Semi-structured Outdoor Environments Research Articles

2D LIDAR 스캐너와 파티클 필터 레이저빔 모델 기반의 과수 로봇의 주간 내 자율주행

In mountainous orchards, agricultural tasks, such as crop protection and harvesting, are characterized as being labor intensive and dangerous. An autonomous orchard robot that can execute these unattended seems a promising alternative to increase task operability. An essential function in the development of an autonomous orchard robot is navigation, which is usually based on tree-row detection from LIDAR scan data by using navigational algorithms. This research applies a probabilistic particle filter (PF) algorithm with a novel laser-beam model for the autonomous in-row navigation of an orchard robot. The navigational accuracy of the algorithm is assessed in a Dutch apple orchard over a distance of 500 m, with the robot driving at two velocities: 0.25 m/s and 0.50 m/s. At both speeds, almost 50% of the observed lateral deviations were lower than 0.05 m from the optimal navigation line. With the use of the PF algorithm, the robot navigated itself between six patterns of tree rows with artificially removed trees. Some lateral deviations exceeded 0.10 m when three adjacent trees were missing in both tree rows. Based on these results, a PF with a laser beam model is an accurate and robust algorithm for the autonomous in-row navigation in semi-structured outdoor environments, such as orchards.

Autonomous In-Row Navigation of an Orchard Robot with a 2D LIDAR Scanner and Particle Filter with a Laser-Beam Model

In mountainous orchards, agricultural tasks, such as crop protection and harvesting, are characterized as being labor intensive and dangerous. An autonomous orchard robot that can execute these unattended seems a promising alternative to increase task operability. An essential function in the development of an autonomous orchard robot is navigation, which is usually based on tree-row detection from LIDAR scan data by using navigational algorithms. This research applies a probabilistic particle filter (PF) algorithm with a novel laser-beam model for the autonomous in-row navigation of an orchard robot. The navigational accuracy of the algorithm is assessed in a Dutch apple orchard over a distance of 500 m, with the robot driving at two velocities: 0.25 m/s and 0.50 m/s. At both speeds, almost 50% of the observed lateral deviations were lower than 0.05 m from the optimal navigation line. With the use of the PF algorithm, the robot navigated itself between six patterns of tree rows with artificially removed trees. Some lateral deviations exceeded 0.10 m when three adjacent trees were missing in both tree rows. Based on these results, a PF with a laser beam model is an accurate and robust algorithm for the autonomous in-row navigation in semi-structured outdoor environments, such as orchards.

Data fusion and path-following controllers comparison for autonomous vehicles

This paper presents a comparative study of two path-following controllers developed for guiding autonomous vehicles in semi-structured outdoor environments. Part of this paper is focused on the performance of two path-following controllers, which are implemented using two different approaches, the first using fuzzy logic and the second using chained systems theory. The control effort and the errors magnitude along the path are evaluated in a comparative way. A magnetic guidance system for autonomous vehicles navigation in semi-structured outdoor environments is also described, integrating redundant encoders data and absolute positioning data provided by on-board magnetic sensors and magnetic markers buried in the road. Simulation and experimental results are presented showing the effectiveness of the overall control system.

Feature extraction for outdoor mobile robot navigation based on a modified Gauss–Newton optimization approach

This paper discusses the problem of feature detection for semi-structured outdoor environments such as campuses and parks using laser range sensors. In these environments, commonly encountered natural features that can be very useful for mobile robot navigation include edges (large discontinuity) and circles (e.g., trees, pillars). The term feature is used to denote objects which are “likely” to be detectable when the sensor is moved to new locations. Note that there has been no systematic approach for feature detection in outdoor environments. In this paper, we present an algorithm for feature detection. The algorithm consists of data segmentation and parameter acquisition. A modified Gauss–Newton method is proposed for fitting circle parameters iteratively. Experimental results show that the proposed algorithm is efficient in detecting features for semi-structured outdoor environments and is applicable to real time simultaneous localization and mapping.