In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor.
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BACKGROUND: Rapid technological development has been opening new possibilities for children with disabilities. In particular, robots can enable and create new opportunities in therapy, rehabilitation, education, or leisure. OBJECTIVE: The aim of this article is to share experiences, challenges and learned lessons by the authors, all of them with experience conducting research in the field of robotics for children with disabilities, and to propose future directions for research and development. METHODS: The article is the result of several consensus meetings to establish future research priorities in this field. CONCLUSIONS: This article outlines a research agenda for the future of robotics in childcare and supports the establishment of R4C – Robots for Children, a network of experts aimed at sharing ideas, promoting innovative research, and developing good practices on the use of robots for children with disabilities. RESULTS: Robots have a huge potential to support children with disabilities: they can play the role of a play buddy, of a mediator when interacting with other children or adults, they can promote social interaction, and transfer children from the role of a spectator of the surrounding world to the role of an active participant. To fulfill their potential, robots have to be “smart”, stable and reliable, easy to use and program, and give the just-right amount of support adapted to the needs of the child. Interdisciplinary collaboration combined with user centered design is necessary to make robotic applications successful. Furthermore, real-life contexts to test and implement robotic interventions are essential to refine them according to real needs.
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An important issue in the field of motion control of wheeled mobile robots is that the design of most controllers is based only on the robot’s kinematics. However, when high-speed movements and/or heavy load transportation are required, it becomes essential to consider the robot dynamics as well. The control signals generated by most dynamic controllers reported in the literature are torques or voltages for the robot motors, while commercial robots usually accept velocity commands. In this context, we present a velocity-based dynamic model for differential drive mobile robots that also includes the dynamics of the robot actuators. Such model has linear and angular velocities as inputs and has been included in Peter Corke’s Robotics Toolbox for MATLAB, therefore it can be easily integrated into simulation systems that have been built for the unicycle kinematics. We demonstrate that the proposed dynamic model has useful mathematical properties. We also present an application of such model on the design of an adaptive dynamic controller and the stability analysis of the complete system, while applying the proposed model properties. Finally, we show some simulation and experimental results and discuss the advantages and limitations of the proposed model.
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Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life. In non-structured environments and in the presence of uncertainties, such degrees of intelligence are not easy to obtain. Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fashion without any need for hand-crafted features or policies. Especially in the context of robotics, in which the cost of real-world data is usually extremely high, reinforcement learning solutions achieving high sample efficiency are needed. In this paper, we propose a framework combining the learning of a low-dimensional state representation, from high-dimensional observations coming from the robot 's raw sensory readings, with the learning of the optimal policy, given the learned state representation. We evaluate our framework in the context of mobile robot navigation in the case of continuous state and action spaces. Moreover, we study the problem of transferring what learned in the simulated virtual environment to the real robot without further retraining using real-world data in the presence of visual and depth distractors, such as lighting changes and moving obstacles.
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The inefficiency of maintaining static and long-lasting safety zones in environments where actual risks are limited is likely to increase in the coming decades, as autonomous systems become more common and human workers fewer in numbers. Nevertheless, an uncompromising approach to safety remains paramount, requiring the introduction of novel methods that are simultaneously more flexible and capable of delivering the same level of protection against potentially hazardous situations. We present such a method to create dynamic safety zones, the boundaries of which can be redrawn in real-time, taking into account explicit positioning data when available and using conservative extrapolation from last known location when information is missing or unreliable. Simulation and statistical methods were used to investigate performance gains compared to static safety zones. The use of a more advanced probabilistic framework to further improve flexibility is also discussed, although its implementation would not offer the same level of protection and is currently not recommended.
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Abstract Long-haul travel does not constitute an obstacle for tourists to travel and is fast gaining the attention of tourists in new and unique experiences. This study was conducted to identify the long-haul travel motivation by international tourists to Penang. A total of 400 respondents participated in this survey, conducted around the tourist attractions in Penang, using cluster random sampling. However, only 370 questionnaires were only used for this research. Data were analysed using SPSS software 22 version. The findings, ‘knowledge and novelty seeking’ were the main push factors that drove long-haul travel by international tourists to Penang. Meanwhile, the main pull factor that attracts long- haul travel by international tourists to Penang was its ‘culture and history’. Additionally, there were partly direct and significant relationships between socio-demographic, trip characteristics and travel motivation (push factors and pull factors). Overall, this study identified the long-haul travel motivations by international tourists to Penang based on socio-demographic, trip characteristics and travel motivation and has indirectly helped in understanding the long-haul travel market particularly for Penang and Southeast Asia. This research also suggested for an effective marketing and promotion strategy in pro- viding useful information that is the key to attract international tourists to travel long distances. Keywords:
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