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.

Unlike most human-engineered systems, many biological systems rely on emergent behaviors from low-level interactions, enabling greater diversity and superior adaptation to complex, dynamic environments. This study explores emergent decentralized rotation in the Loopy multicellular robot, composed of homogeneous, physically linked, 1-degree-of-freedom cells. Inspired by biological systems like sunflowers, Loopy uses simple local interactions-diffusion, reaction, and active transport of simulated chemicals, called morphogens-without centralized control or knowledge of its global morphology. Through these interactions, the robot self-organizes to achieve coordinated rotational motion and forms lobes-local protrusions created by clusters of motor cells. This study investigates how these interactions drive Loopy's rotation, the impact of its morphology, and its resilience to actuator failures. Our findings reveal two distinct behaviors: 1) inner valleys between lobes rotate faster than the outer peaks, contrasting with rigid body dynamics, and 2) cells rotate in the opposite direction of the overall morphology. The experiments show that while Loopy's morphology does not affect its angular velocity relative to its cells, larger lobes increase cellular rotation and decrease morphology rotation relative to the environment. Even with up to one-third of its actuators disabled and significant morphological changes, Loopy maintains its rotational abilities, highlighting the potential of decentralized, bio-inspired strategies for resilient and adaptable robotic systems.

Natural environments pose significant challenges for autonomous robot navigation, particularly due to their unstructured and ever-changing nature. Hiking trails, with their dynamic conditions influenced by weather, vegetation, and human traffic, represent one of these challenges. This work introduces a novel approach to autonomous hiking trail navigation that balances trail adherence with the flexibility to adapt to off-trail routes when necessary. The solution is a Traversability Analysis module that integrates semantic data from camera images with geometric information from LiDAR to create a comprehensive understanding of the surrounding terrain. A planner uses this traversability map to navigate safely, adhering to trails while allowing off-trail movement when necessary to avoid on-trail hazards or for safe off-trail shortcuts. The method is evaluated through simulation to determine the balance between semantic and geometric information in traversability estimation. These simulations tested various weights to assess their impact on navigation performance across different trail scenarios. Weights were then validated through autonomous field tests at the West Virginia University Core Arboretum, demonstrating the method’s effectiveness in a real-world environment.

This study presents a methodology to safely manipulate branches to aid various agricultural tasks. Humans in a real agricultural environment often manipulate branches to perform agricultural tasks effectively, but current agricultural robots lack this capability. This proposed strategy to manipulate branches can aid in different precision agriculture tasks, such as fruit picking in dense foliage, pollinating flowers under occlusion, and moving overhanging vines and branches for navigation. The proposed method modifies RRT* to plan a path that satisfies the branch geometric constraints and obeys branch deformable characteristics. Re-planning is done to obtain a path that helps the robot exert force within a desired range so that branches are not damaged during manipulation. Experimentally, this method achieved a success rate of 78% across 50 trials, successfully moving a branch from different starting points to a target region.

This study presents Flower Pose Estimation (FloPE), a real-time flower pose estimation framework for computationally constrained robotic pollination systems. Robotic pollination has been proposed to supplement natural pollination to ensure global food security due to the decreased population of natural pollinators. However, flower pose estimation for pollination is challenging due to natural variability, flower clusters, and high accuracy demands due to the flowers’ fragility when pollinating. This method leverages 3D Gaussian Splatting to generate photorealistic synthetic datasets with precise pose annotations, enabling effective knowledge distillation from a high-capacity teacher model to a lightweight student model for efficient inference. The approach was evaluated on both single and multi-arm robotic platforms, achieving a mean pose estimation error of 0.6 cm and 19.14 degrees within a low computational cost. Our experiments validate the effectiveness of FloPE, achieving up to 78.75% pollination success rate and outperforming prior robotic pollination techniques.

