Rapid technological and scientific developments in the robotics field have led to the expansion of the use of service robots in domestic environments. The purpose of the study reported in this paper is to identify the factors that determine people’s psychology, attitude, and intention to use a social robotic system. A new conceptual framework is developed and empirically tested, using data from 347 people, performing Structural Equation Modeling analysis. The proposed framework incorporates various factors related to hedonic attitudes, utilitarian attitudes, normative beliefs, control beliefs, and personality traits. The results reveal predominantly the positive impact of normative beliefs and utilitarian and hedonic attitudes but, also, the negative impact of control beliefs on people’s intention to use social robot systems. Consequently, it is concluded that future clients are not only influenced by society’s general attitudes, perceptions, and prejudices towards technology but, also, by the functional benefits of social robots.

This work focuses on the efficiency improvement of grid-based Coverage Path Planning (CPP) methodologies in real-world applications with UAVs. While several sophisticated approaches are met in literature, grid-based methods are not commonly used in real-life operations. This happens mostly due to the error that is introduced during the region’s representation on the grid, a step mandatory for such methods, that can have a great negative impact on their overall coverage efficiency. A previous work on UAVs’ coverage operations for remote sensing, has introduced a novel optimization procedure for finding the optimal relative placement between the region of interest and the grid, improving the coverage and resource utilization efficiency of the generated trajectories, but still, incorporating flaws that can affect certain aspects of the method’s effectiveness. This work goes one step forward and introduces a CPP method, that provides three different ad-hoc coverage modes: the Geo-fenced Coverage Mode, the Better Coverage Mode and the Complete Coverage Mode, each incorporating features suitable for specific types of vehicles and real-world applications. For the design of the coverage trajectories, user-defined percentages of overlap (sidelap and frontlap) are taken into consideration, so that the collected data will be appropriate for applications like orthomosaicing and 3D mapping. The newly introduced modes are evaluated through simulations, using 20 publicly available benchmark regions as testbed, demonstrating their stenghts and weaknesses in terms of coverage and efficiency. The proposed method with its ad-hoc modes can handle even the most complex-shaped, concave regions with obstacles, ensuring complete coverage, no-sharp-turns, non-overlapping trajectories and strict geo-fencing. The achieved results demonstrate that the common issues encountered in grid-based methods can be overcome by considering the appropriate parameters, so that such methods can provide robust solutions in the CPP domain.

This paper focuses on Coverage Path Planning (CPP) methodologies, particularly in the context of multi-robot missions, to efficiently cover user-defined Regions of Interest (ROIs) using groups of UAVs, while emphasizing on the reduction of energy consumption and mission duration. Optimizing the efficiency of multi-robot CPP missions involves addressing critical factors such as path length, the number of turns, re-visitations, and launch positions. Achieving these goals, particularly in complex and concave ROIs with No-Go Zones, is a challenging task. This work introduces a novel approach to address these challenges, emphasizing the selection of launch points for UAVs. By optimizing launch points, the mission’s energy and time efficiency are significantly enhanced, leading to more efficient coverage of the selected ROIs. To further support our research and foster further exploration on this topic, we provide the open-source implementation of our algorithm and our evaluation mechanisms.

This dataset was collected to support Vehicle Routing Problem (VRP) optimization by providing structured time and distance matrices. A Third-Party Logistics (3PL) company granted access to its order management software, from which data on daily delivery problems involving pharmaceutical distribution were obtained. The dataset consists of carefully processed distance and time matrices, over a period of nine days. Each day’s problem involved 60-85 delivery stops that needed to be serviced. While the actual delivery routes covered only specific paths taken on the road, the generated matrices provide a complete view of travel distances and times between all locations, information essential for optimizing the routing process. To ensure confidentiality, only the structured matrices are provided, without the original address data. These matrices were generated using an API that computes travel durations based on historical traffic patterns, real-time data, and predictive models.

This paper focuses on Coverage Path Planning (CPP) methodologies, particularlyin the context of multi-robot missions, to efficiently cover user-defined Regions ofInterest (ROIs) using UAV swarms, while emphasizing on the reduction of energyconsumption and mission duration. Optimizing the efficiency of multi-robot CPPmissions involves addressing critical factors such as path length, the number ofturns, re-visitations, and launch positions. Achieving these goals, particularly incomplex and concave ROIs with No-Go Zones, is a challenging task. This workintroduces a novel approach to address these challenges, emphasizing the selectionof launch points for UAVs. By optimizing launch points, the mission’s energy andtime efficiency are significantly enhanced, leading to more efficient coverage ofthe selected ROIs. To further support our research and foster further exploration on this topic, we provide the open-source implementation of our algorithm andour evaluation mechanisms.