Nanomaterials associated with plant growth and crop cultivation revolutionize traditional concepts of agriculture. However, the poor reiterability of these materials in agricultural applications necessitates the development of environmentally-friendly approaches. To address this, biocompatible gelatin nanoparticles (GNPs) as nanofertilizers with a small size (≈150 nm) and a positively charged surface (≈30 mV) that serve as a versatile tool in agricultural practices is designed. GNPs load agrochemical agents to improve maintenance and delivery. The biocompatible nature and small size of GNPs ensure unrestricted nutrient absorption on root surfaces. Furthermore, when combined with pesticides, GNPs demonstrate remarkable enhancements in insecticidal (≈15%) and weed-killing effects (≈20%) while preserving the efficacy of the pesticide. That GNPs have great potential for use in sustainable agriculture, particularly in inducing plant growth, specifically plant root growth, without fertilization and in enhancing the functions of agrochemical agents is proposed. It is suggested conceptual applications of GNPs in real-world agricultural practices.

Gelatin Nanoparticles Biocompatible gelatin nanoparticles (GNPs), with their small size and positive charge, present a breakthrough in sustainable agriculture. The GNPs as nanofertilizers enhance nutrient absorption and improve the delivery of agrochemicals. When combined with pesticides, the GNPs boost their effectiveness in controlling pests and weeds, demonstrating significant potential for advancing eco-friendly agricultural practices and plant growth. More in article number 2402899, Jangho Kim and co-workers.

Field robot systems have recently been applied in a wide range of research fields. Further automation, development, and activation of such systems require cooperation among heterogeneous robots. Classical control theory is inefficient in managing large-scale complex dynamic systems. Therefore, a discrete-event system based on the supervisory control theory must be introduced to overcome this limitation. In this article, we propose a hybrid system-based hierarchical control architecture using a supervisory control-based high-level controller and a traditional control-based low-level controller. The hybrid system and its dynamics are modeled through a formal method, called hybrid automata, and the behavior specifications are designed to express the control objectives for cooperation. In addition, modular supervisors that are more scalable and maintainable than a centralized supervisory controller were synthesized. The proposed hybrid system and hierarchical control architecture were implemented, validated, and evaluated for performance through a physics-based simulator and field tests. The experimental results confirmed that the robot team satisfied the given specifications and presented systematic results, validating the efficiency of the proposed control architecture.

In this paper, we investigate the effect of haptic cueing on a human operator's performance in the field of bilateral teleoperation of multiple mobile robots, particularly multiple unmanned aerial vehicles (UAVs). Two aspects of human performance are deemed important in this area, namely, the maneuverability of mobile robots and the perceptual sensitivity of the remote environment. We introduce metrics that allow us to address these aspects in two psychophysical studies, which are reported here. Three fundamental haptic cue types were evaluated. The Force cue conveys information on the proximity of the commanded trajectory to obstacles in the remote environment. The Velocity cue represents the mismatch between the commanded and actual velocities of the UAVs and can implicitly provide a rich amount of information regarding the actual behavior of the UAVs. Finally, the Velocity+Force cue is a linear combination of the two. Our experimental results show that, while maneuverability is best supported by the Force cue feedback, perceptual sensitivity is best served by the Velocity cue feedback. In addition, we show that large gains in the haptic feedbacks do not always guarantee an enhancement in the teleoperator's performance.

Nanomaterials associated with plant growth and crop cultivation revolutionize traditional concepts of agriculture. However, the poor reiterability of these materials in agricultural applications necessitates the development of environmentally-friendly approaches. To address this, biocompatible gelatin nanoparticles (GNPs) as nanofertilizers with a small size (≈150 nm) and a positively charged surface (≈30 mV) that serve as a versatile tool in agricultural practices is designed. GNPs load agrochemical agents to improve maintenance and delivery. The biocompatible nature and small size of GNPs ensure unrestricted nutrient absorption on root surfaces. Furthermore, when combined with pesticides, GNPs demonstrate remarkable enhancements in insecticidal (≈15%) and weed-killing effects (≈20%) while preserving the efficacy of the pesticide. That GNPs have great potential for use in sustainable agriculture, particularly in inducing plant growth, specifically plant root growth, without fertilization and in enhancing the functions of agrochemical agents is proposed. It is suggested conceptual applications of GNPs in real-world agricultural practices.

Gelatin Nanoparticles Biocompatible gelatin nanoparticles (GNPs), with their small size and positive charge, present a breakthrough in sustainable agriculture. The GNPs as nanofertilizers enhance nutrient absorption and improve the delivery of agrochemicals. When combined with pesticides, the GNPs boost their effectiveness in controlling pests and weeds, demonstrating significant potential for advancing eco-friendly agricultural practices and plant growth. More in article number 2402899, Jangho Kim and co-workers.

