Scalable Control of Decentralised Sensor Platforms

Grocholsky, Ben; Makarenko, Alexei; Kaupp, Tobias; Durrant‐Whyte, Hugh

doi:10.1007/3-540-36978-3_7

Cited by 42 publications

(41 citation statements)

References 8 publications

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“…This work builds on previous endeavors in decentralized data fusion [5,4] and active sensor networks [3]. The established architecture and methodology is used here.…”

Section: Related Workmentioning

confidence: 99%

“…As in [3], a control layer is implemented above the DDF framework. Figure 1 details the structure of an active sensing node.…”

Section: System Architecture and Approachmentioning

confidence: 99%

“…This paper describes the implementation of a decentralized architecture for autonomous teams of aerial and ground vehicles engaged in active perception. We provide a theoretical framework based on an established approach to the underlying sensor fusion problem [3]. This provides transparent integration of information from heterogeneous sources.…”

mentioning

confidence: 99%

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Synergies in Feature Localization by Air-Ground Robot Teams

Grocholsky

Bayraktar

Kumar

et al. 2006

Springer Tracts in Advanced Robotics

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Abstract. This paper describes the implementation of a decentralized architecture for autonomous teams of aerial and ground vehicles engaged in active perception. We provide a theoretical framework based on an established approach to the underlying sensor fusion problem [3]. This provides transparent integration of information from heterogeneous sources. The approach is extended to include an information-theoretic utility measure that captures the task objective and robot inter-dependencies. A distributed solution mechanism is employed to determine information maximizing trajectories and assignments subject to the constraints of individual vehicle and sensor sub-systems. This architecture enables the benefit of the complementary aerial and ground based vehicle and sensor capabilities to be realized. The approach is applied to missions involving searching for and tracking multiple ground targets. Experimental results for vehicles equipped with cameras are presented. These illustrate the impact of the team configuration on overall system performance.

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“…This work builds on previous endeavors in decentralized data fusion [5,4] and active sensor networks [3]. The established architecture and methodology is used here.…”

Section: Related Workmentioning

confidence: 99%

“…As in [3], a control layer is implemented above the DDF framework. Figure 1 details the structure of an active sensing node.…”

Section: System Architecture and Approachmentioning

confidence: 99%

See 1 more Smart Citation

Synergies in Feature Localization by Air-Ground Robot Teams

Grocholsky

Bayraktar

Kumar

et al. 2006

Springer Tracts in Advanced Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The observation uncertainty of the sensor alone is still conditioned on all previous observations from all aircraft, thus a coordinated behavior results, as demonstrated by Grocholsky et al 22 for information filter based decentralized control. However, because this behavior does not take planned observations of other vehicles into account, every vehicle plans to obtain the most uncertainty reduction possible, in expectation, at the subsequent time step, even if another vehicle will already acquire the same information.…”

Section: Single and Pairwise Approximationsmentioning

confidence: 77%

Distributed Cooperative Search Using Information - Theoretic Costs for Particle Filters, with Quadrotor Applications

Hoffmann

Waslander

Tomlin

2006

AIAA Guidance, Navigation, and Control Conference and Exhibit

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Search and rescue missions can be efficiently and automatically performed by small, highly maneuverable unmanned aerial vehicle (UAV) teams. The search problem is complicated by a lack of prior information, nonlinear mapping between sensor observations and the physical world, and potentially non-Gaussian sensor noise models. To address these problems, a distributed control algorithm is proposed, using information theoretic methods with particle filters, to compute optimal control inputs for a multi-vehicle, coordinated localization of a stationary target. This technique exploits the structure of the probability distributions of the target state and of the sensor measurements to compute the control inputs that maneuver the UAVs to make observations that minimize the expected future uncertainty of the target state. Because the method directly uses the particle filter state and an accurate sensor noise model to compute the mutual information, it is no longer necessary to discard information by using linear and Gaussian approximations. To ensure safety of the vehicles, the algorithm incorporates collision avoidance and control authority constraints. The resulting information theoretic cost calculation is coupled amongst the vehicles and becomes prohibitive as the size of the UAV team becomes large. Therefore, single vehicle and pairwise approximations to the cost function are used that greatly reduce the computational burden and allow for development of a distributed algorithm for real-time optimization of vehicle trajectories. Simulation results are shown for a bearings-only sensor model with multiple vehicles. Initial flight tests of the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) show the feasibility of implementation of this algorithm on the quadrotor testbed and in real world situations.

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“…In [9,10], the configuration of a team of mobile robots was actively controlled by minimizing the expected error in tracking target positions, and a decentralised system architecture maximizing local information gains was presented in [2]. A reactive motion planner was reported in [7] that maximizes the shortest distance that a target needs to move in order to escape an observer's visibility region.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Multi-robot Target Tracking

Jung¹,

Sukhatme

Distributed Autonomous Robotic Systems 7

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Summary. Target tracking performance can be improved by using multiple robot trackers, but this requires a coordinated motion strategy among the robots. We propose an algorithm based on treating the densities of robots and targets as properties of the environment in which they are embedded. By suitably manipulating these densities a control law for each robot is proposed. The proposed algorithm has been tested through intensive simulations and a realrobot experiment. First, two different versions of the approach were evaluated by studying the performance change as the communication range among robots varies. The results showed that our treatment of the coordination problem is effective and efficient. Second, the developed system was tested on two Segway RMP robots, and the behaviors of the robots in a cooperative tracking experiment provide evidence that the proposed method controls multiple robots appropriately according to the target distribution change.

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Scalable Control of Decentralised Sensor Platforms

Cited by 42 publications

References 8 publications

Synergies in Feature Localization by Air-Ground Robot Teams

Synergies in Feature Localization by Air-Ground Robot Teams

Distributed Cooperative Search Using Information - Theoretic Costs for Particle Filters, with Quadrotor Applications

Cooperative Multi-robot Target Tracking

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