相关论文: Learned Visual Navigation for Under-Canopy Agricul…
The livestock industry faces several challenges, including labor-intensive management, the threat of predators and environmental sustainability concerns. Therefore, this paper explores the integration of quadruped robots in extensive…
Rapid progress in embedded computing hardware increasingly enables on-board image processing on small robots. This development opens the path to replacing costly sensors with sophisticated computer vision techniques. A case in point is the…
There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous…
Aquaculture is a big marine industry and contributes to securing global food demands. Underwater vehicles such as remotely operated vehicles (ROVs) are commonly used for inspection, maintenance, and intervention (IMR) tasks in fish farms.…
Wheeled-legged robots combine the efficiency of wheels with the obstacle negotiation of legs, yet many state-of-the-art systems rely on costly actuators and sensors, and fall-recovery is seldom integrated, especially for wheeled-legged…
Agricultural robots have emerged as powerful members in agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement, resulting in limited mobility and poor adaptability.…
In autonomous driving, perception systems are piv otal as they interpret sensory data to understand the envi ronment, which is essential for decision-making and planning. Ensuring the safety of these perception systems is fundamental for…
Mobile ground robots require perceiving and understanding their surrounding support surface to move around autonomously and safely. The support surface is commonly estimated based on exteroceptive depth measurements, e.g., from LiDARs.…
This paper introduces an advanced AI-driven perception system for autonomous quadcopter navigation in GPS-denied indoor environments. The proposed framework leverages cloud computing to offload computationally intensive tasks and…
Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric…
The adoption of mobile robotic platforms in complex environments, such as agricultural settings, requires these systems to exhibit a flexible yet effective architecture that integrates perception and control. In such scenarios, several…
Autonomous ground vehicles have been designed for the purpose of that relies on ranging and bearing information received from forward looking camera on the Formation control . A visual guidance control algorithm is designed where real time…
Researchers and robotic development groups have recently started paying special attention to autonomous mobile robot navigation in indoor environments using vision sensors. The required data is provided for robot navigation and object…
Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on…
The use of an efficient coverage planning method is key for autonomous navigation in agricultural environments, where a robot must cover large areas of crops. This paper generally reviews the current state of the art of coverage path…
The miniaturisation of sensors and processors, the advancements in connected edge intelligence, and the exponential interest in Artificial Intelligence are boosting the affirmation of autonomous nano-size drones in the Internet of Robotic…
The development of precision agriculture has gradually introduced automation in the agricultural process to support and rationalize all the activities related to field management. In particular, service robotics plays a predominant role in…
We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert…
Autonomous aerial navigation in dense natural environments remains challenging due to limited visibility, thin and irregular obstacles, GNSS-denied operation, and frequent perceptual degradation. This work presents an improved deep…
Estimating terrain traversability in off-road environments requires reasoning about complex interaction dynamics between the robot and these terrains. However, it is challenging to create informative labels to learn a model in a supervised…