Due to the complexity of many decision making problems, tree search algorithms often have inadequate information to produce accurate transition models. This results in ambiguities (uncertainties for which there are multiple plausible models). Faced with ambiguities, robust methods have been used to produce safe solutions—often by maximizing the lower bound over the set of plausible transition models. However, they often overlook how much the representation of uncertainty can impact how a decision is made. This work introduces the Ambiguity Attitude Graph Search (AAGS), advocating for more comprehensive representations of ambiguities in decision making. Additionally, AAGS allows users to adjust their ambiguity attitude (or preference), promoting exploration and improving users’ ability to control how an agent should respond when faced with a set of plausible alternatives. Simulation in a dynamic sailing environment shows how environments with high entropy transition models can lead robust methods to fail. Results further demonstrate how adjusting ambiguity attitudes better fulfills objectives while mitigating this failure mode of robust approaches. Because this approach is a generalization of the robust framework, these results further demonstrate how algorithms focused on ambiguity have applicability beyond safety-critical systems.

This work presents the design of Stickbug, a six-armed, multi-agent, precision pollination robot that combines the accuracy of single-agent systems with swarm parallelization in greenhouses. Precision pollination robots have often been proposed to offset the effects of a decreasing population of natural pollinators, but they frequently lack the required parallelization and scalability. Stickbug achieves this by allowing each arm and drive base to act as an individual agent, significantly reducing planning complexity. Stickbug uses a compact holonomic Kiwi drive to navigate narrow greenhouse rows, a tall mast to support multiple manipulators and reach plant heights, a detection model and classifier to identify Bramble flowers, and a felt-tipped end-effector for contact-based pollination. Initial experimental validation demonstrates that Stickbug can attempt over 1.5 pollinations per minute with a 49% success rate. Additionally, a Bramble flower perception dataset was created and is publicly available alongside Stickbug’s software and design files.

Unlike most human-engineered systems, biological systems are emergent from low-level interactions, allowing much broader diversity and superior adaptation to complex environments. Inspired by the process of morphogenesis in nature, a bottom-up design approach for robot morphology is proposed to treat a robot's body as an emergent response to underlying processes rather than a predefined shape. This paper presents Loopy, a “Swarm-of-One” polymorphic robot testbed that can be viewed simultaneously as a robotic swarm and a single robot. Loopy's shape is determined jointly by self-organization and morphological computing using physically linked homogeneous cells. Experimental results show that Loopy can form symmetric shapes consisting of lobes. Using the same set of parameters, even small amounts of initial noise can change the number of lobes formed. However, once in a stable configuration, Loopy has an “inertia” to transfiguring in response to dynamic parameters. By making the connections among self-organization, morphological computing, and robot design, this work lays the foundation for more adaptable robot designs in the future.

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.

We present an approach to enhance wheeled planetary rover dead-reckoning localization performance by leveraging the use of zero-type constraint equations in the navigation filter. Without external aiding, inertial navigation solutions inherently exhibit cubic error growth. Furthermore, for planetary rovers that are traversing diverse types of terrain, wheel odometry is often unreliable for use in localization, due to wheel slippage. For current Mars rovers, computer vision-based approaches are generally used whenever there is a high possibility of positioning error; however, these strategies require additional computational power, energy resources, adequate features in the environment, and significantly slow down the rover traverse speed. To this end, we propose a navigation approach that compensates for the high likelihood of odometry errors by providing a reliable navigation solution that leverages non-holonomic vehicle constraints as well as state-aware pseudo-measurements (e.g., zero velocity and zero angular rate) updates during periodic stops. By using this, computationally expensive visual-based corrections could be performed less often. Experimental tests that compare against GPS-based localization are used to demonstrate the accuracy of the proposed approach. The source code, post-processing scripts, and example datasets associated with the paper are published in a public repository.

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.