Field robot systems have recently been applied in a wide range of research fields. Further automation, development, and activation of such systems require cooperation among heterogeneous robots. Classical control theory is inefficient in managing large-scale complex dynamic systems. Therefore, a discrete-event system based on the supervisory control theory must be introduced to overcome this limitation. In this article, we propose a hybrid system-based hierarchical control architecture using a supervisory control-based high-level controller and a traditional control-based low-level controller. The hybrid system and its dynamics are modeled through a formal method, called hybrid automata, and the behavior specifications are designed to express the control objectives for cooperation. In addition, modular supervisors that are more scalable and maintainable than a centralized supervisory controller were synthesized. The proposed hybrid system and hierarchical control architecture were implemented, validated, and evaluated for performance through a physics-based simulator and field tests. The experimental results confirmed that the robot team satisfied the given specifications and presented systematic results, validating the efficiency of the proposed control architecture.

In this paper, we investigate the effect of haptic cueing on a human operator's performance in the field of bilateral teleoperation of multiple mobile robots, particularly multiple unmanned aerial vehicles (UAVs). Two aspects of human performance are deemed important in this area, namely, the maneuverability of mobile robots and the perceptual sensitivity of the remote environment. We introduce metrics that allow us to address these aspects in two psychophysical studies, which are reported here. Three fundamental haptic cue types were evaluated. The Force cue conveys information on the proximity of the commanded trajectory to obstacles in the remote environment. The Velocity cue represents the mismatch between the commanded and actual velocities of the UAVs and can implicitly provide a rich amount of information regarding the actual behavior of the UAVs. Finally, the Velocity+Force cue is a linear combination of the two. Our experimental results show that, while maneuverability is best supported by the Force cue feedback, perceptual sensitivity is best served by the Velocity cue feedback. In addition, we show that large gains in the haptic feedbacks do not always guarantee an enhancement in the teleoperator's performance.

In this paper, a novel decentralized control strategy for bilaterally teleoperating heterogeneous groups of mobile robots from different domains (aerial, ground, marine, and underwater) is proposed. By using a decentralized control architecture, the group of robots, which is treated as the slave side, is made able to navigate in a cluttered environment while avoiding obstacles, interrobot collisions, and following the human motion commands. Simultaneously, the human operator acting on the master side is provided with a suitable force feedback informative of the group response and of the interaction with the surrounding environment. Using passivity-based techniques, we allow the behavior of the group to be as flexible as possible with arbitrary split and join events (e.g., due to interrobot visibility/packet losses or specific task requirements) while guaranteeing the stability of the system. We provide a rigorous analysis of the system stability and steady-state characteristics and validate performance through human/hardware-in-the-loop simulations by considering a heterogeneous fleet of unmanned aerial vehicles (UAVs) and unmanned ground vehicles as a case study. Finally, we also provide an experimental validation with four quadrotor UAVs.

This study proposes an isotemporal task allocation system for autonomous tractor vehicles to improve agricultural task efficiency. The proposed system integrates Voronoi-based workspace partitioning and isotemporal task allocation. The method performs isotemporal tasks by considering the performance and status (including distance, speed, and fuel and battery capacity) of each tractor by adopting an optimal workspace partitioning method. Based on these factors, the system optimizes the sub-workspace allocation to minimize the task time deviation and ensure fair workload distribution among heterogeneous robots. The proposed system is evaluated through numerical verification and field evaluation in an agricultural environment. The results of the field evaluation show that the task efficiency is significantly improved, such as a 25.88% reduction in total task time and a 92.89% reduction in task time deviation under optimized conditions. In addition, the similar results of the two evaluations indicate high consistency and performance maintenance of the proposed system performance. Through the proposed system, it can be easily applied to various tractor-based vehicle cooperative task models, and efficient task performance can be expected by reducing idle time and allowing tractors to perform the next task.

Pest management is critical in agriculture, requiring solutions to ensure both productivity and environmental sustainability. This paper presents a novel pest management framework that employs cooperation between unmanned aerial vehicle (UAV) and unmanned ground vehicles (UGVs) to address the limitations of traditional approaches and enhance operational efficiency. The proposed system integrates mapping, perception, sampling, biosensing, and spraying techniques, optimizing resource utilization through a hierarchical task allocation mechanism. The UAV conducts wide-area exploration and provide global guidance, while UGVs execute autonomous localized operations, focusing on tasks such as real-time detection and sampling using manipulator. By incorporating strategies for maximizing information gain and employing heterogeneous multi-robot-based cooperation, the system overcomes challenges such as limited battery life and scalability in unstructured agricultural environments. The framework is evaluated via real-to-sim experiments, demonstrating its transformative potential in precision agriculture.