Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is [0.0245m0.0224m0.0039rad]T and the average localization errors using the conventional EKF are [1.0405m2.2700m0.1782rad]T, [0.4963m0.3482m0.0254rad]T and [0.2774m0.3897m0.0353rad]T, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.

Can a Skywalker Localize the Midpoint of a Rope?

This article poses a question about a simple localization problem. The question is if an oblivious walker on a line segment can localize the midpoint of the line segment in a finite number of steps observing the direction (i.e., Left or Right) and the distance to the nearest end point. This problem arises from self-stabilizing location problems by autonomous mobile robots with limited visibility , which is an abstract model attracting a wide interest in distributed computing. Contrary to appearances, it is far from trivial whether this simple problem is solvable, and it is not settled yet. This article is concerned with three variants of the problem with a minimal relaxation and presents self-stabilizing algorithms for them. We also show an easy impossibility theorem for bilaterally symmetric algorithms.

Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study

Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and constraints. These indoor environments are limited in physical space and there is the localization problem, due to GPS unavailability. Therefore, this work aims to give a step toward the usage of drones for indoor farming and livestock management. To investigate on the drone positioning in these workspaces, two visual simultaneous localization and mapping (VSLAM)—LSD-SLAM and ORB-SLAM—algorithms were compared using a monocular camera onboard a small drone. Several experiments were carried out in a greenhouse and a dairy farm barn with the absolute trajectory and the relative pose error being analyzed. It was found that the approach that suits best these workspaces is ORB-SLAM. This algorithm was tested by performing waypoint navigation and generating maps from the clustered areas. It was shown that aerial VSLAM could be achieved within these workspaces and that plant and cattle monitoring could benefit from using affordable and off-the-shelf drone technology.

A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

The localization of an acoustic source has attracted much attention in the scientific community, having been applied in several different real-life applications. At the same time, the use of neural networks in the acoustic source localization problem is not common; hence, this work aims to show their potential use for this field of application. As such, the present work proposes a deep feed-forward neural network for solving the acoustic source localization problem based on energy measurements. Several network typologies are trained with ideal noise-free conditions, which simplifies the usual heavy training process where a low mean squared error is obtained. The networks are implemented, simulated, and compared with conventional algorithms, namely, deterministic and metaheuristic methods, and our results indicate improved performance when noise is added to the measurements. Therefore, the current developed scheme opens up a new horizon for energy-based acoustic localization, a field where machine learning algorithms have not been applied in the past.