Centralized Ensemble-Based Trajectory Planning of Cooperating Sensors for Estimating Atmospheric Dispersion Processes

Abstract

Abstract. Optimal coordination of multiple sensors is crucial for efficient atmospheric dispersion estimation. The proposed approach adaptively provides optimized trajectories with respect to sensor cooperation and uncertainty reduction of the process estimate. To avoid the timeconsuming solution of a complex optimal control problem, estimation and vehicle control are considered separate problems linked in a sequential procedure. Based on a partial differential equation model, the Ensemble Transform Kalman Filter is applied for data assimilation and generation of observation targets offering maximum information gain. A centralized model-predictive vehicle controller simultaneously provides optimal target allocation and collision-free path planning. Extending previous work, continuous measuring is assumed, which attaches more significance to the course of the trajectories. Local attraction points are introduced to draw the sensors to regions of high uncertainty. Moreover, improved target updates increase the sampling efficiency. A simulated test case illustrates the approach in comparison to non-attracted trajectories.

Path Planning for Chainable Non-holonomic System Based on Iterative Learning Control

“…As a result, the actual result of motion control often deviates from the theoretical simulation value, that is, the final configuration of the system does not coincide with the target configuration [3]. The most direct solution is to plan the path again for the system to move from the actual configuration to the target configuration [4][5][6]. However, the secondary path planning may not yield a feasible path, not to mention achieving the desired result, under the strong nonlinearity of the mathematical models for some non-holonomic systems [7,8].…”