Small unmanned aerial vehicles (UAVs) equipped with navigation and video capabilities can be used to perform target localization. Combining UAV state estimates with image data leads to bearing measurements of the target that can be processed to determine its position. This 3-D bearings-only estimation problem is nonlinear and traditional filtering methods are prone to biases, noisy estimates, and filter instabilities. The performance of the target localization is highly dependent on the vehicle trajectory, motivating the development of optimal UAV trajectories. This work presents methods for designing trajectories that increase the amount of information provided by the measurements and shows that these trajectories lead to enhanced estimation performance.The Fisher Information Matrix (FIM) is used to quantify the information provided by the measurements. Several objective functions based on the FIM are considered and the A-optimality criterion is shown to be the best suited for trajectory optimization in the 3-D bearings-only target localization problem. The resulting trajectories produce spirals, which increase the angular separation between measurements and reduce the range to the target, supporting geometric intuition. The problem of simultaneous target estimation and vehicle trajectory optimization is explored and the resulting algorithms produce vehicle trajectories that increase the information provided by the measurements, enhancing the target estimation performance by increasing accuracy, reducing uncertainty and improving filter convergence.

No abstract

Abstract-This paper extends the consensus-based bundle algorithm (CBBA), a distributed task allocation framework previously developed by the authors, to address complex missions for a team of heterogeneous agents in a dynamic environment. The extended algorithm proposes appropriate handling of time windows of validity for tasks, fuel costs of the vehicles, and heterogeneity in the agent capabilities, while preserving the robust convergence properties of the original algorithm. An architecture to facilitate real-time task replanning in a dynamic environment, along with methods to handle varying communication constraints and dynamic network topologies, is also presented. Simulation results and experimental flight tests in an indoor test environment verify the proposed task planning methodology for complex missions.

Abstract-This research presents a distributed chanceconstrained task allocation framework that can be used to plan for multi-agent networked teams operating in stochastic and dynamic environments. The algorithm employs an approximation strategy to convert centralized problem formulations into distributable sub-problems that can be solved by individual agents. A key component of the distributed approximation is a risk adjustment method that allocates individual agent risks based on a global risk threshold. The results show large improvements in distributed stochastic environments by explicitly accounting for uncertainty propagation during the task allocation process.