Cooperative multi-vehicle search and coverage problem in uncertain environments

Mirzaei, Mostafa; Sharifi, Farid; Gordon, Brandon W.; Rabbath, C.A.; Zhang, Youmin

doi:10.1109/cdc.2011.6161448

Cited by 29 publications

(21 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research work on fault-tolerant attitude control for spacecraft in the presence of actuator faults could not also be included but can be found in , , and . Current and future work will focus more on the multi-vehicles cooperative control where preliminary works on cooperative control have been presented in (Izadi et al, 2009b), (Izadi et al, 2011a), (Sharifi et al, 2010), , (Mirzaei et al, 2011) and (Qu & Zhang, 2011).…”

Section: Resultsmentioning

confidence: 99%

Fault Tolerant Flight Control Techniques with Application to a Quadrotor UAV Testbed

Zhang¹,

Chamseddine²

2012

Automatic Flight Control Systems - Latest Developments

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Fault Tolerant Flight Control Techniques with Application to a Quadrotor UAV Testbed

Zhang¹,

Chamseddine²

2012

Automatic Flight Control Systems - Latest Developments

View full text Add to dashboard Cite

“…Neural network, reinforcement learning [8], group dispersion pattern [9], K-shortest path search [10], [11] and mixed integer linear programming [12] have also been explored for efficient area coverage. Similarly, Voronoi partitioning [13], [14], [15], [16] has also been used to restrict the movement of each UAV to a specific partition to avoid overlaps in their paths. In contrast to the previous approaches, [5] efficiently covers the search region for detecting targets without storing the history of visits.…”

Section: A Efficient Area Coverage Based Approachesmentioning

confidence: 99%

Information merging in multi-UAV cooperative search

Khan

Yanmaz

Rinner

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

In this paper, we propose strategies for merging occupancy probabilities of target existence in multi-UAV cooperative search. The objective is to determine the impact of cooperation and type of information exchange on search time and detection errors. To this end, we assume that small-scale UAVs (e.g., quadrotors) with communication range limitations move in a given search region following pre-defined paths to locate a single stationary target. Local occupancy grids are used to represent target existence, to update its belief with local observations and to merge information from other UAVs. Our merging strategies perform Bayes updates of the occupancy probabilities while considering realistic limitations in sensing, communication and UAV movement-all of which are important for small-scale UAVs. Our simulation results show that information merging achieves a reduction in mission time from 27% to 70% as the number of UAVs grows from 2 to 5.

show abstract

“…know the target distribution states. Existing probability map [11] is used to describe target existing probability of each grid. Let ( , , ) denote target existing probability of grid ( , ) at time , let denote sensor detection precision, let denote sensor false probability, and let ( ) denote whether the target is detected or not at time.…”

Section: Pheromone Mapmentioning

confidence: 99%

“…Compared with stochastic and greedy searching algorithm, it is verified to be more effective [10]. In [11], the distributed approach integrating dynamic programming is proposed, and the objective is to gain more information of the whole team. By this method, each UAV can find its own flight path, respectively.…”

Section: Introductionmentioning

confidence: 99%

Distributed Cooperative Search Control Method of Multiple UAVs for Moving Target

Guan

2015

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

To reduce the impact of uncertainties caused by unknown motion parameters on searching plan of moving targets and improve the efficiency of UAV’s searching, a novel distributed Multi-UAVs cooperative search control method for moving target is proposed in this paper. Based on detection results of onboard sensors, target probability map is updated using Bayesian theory. A Gaussian distribution of target transition probability density function is introduced to calculate prediction probability of moving target existence, and then target probability map can be further updated in real-time. A performance index function combining with target cost, environment cost, and cooperative cost is constructed, and the cooperative searching problem can be transformed into a central optimization problem. To improve computational efficiency, the distributed model predictive control method is presented, and thus the control command of each UAV can be obtained. The simulation results have verified that the proposed method can avoid the blindness of UAV searching better and improve overall efficiency of the team effectively.

show abstract

Cooperative multi-vehicle search and coverage problem in uncertain environments

Cited by 29 publications

References 13 publications

Fault Tolerant Flight Control Techniques with Application to a Quadrotor UAV Testbed

Fault Tolerant Flight Control Techniques with Application to a Quadrotor UAV Testbed

Information merging in multi-UAV cooperative search

Distributed Cooperative Search Control Method of Multiple UAVs for Moving Target

Contact Info

Product

Resources

About