Event-triggered based state estimation for autonomous operation of an aerial robotic vehicle

In this article the problem of event-triggered (ET) state estimation for autonomous navigation of an aerial vehicle is investigated numerically. The aerial vehicle is considered as a general example of a nonlinear non-Gaussian system for state estimation under process and measurement noise. The motivation behind the problem is the conditions that the aerial vehicles are facing in extreme and hazardous environments due to constant exposure of the sensors and actuators to the high frequency process and measurement noises. Here we consider autonomous operation of a quadcopter for mapping of a radioactive environment, where the quadcopter may subject to radiations and non-Gaussian noises. Autonomous operation of the aerial vehicle with a limited available energy and for a longer period of time, demands an efficient management of the energy sources. Therefore, in this study we take the first step towards this goal by studying an event triggering strategy in which the data measured by the sensors is transmitted to the processing unit only if certain events happen. The sensor employed for navigation purpose is the inertial measurement unit, including accelerometers and gyroscopes, used to estimate the quadcopter states only when their measurements are informative. An event-triggered particle filtering (PF) state estimation technique is adopted for this application. The choice of particle filter as state-estimator is inevitable not only because of nonlinear and non-Gaussian nature of the system, but also because of non-Gaussianity of the conditional distribution of the posteriori probability density function resulting from the event triggering. In the proposed method, it is proved that particles are weighted differently in the case of event triggering and no triggering. The numerical results for robust nonlinear attitude stabilization of the quadcopter in the presence of event-triggered particle filter state estimation confirm the efficiency of the proposed method

Simultaneous localization and mapping in a multi-robot system in a dynamic environment with unknown initial correspondence

A basic assumption in most approaches to simultaneous localization and mapping (SLAM) is the static nature of the environment. In recent years, some research has been devoted to the field of SLAM in dynamic environments. However, most of the studies conducted in this field have implemented SLAM by removing and filtering the moving landmarks. Moreover, the use of several robots in large, complex, and dynamic environments can significantly improve performance on the localization and mapping task, which has attracted many researchers to this problem more recently. In multi-robot SLAM, the robots can cooperate in a decentralized manner without the need for a central processing center to obtain their positions and a more precise map of the environment. In this article, a new decentralized approach is presented for multi-robot SLAM problems in dynamic environments with unknown initial correspondence. The proposed method applies a modified Fast-SLAM method, which implements SLAM in a decentralized manner by considering moving landmarks in the environment. Due to the unknown initial correspondence of the robots, a geographical approach is embedded in the proposed algorithm to align and merge their maps. Data association is also embedded in the algorithm; this is performed using the measurement predictions in the SLAM process of each robot. Finally, simulation results are provided to demonstrate the performance of the proposed method

Event-triggered particle filtering and Cramer-Rao lower bound computation

In this article, an event-triggered particle filtering method is presented to estimate the states of stochastic nonlinear systems with the ultimate goal to reduce the information exchange in networked systems. In the event-triggered estimation, measurements are transferred to an estimator only if certain event conditions are satisfied. Using these event-triggered measurements leads to non-Gaussianity of the conditional posterior distribution in minimum mean square error estimators even in the presence of Gaussian process and measurement noises. Therefore, in this article, a particle filter–based method is employed to solve the non-Gaussianity issue in nonlinear systems due to event-triggered measurements. In the proposed scheme, when no information is sent to the estimator, particles weight update role is modified according to the event-triggering probability density function. To evaluate the performance of the proposed state estimation scheme, the conditional posterior Cramér–Rao lower bound is obtained using Monte Carlo simulations. The bound is also computed for nonlinear Gaussian systems with a Gaussian event-triggering mechanism as a special case. Finally, the efficiency of the proposed method is demonstrated for a networked interconnected four-tank system through simulation and a comparison study is also provided.Qatar Foundation; Qatar National Research FundScopu