Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research.

In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR) device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP) with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision.

The applications of Inertial Measurement Unit (IMU) based on Micro Electro Mechanical System (MEMS) are widely used because of its low-cost, small volume and low energy consumption. Unlike the accelerometers and magnetometers, which are respectively sensitive to vibrations and ferrous materials interference, MEMS type gyroscope is able to provide stable orientation information for a short period of time. Compared with these two sensors, gyroscopes are neither disturbed by vibrations nor affected by metallic materials, and therefore it would be one of the primary sensors for industrial applications. Reviewing the most recently related works, accurate attitude estimations are usually obtained by fusing gyro integration. However, it is well known that series drifts will be induced after a long-term integration. Furthermore, according to the literature and the associated experiments, they all demonstrated that MEMS type gyroscopes have strong temperature correlation because of the silicon structure. Put it simply, the temperature variations will give rise to gyro drifts severely and thereby degrade the orientation estimation precision. To address this problem, this paper proposed a temperature modeling technique and a real-time drift pre-compensation procedure by applying autoregressive moving average (ARMA) technology. Different from the traditional modeling methods, the proposed method is able to describe transient drifting behaviors as well as steady state ones. Moreover, comparison studies using artificial neural networks (ANNs) are also presented. Experiments show that the proposed method provides a stable and reliable temperature-caused gyroscope drift pre-compensation. The developed method is also very suitable for the realization in a low-cost embedded system.