Coordinated Navigation of Two Agricultural Robots in a Vineyard: A Simulation Study.

The development of an effective agricultural robot presents various challenges in actuation, localization, navigation, sensing, etc., depending on the prescribed task. Moreover, when multiple robots are engaged in an agricultural task, this requires appropriate coordination strategies to be developed to ensure safe, effective, and efficient operation. This paper presents a simulation study that demonstrates a robust coordination strategy for the navigation of two heterogeneous robots, where one robot is the expert and the second robot is the helper in a vineyard. The robots are equipped with localization and navigation capabilities so that they can navigate the environment and appropriately position themselves in the work area. A modular collaborative algorithm is proposed for the coordinated navigation of the two robots in the field via a communications module. Furthermore, the robots are also able to position themselves accurately relative to each other using a vision module in order to effectively perform their cooperative tasks. For the experiments, a realistic simulation environment is considered, and the various control mechanisms are described. Experiments were carried out to investigate the robustness of the various algorithms and provide preliminary results before real-life implementation.

Efficacy of a Robot-Assisted Intervention in Improving Learning Performance of Elementary School Children with Specific Learning Disorders.

(1) Background: There has been significant recent interest in the potential role of social robots (SRs) in special education. Specific Learning Disorders (SpLDs) have a high prevalence in the student population, and early intervention with personalized special educational programs is crucial for optimal academic achievement. (2) Methods: We designed an intense special education intervention for children in the third and fourth years of elementary school with a diagnosis of a SpLD. Following confirmation of eligibility and informed consent, the participants were prospectively and randomly allocated to two groups: (a) the SR group, for which the intervention was delivered by the humanoid robot NAO with the assistance of a special education teacher and (b) the control group, for which the intervention was delivered by the special educator. All participants underwent pre- and post-intervention evaluation for outcome measures. (3) Results: 40 children (NAO = 19, control = 21, similar baseline characteristics) were included. Pre- and post-intervention evaluation showed comparable improvements in both groups in cognition skills (decoding, phonological awareness and reading comprehension), while between-group changes favored the NAO group only for some phonological awareness exercises. In total, no significant changes were found in any of the groups regarding the emotional/behavioral secondary outcomes. (4) Conclusion: NAO was efficient as a tutor for a human-supported intervention when compared to the gold-standard intervention for elementary school students with SpLDs.

Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques.

Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child's behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child's behavior.