In this paper we introduce the core results of the project on visualisation and analysis of data collected from the vertical transport facilities. The aim of the project was to provide better user experience as well as to help building maintenance staff to increase productivity of their work. We elaborated a web-based system for vertical transportation, to cover the needs of (1) staff working on building maintenance, (2) people who are regularly using the facilities in the corresponding buildings.
One of important aspects of the project we present in this paper, was to embed the corresponding development activities into the learning and teaching activities within the School of Science, RMIT University, Australia. RMIT University aims to create transformative experiences for its students, so that the students are ready for their next steps in life and work. The 2020 RMIT strategic plan defines the goals that we will pursue together over the following years, and one of the priorities in this plan is . In our earlier work we presented how collaborative industrial project are embedded into engineering curriculum in the School of Science (disciplines and ) and the School of Engineering, @cite_6 @cite_7 @cite_16 @cite_15 @cite_9 . These project covered research areas of software engineering, robotics, as well as autonomous and automotive systems.
@cite_7 @cite_9 @cite_6 @cite_15 @cite_16
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This paper presents how collaborative industrial project are embedded into engineering curriculum at two departments: School of Science and the School of Engineering, at RMIT University, Australia. We introduce general structure of the industrial projects as Work Integrated Learning (WIL) modules, as well as provide a number of examples of recently completed projects. Industrial summer projects, which were running in the years 2015 16 in collaboration with ANZ, ABB, Alfred Hospital, etc., were pipelined with final year projects from the School of Engineering as well as with the Software Engineering Projects (Bachelor and Master level) from the School of Science. The goal of these projects was to enable continuity of activities as per industry requirements and enhance learning experience, as well as, employability of the students. All the projects were successfully completed, also receiving positive feedback from industry partners. Some of the projects led to student's employment within the companies that have sponsored the projects. With this approach, Future Designers Grant from the Department of State Development, Business and Innovation, Victoria, was efficiently implemented and a new product developed. After receiving Bosch Venture Forum Award in Germany, in June 2015 industry collaboration has extended to new partner, School of Science and activities continued over the summer. New design is implanted as well as large number of improvements. This paper presents how research on autonomous systems is embedded into the curriculum at two departments at the RMIT University, Australia: the School of Engineering and the School of Science. We introduce general structure of our Work Integrated Learning (WIL) modules and the recently completed projects having as a core component research and development of autonomous systems. We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity (SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which allows active learning of inverse models in high-dimensional redundant robots. This allows a robot to efficiently and actively learn distributions of parameterized motor skills policies that solve a corresponding distribution of parameterized tasks goals. The architecture makes the robot sample actively novel parameterized tasks in the task space, based on a measure of competence progress, each of which triggers low-level goal-directed learning of the motor policy parameters that allow to solve it. For both learning and generalization, the system leverages regression techniques which allow to infer the motor policy parameters corresponding to a given novel parameterized task, and based on the previously learnt correspondences between policy and task parameters. We present experiments with high-dimensional continuous sensorimotor spaces in three different robotic setups: (1) learning the inverse kinematics in a highly-redundant robotic arm, (2) learning omnidirectional locomotion with motor primitives in a quadruped robot, and (3) an arm learning to control a fishing rod with a flexible wire. We show that (1) exploration in the task space can be a lot faster than exploration in the actuator space for learning inverse models in redundant robots; (2) selecting goals maximizing competence progress creates developmental trajectories driving the robot to progressively focus on tasks of increasing complexity and is statistically significantly more efficient than selecting tasks randomly, as well as more efficient than different standard active motor babbling methods; (3) this architecture allows the robot to actively discover which parts of its task space it can learn to reach and which part it cannot. We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70 compared to a closest frontier exploration strategy and 57 compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state.