Autonomous Flying Vehicle Research at the University of Southern California

Saripalli, Srikanth; Naffin, David J.; Sukhatme, Gaurav S.

doi:10.1007/978-94-017-2376-3_8

Cited by 24 publications

(11 citation statements)

References 2 publications

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“…The proposed decentralized search algorithm expresses individual and collective goals in four weighted vectors which, when summed, provide the desired heading h of the UAV as shown in Equation 1.…”

Section: Decentralized Search Algorithmmentioning

confidence: 99%

Optimizing a search strategy for multiple mobile agents

2007

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In this paper, we propose a rule-based search method for multiple mobile distributed agents to cooperatively search an area for mobile target detection. The collective goals of the agents are (1) to maximize the coverage of a search area without explicit coordination among the members of the group, (2) to achieve sufficient minimum coverage of a search area in as little time as possible, and (3) to decrease the predictability of the search pattern of each agent. We assume that the search space contains multiple mobile targets and each agent is equipped with a non-gimbaled visual sensor and a range-limited radio frequency sensor. We envision the proposed search method to be applicable to cooperative mobile robots, Unmanned Aerial Vehicles (UAVs), and Unmanned Underwater Vehicles (UUVs). The search rules used by each agent characterize a decentralized search algorithm where the mobility decision of an agent at each time increment is independently made as a function of the direction of the previous motion of the agent, the known locations of other agents, the distance of the agent from the boundaries of the search area, and the agent's knowledge of the area already covered by the group. Weights and parameters of the proposed decentralized search algorithm are tuned to particular scenarios and goals using a genetic algorithm. We demonstrate the effectiveness of the proposed search method in multiple scenarios with varying numbers of agents. Furthermore, we use the results of the tuning processes for different scenarios to draw conclusions on the role each weight and parameter plays during the execution of a mission.

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Section: Decentralized Search Algorithmmentioning

confidence: 99%

Optimizing a search strategy for multiple mobile agents

2007

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“…This form of factorization is only feasible when the robot's dynamics do not inhibit the separability. An extreme example of highly coupled DOF are helicopters; Saripalli et al (2002) have demonstrated that behavior-based control is still feasible for particular behavioral regimes.…”

Section: Behavior Compositionmentioning

confidence: 99%

Behavior-Based Methods for Modeling and Structuring Control of Social Robots

Shell¹,

Matarić²

2005

Cognition and Multi-Agent Interaction

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“…In [1], control strategies were developed for a single UAV to provide sensory data, as well as communication between and coordination of ground units. The efficiency of different trajectory planning algorithms was compared in [2] for a single UAV approaching multiple targets in a convex unobstructed space.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Control of UAVs for Localization of Intermittently Emitting Mobile Targets

Pack

DeLima

Toussaint

et al. 2009

IEEE Trans. Syst., Man, Cybern. B

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Compared with a single platform, cooperative autonomous unmanned aerial vehicles (UAVs) offer efficiency and robustness in performing complex tasks. Focusing on ground mobile targets that intermittently emit radio frequency signals, this paper presents a decentralized control architecture for multiple UAVs, equipped only with rudimentary sensors, to search, detect, and locate targets over large areas. The proposed architecture has in its core a decision logic which governs the state of operation for each UAV based on sensor readings and communicated data. To support the findings, extensive simulation results are presented, focusing primarily on two success measures that the UAVs seek to minimize: overall time to search for a group of targets and the final target localization error achieved. The results of the simulations have provided support for hardware flight tests.

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Autonomous Flying Vehicle Research at the University of Southern California

Cited by 24 publications

References 2 publications

Optimizing a search strategy for multiple mobile agents

Optimizing a search strategy for multiple mobile agents

Behavior-Based Methods for Modeling and Structuring Control of Social Robots

Cooperative Control of UAVs for Localization of Intermittently Emitting Mobile Targets

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