Algorithms, Hardware Systems, and Virtual Environments for Perception-Guided Robotic Weed Management

Weed competition is one of the most limiting factors affecting yield and profitability in crop production. In addition to the shortage and cost of labor for weed management, the widespread occurrence of herbicide-resistant weeds due to the heavy reliance on herbicides is threatening agricultural sustainability and global food security. The introduction of robot-based automation for weed management has great potential to effectively address weed issues and reduce the drudgery of manual labor. The progress with robotic weed control requires improvements in the robots’ perception to understand complex agricultural environments and weed control actuators. Vision-based robot perception, a process of identifying and interpreting visual information in order to represent and understand the environment, is one of the most promising routes toward robotic weed management. This dissertation aims to develop algorithms and systems for perception-guided robotic weed management, where effective weed detection is essential. Despite the promise of robotic weed management, several technological advances must be made in the areas of plant detection and weeding actuation before robots are fully capable of autonomous weeding. In this regard, this dissertation has four specific objectives towards achieving precision weed management. The first objective is to study the influence of image quality and light consistency on the performance of convolutional neural networks (CNNs) for weed detection. State-of-the-art CNNs rely on a vast number of training images, which are time-consuming and expensive to collect and annotate. The second objective is to develop an image synthesis and semi-supervised learning pipeline to reduce the need for annotated training images for weed detection. Virtual environments are powerful tools and have been widely used for developing robotic systems. However, virtual environments for agriculture are lacking. The third objective is to develop a photometric-based framework to facilitate the synthesis of 3D agricultural vegetation scenes that are both geometrically and optically detailed. In the fourth objective, algorithms and hardware systems have been developed for vision-guided automatic micro-volume herbicide spray using linearly actuated nozzles on a mobile robot platform. The system precisely applies a micro-volume of herbicide liquid on every single weed plant. Compared to the traditional application approach where herbicides are broadcasted, the system greatly reduces herbicide usage

Multirobot Routing Algorithms for Robots Operating in Vineyards.

We consider the problem of multirobot routing in vineyards, a task motivated by our ongoing project aiming at creating a corobotic system to implement precision irrigation on large-scale commercial vineyards. The problem is related to a combinatorial optimization problem on graphs called 'team orienteering.' Team orienteering is known to be NP-hard, thus motivating the development of heuristic solutions that can scale to large problem instances. We propose three different parameter-free approaches informed by the domain we consider and compare them against a general purpose heuristic developed previously. In numerous benchmarks derived from data gathered in a commercial vineyard, we demonstrate that our solutions outperform the general purpose heuristic and are scalable, thus allowing us to solve instances with tens of thousands of vertices in the graphs. Note to Practitioners-Routing problems with budget and motion constraints are pervasive to many applications. In particular, the structural constraints considered in this problem are found not only in agricultural environments but also in warehouse logistics and other domains where goods are arranged along regular linear structures. This article proposes and analyzes algorithms that can be applied when multiple agents must be coordinated in these environments. In particular, by utilizing domain-specific knowledge, the solutions proposed in this article outperform general purpose approaches that poorly scale with the size of the environment. The algorithms we present also ensure that no collisions occur between robots-an aspect normally neglected in algorithms previously proposed to solve the team orienteering problem

Multirobot Routing Algorithms for Robots Operating in Vineyards

We consider the problem of multirobot routing in vineyards, a task motivated by our ongoing project aiming at creating a corobotic system to implement precision irrigation on large-scale commercial vineyards. The problem is related to a combinatorial optimization problem on graphs called 'team orienteering.' Team orienteering is known to be NP-hard, thus motivating the development of heuristic solutions that can scale to large problem instances. We propose three different parameter-free approaches informed by the domain we consider and compare them against a general purpose heuristic developed previously. In numerous benchmarks derived from data gathered in a commercial vineyard, we demonstrate that our solutions outperform the general purpose heuristic and are scalable, thus allowing us to solve instances with tens of thousands of vertices in the graphs. Note to Practitioners-Routing problems with budget and motion constraints are pervasive to many applications. In particular, the structural constraints considered in this problem are found not only in agricultural environments but also in warehouse logistics and other domains where goods are arranged along regular linear structures. This article proposes and analyzes algorithms that can be applied when multiple agents must be coordinated in these environments. In particular, by utilizing domain-specific knowledge, the solutions proposed in this article outperform general purpose approaches that poorly scale with the size of the environment. The algorithms we present also ensure that no collisions occur between robots-an aspect normally neglected in algorithms previously proposed to solve the team orienteering problem