the Kalman filter was used to predict the path of a moving obstacle in the immediate future, and the path planning algorithm can accordingly adjust the path to avoid the obstacles more effectively. r Environmental factors were not included in the problem.…”

Section: Discussion On Formation Path Planningmentioning

confidence: 99%

A survey of formation control and motion planning of multiple unmanned vehicles

2018

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SUMMARYThe increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development.

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Section: Discussion On Formation Path Planningmentioning

confidence: 99%

A survey of formation control and motion planning of multiple unmanned vehicles

2018

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“…Furthermore, UAVs' path planning has been widely investigated in the research community, where the main idea is to find the optimum path from the origin to the destination, while avoiding collisions with obstacles and meeting the UAV performance constraints [32]. Previous works in this area utilized di↵erent methods for solving this issue, for example in [33], the controller was implemented for each UAV to search the environment and identify the maximum number of targets at the end of the search.…”

Section: A Related Workmentioning

confidence: 99%

“…Furthermore, Richards and Jonathan How [34] implemented a Decentralized Model Predictive Control (DMPC) for a team of cooperating UAVs and used coupled constraints to avoid collisions. The above works and many others like the ones in [35], [36], [37], [38], [39], [32] and [40] made one assumption for simplicity or for collision avoidance; that each UAV had a constant altitude (moves on a 2 dimensional plane) or each aerial vehicle moved on di↵erent altitudes, an assumption that is restricting the generalisation of the presented approaches.…”

Section: A Related Workmentioning

confidence: 99%

“…As mentioned above, there have been many works on cooperative control of UAVs, where most of them implemented and simulated UAV controllers in a two dimensional space as in ( [33], [47], [34], [35], [36], [37], [38], [39], [32], [40] and [34]). Few of them utilized a three dimensional space but the authors used a simple linear model or did not consider the attitude problem of the UAV ( [44], [46], [48]).…”

Section: A Related Workmentioning

confidence: 99%

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Distributed model predictive control for unmanned aerial vehicles

Mansouri

Nikolakopoulos

Gustafsson

2015

2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

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Abstract-In this article a distributed model predictive control scheme, for the cooperative motion control of Unmanned Aerial Vehicles (UAVs) is being presented. The UAVs are modeled by a 6-DOF nonlinear kinematic model. Two di↵erent control architectures: a centralized and a distributed MPC, are studied and evaluated in simulation experiments. In the centralized approach, one central MPC controller is responsible for the movement coordination of all the UAVs, while in the distributed approach each aerial vehicle plans only for its own actions, while the objective function is coupled with the behavior of the rest of the team members and the constraints are decoupled. In this approach, each agent only shares the future position of itself with the other agents to avoid collisions. For reducing the computation time and complexity, only one step ahead prediction in the corresponding MPC schemes have been considered without a loss of generality. Finally, the e ciency of the overall suggested decentralized MPC scheme, as well as it comparison with the centralized approach, is being evaluated through the utilization of multiple simulation scenarios.

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“…TST include static or dynamic targets, for which time windows in which they should be recognized are defined. To recognize dynamic objects, one must modify the standard algorithm [14,15]. To correctly plan a route for UAVs, one should also take into account the impact of weather conditions on flight parameters [16].…”

Section: Introductionmentioning

confidence: 99%

UAV Mission Planning with SAR Application

Stecz

Gromada

2020

Sensors

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The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors’ capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm—Vehicle Route Planning with Time Window (VRPTW)—using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.

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Unmanned aerial vehicle dynamic path planning in an uncertain environment

Cited by 26 publications

References 18 publications

A survey of formation control and motion planning of multiple unmanned vehicles

A survey of formation control and motion planning of multiple unmanned vehicles

Distributed model predictive control for unmanned aerial vehicles

UAV Mission Planning with SAR Application

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