Precision robotic pollination systems can not only fill the gap of declining natural pollinators, but can also surpass them in efficiency and uniformity, helping to feed the fast-growing human population on Earth. This paper presents the design and ongoing development of an autonomous robot named “BrambleBee”, which aims at pollinating bramble plants in a greenhouse environment. Partially inspired by the ecology and behavior of bees, BrambleBee employs state-of-the-art localization and mapping, visual perception, path planning, motion control, and manipulation techniques to create an efficient and robust autonomous pollination system.

This paper considers the fusion of carrier-phase differential GPS (CP-DGPS), peer-to-peer ranging radios, and low-cost inertial navigation systems (INS) for the application of relative navigation of small unmanned aerial vehicles (UAVs) in close formation-flight. A novel sensor fusion algorithm is presented that incorporates locally processed tightly coupled GPS/INS-based absolute navigation solutions from each UAV in a relative navigation filter that estimates the baseline separation using integer-fixed relative CP-DGPS and a set of peer-to-peer ranging radios. The robustness of the dynamic baseline estimation performance under conditions that are typically challenging for CP-DGPS alone, such as a high occurrence of phase breaks, poor satellite visibility/geometry due to extreme UAV attitude, and heightened multipath intensity, amongst others, is evaluated using Monte Carlo simulation trials. The simulation environment developed for this work combines a UAV formation flight control simulator with a GPS constellation simulator, stochastic models of the inertial measurement unit (IMU) sensor errors, and measurement noise of the ranging radios. The sensor fusion is shown to offer improved robustness for 3-D relative positioning in terms of 3-D residual sum of squares (RSS) accuracy and increased percentage of correctly fixed phase ambiguities. Moreover, baseline estimation performance is significantly improved during periods in which differential carrier phase ambiguities are unsuccessfully fixed. Note to Practitioners-This paper was motivated by the need to enhance the robustness of CP-DGPS/INS relative navigation. In particular, small UAVs exhibit fast dynamics and are often subjected to large and quickly changing bank angles. This in turn induces missed satellite observations and changes in the phase ambiguity. This paper suggests leveraging the emergence of Ultra Wideband ranging radios to directly observe the baseline separation. In this paper, we outline the details of the algorithm implementation. We then use a simulation to show that adding UWB greatly helps to enhance the robustness of the carrier ambiguity integer-resolving algorithm, which is necessary for improved solution accuracy. This work has extensions to ground vehicles, ocean buoys, and space vehicles. In future work, we will experimentally validate results.

Low-cost navigation can be performed for aircraft using the integration of Inertial Navigation System (INS) information in conjunction with a regulatory information source, such as Global Position System (GPS). For situations where GPS is unavailable, it is desirable to have an alternative information source in order to regulate the known drifting phenomenon experienced by INS. In this paper, the use of wide-field optical flow is explored to regulate INS drift for the purpose of GPS-denied navigation. An Unscented Information Filter (UIF) algorithm for Unmanned Aerial Vehicle (UAV) velocity and attitude estimation is proposed and evaluated with experimental flight data. The results demonstrate that the new filtering algorithm is capable of estimating the vehicle speed with approximately 1.4 m/s of error and regulating attitude errors to within 1.5 degrees standard deviation of error for both roll and pitch angles.

The purpose of this paper is to propose the application of an extended Kalman filter (EKF) for the sensors fusion task within the problem of aerial refueling for unmanned aerial vehicles (UAVs). Specifically, the EKF is used to combine the position data from a global positioning system (GPS) and a machine vision (MV)-based system for providing a reliable estimation of the tanker–UAV relative position throughout the docking and the refueling phase. The performance of the scheme has been evaluated using a virtual environment specifically developed for the study of the UAV aerial refueling problem. Particularly, the EKF-based sensor fusion scheme integrates GPS data with MV-based estimates of the tanker–UAV position derived through a combination of feature extraction, feature classification, and pose estimation algorithms. The achieved results indicate that the accuracy of the relative position using GPS or MV estimates can be improved by at least one order of magnitude with the use of EKF in lieu of other sensor fusion techniques.