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With the increasing demand for Unmanned Aerial Vehicles (UAVs) in both military and civil applications, the ability for UAVs to automatically avoid obstacles and navigate to specific destinations has been receiving growing attention. However, most ...

Segmentation-based autonomous navigation has recently been presented as an appealing approach to guiding robotic platforms through crop rows without requiring perfect GPS localization. Nevertheless, current techniques are restricted to situations ...

• We propose two segmentation-based methods for autonomous navigation in orchards.
• Our algorithms are tested on unseen crops, such as vineyards and fruit orchards.
• We trained a neural network with a synthetic dataset, ensuring ...

The paper proposed a traversability learning method based on the human demonstration for generating risk cost maps. These maps aid mobile robots in identifying safe areas for reliable autonomous navigation over uneven terrain. Firstly, a maximum ...

In this paper, we introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents. The SEA utilizes semantic graph maps that intricately delineate the relationships ...

• Enhanced Navigation Capabilities: The SEA enhances navigation using semantic graph maps, improving localization and tasks.
• Robust Against Sensor Noise: SEA achieves robust navigation by leveraging semantic knowledge, even with sensor ...

Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regions. ...

• A systematic review of ground robots and their individual components used in precision weed management.
• Analysis of navigation technology, imaging sensors, and different weed management strategies.
• Investigate the potential of ...

Robotics and variable rate technology (VRT) have shown great potential for site-specific weed management (SSWM), but these technologies face several challenges like accurate weed identification, high initial cost, integration with existing ...

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• An end to end crop row detection pipeline without extra post-porocess is proposed.
• Each crop row is modeled as a collection of points on a line for detection.
• Crop row features are gatherd through pooling along columns and rows.

Vision-based autonomous navigation technology is vital important for unmanned driving of agricultural machinery and precise operation. Crop row detection, a fundamental task of vision-based navigation, has a significant impact on automatic ...

Semi-autonomous aircraft navigation is a high-risk domain where confidence on the prediction is required. For that, this paper introduces the use of conformal predictions strategies for regression problems. While standard approaches use an ...

The Inter-Satellite Link (ISL) technology plays a vital role in BeiDou Navigation Satellite System (BDS), and it’s a developmental trend of the future GNSS. However, ISL is insensitive to both the Earth’s rotation and the constellation’s overall ...

Orchard tasks often require robots to navigate along the central axes of tree rows in the dynamic orchard environment, making traditional map-based path planning methods unsuitable for these environments. In this work, we propose a practical ...

This article introduces a novel Deep Reinforcement Learning (DRL)-based approach for mapless navigation in Industrial Autonomous Mobile Robots, emphasizing advancements in generalization through Potential Risk State Augmentation (PRSA) and an ...

Autonomous navigation and information gathering in challenging environments are demanding since the robot’s sensors may be susceptible to non-negligible noise, its localization and mapping may be subject to significant uncertainty and drift, and ...

Reinforcement learning (RL) and imitation learning (IL) are quite two useful machine learning techniques that were shown to be potential in enhancing navigation performance. Basically, both of these methods try to find a policy decision function ...

Due to random sampling and the unpredictability of moving obstacles, it remains challenging for mobile robots to effectively learn navigation policies and accomplish obstacle avoidance safely. Overcoming such challenges can reduce the time cost ...

Unmanned aerial vehicles (UAVs) are a valuable source of data for a wide range of real-time applications, due to their functionality, availability, adaptability, and maneuverability. Working as mobile sensors, they can provide a cost-effective ...

Recently, camera-based approaches have been proposed to replace expensive LiDAR or real-time kinematic (RTK) based solutions for the unmanned tractors operating in an orchard-like environment. However, constructing a completely autonomous ...

• Multi-task neural network for trunk, obstacle, and traversable area detection.
• Frame transformation algorithm for perception results downsizing and transformation.
• Motion planning module for global path planning and control ...

• Proposing a CSP attention block to achieve feature enhancement.
• A fast receptive field expansion module is designed to expand feature receptive field.
• An adaptively spatial feature fusion module is introduced to implement feature ...

Autonomous navigation of agricultural devices plays an indispensable step for the realization of various field intelligent operation tasks. However, the accurate extraction of navigation path is still a challenging task due to the complex orchard ...

• This paper presents a novel one-shot domain adaptive real-time 3D detection method that addresses the challenge of insufficient target domain samples by designing a semantic-geometry-intensity space generator. This generator learns both ...

By introducing deep learning, LiDAR-based solutions have achieved impressive accuracy in 3D obstacle detection. However, gathering and labeling sufficient samples is the precondition for the effectiveness of existing solutions. This precondition ...

Crop row detection is the basis for the visual navigation of agricultural machinery. Previous research has typically developed crop detection schemes based on specific application objects, with cumbersome image processing steps. A ...

Nowadays, most agricultural robots rely on precise and expensive localisation, typically based on global navigation satellite systems (GNSS) and real-time kinematic (RTK) receivers. Unfortunately, the precision of GNSS localisation significantly ...

• We extend the classic visual teach & repeat to a novel teach & generalise approach.
• Our approach is capable of extracting unique features using a deep learning method.
• The approach only uses an uncalibrated monocular camera and ...