Robotic pollinators not only can aid farmers by providing more cost effective and stable methods for pollinating plants but also benefit crop production in environments not suitable for bees such as greenhouses, growth chambers, and in outer space. Robotic pollination requires a high degree of precision and autonomy but few systems have addressed both of these aspects in practice. In this paper, a fully autonomous robot is presented, capable of precise pollination of individual small flowers. Experimental results show that the proposed system is able to achieve a 93.1% detection accuracy and a 76.9% ‘pollination’ success rate tested with high-fidelity artificial flowers.

Ensuring human safety has been one of the most critical considerations within the field of human–robot interaction. To explore the effects of working with autonomous robots on human coworkers’ perceived workload and job performance, two experiments were conducted in this study. Eight participants were recruited in the first experiment. Results revealed an increase in both subjective and objective workload measurements: compared to the baseline “no robot” to “empty payload” condition, the sum of NASA Task Load Index (NASA-TLX) scores increased from 129.3 (58.8) to 147.6 (53.7) ( $p$ -value = 0.041) and the pupil diameter increased from 4.07 (0.64) mm to 4.11 (0.67) mm; while working with a full payload robot, the sum of NASA-TLX scores increased to 151.7 (55.9) ( $p$ -value = 0.010) and the pupil diameter increased to 4.12 (0.66) mm. Increased task completion time (3.1% for empty payload condition and 6.6% for full payload condition) showed a decrease in human productivity. However, this slight human output reduction was compensated by the substantial gain from the robot. Similar effects of autonomous mobile robots on participants’ NASA-TLX scores and task completion time were observed in the second experiment, where eight participants performed the order picking and sorting tasks in a high-fidelity grocery store setting. Results from the study suggested the feasibility of applying fully autonomous mobile robots in Wholesale and Retail Trade settings to improve human–robot team productivity while prioritizing physical safety and reasonable increases in mental workload.

A fundamental aspect of biological intelligence, from microbes to megafauna, is the ability to forage for the provisions required to sustain life. This sometimes underrated ability to seek out, identify, and use objects of interest in an environment with limited prior knowledge is an important capability that is also needed by robots. Many robotics applications can be modeled as foraging problems, such as search and rescue, wildlife tracking, crop pollination and harvesting, mining and in-situ-resource utilization, and scientific data/sample collection.

Localization based on scalar field map matching (e.g., using gravity anomaly, magnetic anomaly, topographics, or olfaction maps) is a potential solution for navigating in Global Navigation Satellite System (GNSS)-denied environments. In this paper, a scalable framework is presented for cooperatively localizing a group of agents based on map matching given a prior map modeling the scalar field. In order to satisfy the communication constraints, each agent in the group is assigned to different subgroups. A locally centralized cooperative localization method is performed in each subgroup to estimate the poses and covariances of all agents inside the subgroup. Each agent in the group, at the same time, could belong to multiple subgroups, which means multiple pose and covariance estimates from different subgroups exist for each agent. The improved pose estimate for each agent at each time step is then solved through an information fusion algorithm. The proposed algorithm is evaluated with two different types of scalar field based simulations. The simulation results show that the proposed algorithm is able to deal with large group sizes (e.g., 128 agents), achieve 10-m level localization performance with 180 km traveling distance, while under restrictive communication constraints.

Retail robots have increasingly become popular in retail stores, serving as collaborative assistants by completing tedious and repetitive tasks. As key participants in a retail environment, understanding how these robots influence the movements and behaviors of nearby customers can provide valuable insights to improve robot ergonomics. This article examined neuromuscular information changes with and without the presence of a retail robot. Sixteen participants were recruited to perform item picking and sorting tasks in a high-fidelity retail setting. Surface electromyography (EMG) signals were collected from four muscles—biceps brachii, brachioradialis, upper trapezius and erector spinae (ES), to measure muscle activity and EMG-EMG coherence. A global decrease in power spectral density (PSD) was observed across all monitored muscles with the robot's presence, indicating a reduction in overall muscular energy expenditure. The introduction of the retail robot also led to a significant reduction in the peak values and waveform length of the ES muscle. These findings not only highlight the potential of retail robots to enhance the overall shopping experience but also provide crucial insights for enhancing ergonomics during robotic design.