Geolocation of RF Emitters by Many UAVs

Scerri, Paul; Glinton, Robin; Owens, Sean; Scerri, David; Sycara, Katia

doi:10.2514/6.2007-2858

Cited by 38 publications

(32 citation statements)

References 8 publications

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“…Micro Aerial Vehicles (MAVs) provide an appealing solution to many tasks such as monitoring [19,18], reconnaissance, sensing [12], search and rescue, and enemy targeting [24] that may not be accomplished by larger Unmanned Aerial Vehicles (UAVs). MAVs can accomplish such demanding missions without posing a large threat in case of malfunction, since they are by definition small and light.…”

Section: Introductionmentioning

confidence: 99%

A neuro-evolutionary approach to control surface segmentation for micro aerial vehicles

Salichon

Tumer

2013

International Journal of General Systems

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This paper addresses control surface segmentation in Micro Aerial Vehicles (MAVs) by leveraging neuro-evolutionary techniques that allow the control of a higher number of control surfaces. Applying classical control methods to MAVs is a difficult process due to the complexity of the control laws with fast and highly non-linear dynamics. These methods are mostly based on models that are difficult to obtain for dynamic and stochastic environments. Moreover, these problems are exacerbated when both the number of control surfaces increases and the model's accuracy in determining the impact of each control surface decreases. Instead, we focus on neuro-evolutionary techniques that have been successfully applied in many domains with limited models and highly non-linear dynamics. Wind tunnel simulations with AVL show that MAV performances are improved both in terms of reduced deflection angles and reduced drag (up to 5%) over a simplified model in two sets of experiments with different objective functions. We also show robustness to actuator failure with desired roll moment values still attained with failed actuators in the system through the neuro-controller.

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Section: Introductionmentioning

confidence: 99%

A neuro-evolutionary approach to control surface segmentation for micro aerial vehicles

Salichon

Tumer

2013

International Journal of General Systems

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“…Our initial approach was to apply a modified RRT based cooperative information gain approach developed for UAVs [13]. However, it turned out to be difficult to tune to a complex, indoor environment.…”

Section: Autonomous Path Planningmentioning

confidence: 99%

Towards an understanding of the impact of autonomous path planning on victim search in USAR

Scerri

Velagapudi

Sycara

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Technology for multirobot systems has advanced to the point where we can consider their use in a variety of important domains, including urban search and rescue. A key to the practical usefulness of multirobot systems is the ability to have a large number of robots effectively controlled by small numbers of operators. In this paper, two modalities for controlling a team of 24 robots in a foraging task in an urban search and rescue environment are compared. In both modalities, multiple operators must monitor video streams from the robots to detect and mark victims on a map as well as teleoperating robots that cannot get themselves out of difficult situations. In the first modality, the operators must also provide waypoints for the robots to explore, using both video and a partially completed map to choose appropriate waypoints. In the second modality, the robots autonomously plan their paths, allowing operators to focus on monitoring the video, but without being able to interpret video streams to guide exploration. Experimental results show that significantly better overall performance is achieved with autonomous path planning, although the reduction in operator workload is not significant.

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“…Micro Aerial Vehicles (MAVs) offer a great alternative to piloted vehicles in many tasks such as surveillance [1], reconnaissance, sensing [2], search and rescue [3], and automatic target recognition [4], [5]. MAVs can accomplish such missions without endangering human lives, giving an important edge to the organization using them.…”

Section: Introductionmentioning

confidence: 99%

Evolving a Multiagent Controller for Micro Aerial Vehicles

Salichon

Tumer

2012

IEEE Trans. Syst., Man, Cybern. C

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Abstract-Micro Aerial Vehicles (MAVs) are notoriously difficult to control as they are light, susceptible to minor fluctuations in the environment, and obey highly non-linear dynamics. Indeed, traditional control methods, particularly those relying on difficult to obtain models of the interaction between an MAV and its environment have been unable to provide adequate control beyond simple maneuvers. In this paper, we address the problem of controlling an MAV (which has segmented control surfaces) by evolving a neurocontroller and fine-tuning it using multiagent coordination techniques. This approach is based on a control strategy that learns to map MAV states (position, velocity) to MAV actions (e.g., actuator position) to achieve good performance (e.g., flight time) by maximizing an objective function. The main difficulty with this approach is defining the objective functions at the MAV level that allow good performance. In addition, to provide added robustness, we investigate a multiagent approach to control where each control surface aims to optimize a local objective. Our results show that this approach not only provides good MAV control, but provides robustness to (i) wind gusts by a factor of six; (ii) turbulence by a factor of four; and (iii) hardware failures by a factor of eight over a traditional control method.

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Geolocation of RF Emitters by Many UAVs

Cited by 38 publications

References 8 publications

A neuro-evolutionary approach to control surface segmentation for micro aerial vehicles

A neuro-evolutionary approach to control surface segmentation for micro aerial vehicles

Towards an understanding of the impact of autonomous path planning on victim search in USAR

Evolving a Multiagent Controller for Micro Aerial Vehicles

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