Decentralized Bayesian negotiation for cooperative search

Bourgault, F.; Furukawa, Tomonari; Durrant‐Whyte, Hugh

doi:10.1109/iros.2004.1389813

Cited by 53 publications

(46 citation statements)

References 11 publications

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“…Their purpose is to reduce the uncertainty about the target existence probability in its current cell and to avoid covering the same search area. But the information they get is from the flight base, which may be not enough [17]. So in our paper we present our framework to let the UAVs can decide where to go online step by step.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Framework for Probabilistic Search with Multi-UAVs

Qin¹,

Li²

2017

The 5th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2017)

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Abstract. This paper focuses on the task of searching a stationary target using a team of multiple unmanned aerial vehicles (UAV) with limited communication ranges and sensing capabilities. It is based on a probabilistic representation model of the search environment, in which each UAV keeps a probability map on the presence of the target for each cell. The UAVs make observations and update the map for multiple rounds according to the Bayesian rule. In this process, each UAV can exchange data with its neighbors who are in its communication range. The decision of finding the target can be made once its probability of presence in any cell reaches a threshold. In this work, we design an efficient framework to decide the movement of each UAV at each time step, and propose a novel data fusion strategy among UAVs. The simulation result shows that our framework achieves improvements on both the success rate and the time taken to complete the searching task. We also explore the impact of different parameters, like the sensor model, moving mobility model of each UAV, and the data fusion strategies associated with the moving patterns. The simulation results show that the correct rate on target identification can vary greatly under different parameter settings, indicating the importance of the selection of UAV searching components such as the sensor model and the mobility model.

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Section: Related Workmentioning

confidence: 99%

An Efficient Framework for Probabilistic Search with Multi-UAVs

Qin¹,

Li²

2017

The 5th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2017)

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“…At step 0, ) 0 ( Since the observation z ij (1) is not available at time 0, it seems impossible to predict more than one-step ahead. However, before the observation z ij (1) is made, we can assume that the innovation,…”

Section: A Prediction In N-step Optimisationmentioning

confidence: 99%

“…[1]- [4], to be performed with greater efficiency than a single robot. The accuracy of information available about the targets determines which task is to be performed.…”

Section: Introductionmentioning

confidence: 99%

Trajectory planning for multiple robots in bearing-only target localisation

Leung

Huang

Dissanayake

et al. 2005

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems

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-This paper provides a solution to the optimal trajectory planning problem in target localisation for multiple heterogeneous robots with bearing-only sensors. The objective here is to find robot trajectories that maximise the accuracy of the locations of the targets at a prescribed terminal time. The trajectory planning is formulated as an optimal control problem for a nonlinear system with a gradually identified model and then solved using nonlinear Model Predictive Control (MPC). The solution to the MPC optimisation problem is computed through Exhaustive Expansion Tree Search (EETS) plus Sequential Quadratic Programming (SQP). Simulations were conducted using the proposed methods. Results show that EETS alone performs considerably faster than EETS+SQP with only minor differences in information gain, and that a centralised approach outperforms a decentralised one in terms of information gain. We show that a centralised EETS provides a near optimal solution. We also demonstrate the significance of using a matrix to represent the information gathered.Index Terms -bearing only target localisation, multi-robot optimal trajectory planning, Extended Information Filter, Model Predictive Control, Sequential Quadratic Programming

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“…Examples of such missions include aerobiological sampling, persistent surveillance, formation control, distributed resource delivery, and target positioning [1,[3][4][5][6]. The tasks in these missions often require the agents to collaboratively sense, estimate, or reach agreement on global parameters/states, such as the states of the environment or shared variables related to task settings and assignments [7][8][9][10][11]. However, while low-cost agents have the potential to yield benefits such as scalability, cost-saving, and resiliency, these agents typically have limited onboard computation and communication resources.…”

Section: Introductionmentioning

confidence: 99%

Efficient distributed sensing using adaptive censoring based inference

Chowdhary

How

2013

2013 American Control Conference

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In many distributed sensing applications it is likely that only a few agents will have valuable information at any given time. Since wireless communication between agents is resource-intensive, it is important to ensure that the communication effort is focused on communicating valuable information from informative agents. This paper presents communication-efficient distributed sensing algorithms that avoid network cluttering by having only agents with high Value of Information (VoI) broadcast their measurements to the network, while others censor themselves. A novel contribution of the presented distributed estimation algorithm is the use of an adaptively adjusted VoI threshold to determine which agents are informative. This adaptation enables the team to better balance between the communication cost incurred and the long-term accuracy of the estimation. Theoretical results are presented establishing the almost sure convergence of the communication cost and estimation error to zero for distributions in the exponential family. Furthermore, validation through numerical simulations and real datasets show that the new VoI-based algorithms can yield improved parameter estimates than those achieved by previously published hyperparameter consensus algorithms while incurring only a fraction of the communication cost.

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Decentralized Bayesian negotiation for cooperative search

Cited by 53 publications

References 11 publications

An Efficient Framework for Probabilistic Search with Multi-UAVs

An Efficient Framework for Probabilistic Search with Multi-UAVs

Trajectory planning for multiple robots in bearing-only target localisation

Efficient distributed sensing using adaptive censoring based inference

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