In this paper, the localization problem of the mobile robot (MR) with an energy harvesting sensor (EHS) is studied. A specific situation is regarded, which is that the MR equips with an EHS who owns the capacity for absorbing the energy from the surroundings. When the sensor energy is sufficient, the sensor can send its measurement to the filter. Otherwise, the sensor energy-induced measurement missing phenomenon will occur. For the sake of acquiring the measurement missing rate, the relationship between the amount of the sensor energy and its probability distribution is derived recursively at each time instant. The purpose of this paper is to seek out a feasible solution to the addressed MR localization (MRL) problem (MRLP) by devising the desired time-varying filter. Firstly, in the presence of the sensor energy-induced measurement missing phenomenon, an upper bound (UB) for the filtering error (FE) covariance (FEC) is acquired recursively. Then, the acquired UB is minimized by characterizing the desired filter gain adequately. Finally, a simulation is conducted to testify the availability of the proposed MRL algorithm (MRLA).

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 ${\left[0.0245\mathrm{m}0.0224\mathrm{m}0.0039\mathrm{rad}\right]}^T$ and the average localization errors using the conventional EKF are ${\left[1.0405\mathrm{m}2.2700\mathrm{m}0.1782\mathrm{rad}\right]}^T$, ${\left[0.4963\mathrm{m}0.3482\mathrm{m}0.0254\mathrm{rad}\right]}^T$ and ${\left[0.2774\mathrm{m}0.3897\mathrm{m}0.0353\mathrm{rad}\right]}^T$, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.

To address the challenge of parallel parking in narrow environments, a multi‐segment path planning method is proposed for autonomous unmanned vehicles (AUVs), which integrates circular arc and dual clothoid curves, ensuring continuous path curvature and minimal longitudinal space occupancy. First, considering the narrow environmental constraints and the parameters of AUV, the planning for the preparatory phase path and the parking‐in phase path are completed, respectively. The role of the preparatory phase path is to guide the AUV to the target point, and the parking‐in phase path can guide the AUV into the target parking space. Then, by using the particle swarm optimization (PSO) algorithm with a penalty function framework, a multi‐constraint nonlinear programming path function model is established to seek out the parking path. Subsequently, the pure pursuit (PP) algorithm is employed to control the movement of AUV along the planned path. Finally, the simulation result and real‐vehicle experiment are conducted, which show that the parking path planning method proposed in this article enables AUV to safely complete parking operations in the narrow environment.

This paper designs a resilient stubborn observer for switched autonomous systems (SASs) with an energy harvesting sensor (EHS). In order to extend the lifespan of the sensor, a sensor with the ability of harvesting energy is used. For restraining the effect of measurement outliers, a stubborn observer is designed which includes a saturation function with the varying saturation levels. The observer gain fluctuation is considered which is dealt with by the resilience filtering scheme. The aim of this paper is, considering observer gain fluctuation, the measurement outliers and an EHS for SASs, to design a resilient stubborn observer which can guarantee the error dynamics to meet the given performance requirement. Specifically, the existence conditions are derived firstly and then the resilient stubborn observer is designed by linear matrix inequality (LMI) technology. At the end, the performance of the designed observer is evaluated through a numerical simulation and a set of comparative experiments are implemented to further signify the merits of the designed observer.