Human-robot swarm interaction with limited situational awareness

This paper studies how an operator with limited situational awareness can collaborate with a swarm of simulated robots. The robots are distributed in an environment with wall obstructions. They aggregate autonomously but are unable to form a single cluster due to the obstructions. The operator lacks the bird’s-eye perspective, but can interact with one robot at a time, and influence the behavior of other nearby robots. We conducted a series of experiments. They show that untrained participants had marginal influence on the performance of the swarm. Expert participants succeeded in aggregating 85% of the robots while untrained participants, with bird’s-eye view, succeeded in aggregating 90%. This demonstrates that the controls are sufficient for operators to aid the autonomous robots in the completion of the task and that lack of situational awareness is the main difficulty. An analysis of behavioral differences reveals that trained operators learned to gain superior situational awareness

Redesign in the textile industry: Proposal of a methodology for the insertion of circular thinking in product development processes

Despite the growing attention toward negative environmental impacts generated by the textile industry, companies face challenges in achieving sustainable and circular economy (CE) transition. The literature has so far lacked a systematic effort to analyze how textile companies can insert CE elements in their new product development process (NPD), especially regarding the proposition of methodologies that can better assist the companies in this regard. This study aims to identify good green innovation and CE practices in NPD adopted by textile companies and propose a methodology from Design Thinking (DT) to insert circular thinking in NPD. To that end, we conducted the research in two steps: (i) narrative bibliographic review and (ii) field research. The bibliographic review was conducted in the “Web of Science”, “Scopus”, and “Scielo” databases. The field research was executed with four textile companies. Our results show that companies tend to consider socio-environmental aspects at different stages of the development of new products. However, there is opportunities for improvement, especially through the use of ideas from DT. The proposed methodology is composed of two main cycles: the design cycle (DT stages) and the consumption cycle (subsequent stages). It encompasses the five main stages of the DT and the three macro phases of NPD of the textile industry. The ideas coming from the DT, especially creativity, focus on the user and stakeholder integration, assist in the development of innovative and circular solutions. The methodology presents how companies can work on reuse, recycling, and manufacturing issues, so that CE occurs. In the end, we evaluated, together with experts, the applicability of the proposed use of ideas of DT in practical cases. The research advances the discussions on NPD in the textile sector, especially on its potential to contribute to the transition to CE. It explores how DT assists in inserting circular thinking into the NPD and presents alternatives for companies to develop circular products and insert green innovations in their NPD

Parallel Distributional Prioritized Deep Reinforcement Learning for Unmanned Aerial Vehicles

This work presents a study on parallel and distributional deep reinforcement learning applied to the mapless navigation of UAVs. For this, we developed an approach based on the Soft Actor-Critic method, producing a distributed and distributional variant named PDSAC, and compared it with a second one based on the traditional SAC algorithm. In addition, we also embodied a prioritized memory system into them. The UAV used in the study is based on the Hydrone vehicle, a hybrid quadrotor operating solely in the air. The inputs for the system are 23 range findings from a Lidar sensor and the distance and angles towards a desired goal, while the outputs consist of the linear, angular, and, altitude velocities. The methods were trained in environments of varying complexity, from obstacle-free environments to environments with multiple obstacles in three dimensions. The results obtained, demonstrate a concise improvement in the navigation capabilities by the proposed approach when compared to the agent based on the SAC for the same amount of training steps. In summary, this work presented a study on deep reinforcement learning applied to mapless navigation of drones in three dimensions, with promising results and potential applications in various contexts related to robotics and autonomous air navigation with distributed and distributional variants.Comment: 7 pages, 6 figures. Approved at LARS 202

Relationship between physical attributes and heat stress in dairy cattle from different genetic groups.

Dairy cattle raised under harsh conditions have to adapt and prevent heat stress. The aim of this study was to evaluate physical characteristics and their association with heat tolerance in different genetic groups of dairy cattle. Thickness of the skin and coat, length and number of hairs, body measurements, as well as physiological parameters and body temperatures by infrared thermography were determined in 19 Holstein and 19 Girolando (½ and ¾ Holstein) cows. The Holstein cattle were less tolerant to heat stress than Girolando (GH50 and GH75 Holstein), because of the difficulty in dissipating heat due to the larger body size, as well as thicker and longer hairs. The correlations between physical characteristics, physiological parameters, and thermographic measurements prove to be inconsistent among genetic groups and therefore are not predictive of heat tolerance, while the regressions of morphometric characteristics on physiological and thermographic measures were not significant. Thus, the physical characteristics were not good predictors of physiological indices and thermographic temperature and so should not be used

Relationship between physical attributes and heat stress in dairy cattle from different genetic groups.

Dairy cattle raised under harsh conditions have to adapt and prevent heat stress. The aim of this study was to evaluate physical characteristics and their association with heat tolerance in different genetic groups of dairy cattle. Thickness of the skin and coat, length and number of hairs, body measurements, as well as physiological parameters and body temperatures by infrared thermography were determined in 19 Holstein and 19 Girolando (½ and ¾ Holstein) cows. The Holstein cattle were less tolerant to heat stress than Girolando (GH50 and GH75 Holstein), because of the difficulty in dissipating heat due to the larger body size, as well as thicker and longer hairs. The correlations between physical characteristics, physiological parameters, and thermographic measurements prove to be inconsistent among genetic groups and therefore are not predictive of heat tolerance, while the regressions of morphometric characteristics on physiological and thermographic measures were not significant. Thus, the physical characteristics were not good predictors of physiological indices and thermographic temperature and so should not be used