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.

Line detection is an important problem in computer vision, graphics and autonomous robot navigation. Lines detected using a laser range sensor (LRS) mounted on a robot can be used as features to build a map of the environment, and later to localize the robot in the map, in a process known as Simultaneous Localization and Mapping (SLAM). We propose an efficient algorithm for line detection from LRS data using a novel hopping-points Singular Value Decomposition (SVD) and Hough transform-based algorithm, in which SVD is applied to intermittent LRS points to accelerate the algorithm. A reverse-hop mechanism ensures that the end points of the line segments are accurately extracted. Line segments extracted from the proposed algorithm are used to form a map and, subsequently, LRS data points are matched with the line segments to localize the robot. The proposed algorithm eliminates the drawbacks of point-based matching algorithms like the Iterative Closest Points (ICP) algorithm, the performance of which degrades with an increasing number of points. We tested the proposed algorithm for mapping and localization in both simulated and real environments, and found it to detect lines accurately and build maps with good self-localization.

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

Map generation by a robot in a cluttered and noisy environment is an important problem in autonomous robot navigation. This paper presents algorithms and a framework to generate 2D line maps from laser range sensor data using clustering in spatial (Euclidean) and Hough domains in noisy environments. The contributions of the paper are: (1) it shows the applicability of density-based clustering methods and mathematical morphological techniques generally used in image processing for noise removal from laser range sensor data; (2) it presents a new algorithm to generate straight-line maps by applying clustering in the spatial domain; (3) it presents a new algorithm for robot mapping using clustering in a Hough domain; and (4) it presents a new framework to load, delete, install or update appropriate kernels in the robot remotely from the server. The framework provides a means to select the most appropriate kernel and fine-tune its parameters remotely from the server based on online feedback, which proves to be very efficient in dynamic environments with noisy conditions. The accuracy and performance of the techniques presented in this paper are discussed with conventional line segment-based EKF-SLAM and the results are compared.