Persistent Surveillance Using Multiple Unmanned Air Vehicles

Nigam, Nikhil; Kroo, Ilan

doi:10.1109/aero.2008.4526242

Cited by 92 publications

(61 citation statements)

References 31 publications

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“…Due to the finite time horizon considered in this problem, it is computationally expensive to solve for robot trajectories, especially in the multi-UAV case. 1) Persistent Monitoring: Early works in multi-UAV persistent monitoring used a heuristic approach to extend traditional single-UAV solutions to multiple UAVs [175], [176]. Other work focused on enabling real-time computation on-board the UAVs by using parameterized B-spline curves to define the set of feasible trajectories [177].…”

Section: Robot Capabilities Rmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: Robot Capabilities Rmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…The modified CPP (mCPP) generates a tour of the road-network traveling all the roads once no matter what the type area of interest map is: an even or odd graph. Even searching the area having no road somewhere in it can be tackled by the mCPP algorithm by generating a virtual road pattern with a lawnmower (Maza and Ollero 2007) or spiral-like (Nigam and Kroo 2008) algorithm. Figure 2 exemplifies a sample road-network search problem to be solved by the mCPP.…”

Section: Traveling Salesman Problem (Tsp)mentioning

confidence: 99%

Coordinated road-network search route planning by a team of UAVs

Kim

Tsourdos

et al. 2012

International Journal of Systems Science

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This paper presents a road-network search route planning algorithm by which multiple autonomous vehicles are able to efficiently visit every road identified in the map in the context of the Chinese postman problem. Since the typical Chinese postman algorithm can be applied solely to a connected road-network in which ground vehicles are involved, it is modified to be used for a general type of roadmap including unconnected roads as well as the operational and physical constraints of UAVs. For this, a multi-choice multi-dimensional Knapsack problem is formulated to find an optimal solution minimising a flight time and then solved via mixed integer linear programming. To deal with the dynamic constraints of the UAVs, the Dubins theory is used for path generation. In particular, a circular-circular-circular type of the Dubins path is exploited based on a differential geometry to guarantee that the vehicles follow the road precisely in a densely distributed road environment. Moreover, to overcome the computational burden of the multi-choice multi-dimensional Knapsack algorithm, a nearest insertion and auction based approximation algorithm is newly introduced. The properties and performance of the proposed algorithm are evaluated via numerical simulations operating on a real village map and randomly generated maps with different parameters.

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“…Examples include obstacle avoidance [1], testing of new sensors [2], flight control or navigation strategies, observation tasks [3] and gathering of air data, e.g. pollution or toxic plumes [4].…”

Section: Introductionmentioning

confidence: 99%

A minimalist control strategy for small UAVs

Leven

Zufferey

Floreano

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Most autopilots of existing Miniature Unmanned Air Vehicles (MUAVs) rely on control architectures that typically use a large number of sensors (gyros, accelerometers, magnetometers, GPS) and a computationally demanding estimation of flight states. As a consequence, they tend to be complex, require a significant amount of processing power and are usually expensive. Many research projects that aim at experiments with one, or even several, MUAVs would benefit from a simpler, potentially smaller, lighter and less expensive autopilot for their flying platforms.In this paper, we present a minimalist control strategy for fixed-wing MUAVs that provides the three basic functionalities of airspeed, altitude and heading turnrate control while only using two pressure sensors and a single-axis rate gyro. To achieve this, we use reactive control loops, which rely on direct feedback from the sensors instead of full state information. In order to characterize the control strategy, it was implemented on a custom-made autopilot. With data recorded during flight experiments, we carried out a statistical analysis of step responses to altitude and turnrate commands as well as responses to artificial perturbations.

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Persistent Surveillance Using Multiple Unmanned Air Vehicles

Cited by 92 publications

References 31 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Coordinated road-network search route planning by a team of UAVs

A minimalist control strategy for small UAVs

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