EVOLUTIONS OF GROUP MANAGEMENT DEVELOPMENT OF MOBILE ROBOTIC PLATFORMS IN WAREHOUSING 4.0.

The subject of this research is the technology of management of mobile robot groups in the concept of Industry 4.0 and its composition. The purpose of this article is to find ways to implement an effective strategy for building and managing mobile robotic platforms in Warehousing, as a key tool of Lean Production. To achieve this goal, it is necessary to solve the following tasks: to analyze the management of supply chains in Smart Manufacturing, within Industry 4.0 and its impact on achieving the goals of Lean Production; to study the evolution of technologies used in Warehousing in the dynamics of the Industrial Revolution; to analyze the evolution of Warehouse Management Systems (WMS) as one of the most important components on the basis of which the requirements for automation of Warehousing automation in Smart Manufacturing with group management of mobile robotic platforms are implemented and achieved; to compare the impact of the technologies used by Warehousing 4.0 and Warehouse Management Systems on the key indicators of Lean Production. Results: One of the promising ways to achieve the effectiveness of the implementation of Lean Production tools in WMS systems is the use of Collaborative Robot System technology, which makes it possible to ensure a high density of product storage in Warehousing. However, modern mobile robotic platforms have their limitations both in the methods of loading and unloading products, and in the design. Therefore, the authors see the task in improving the design of mobile robotic platforms, which will develop a new intelligent group method of loading and unloading products, increasing the storage density for a variety of goods. Conclusions: The paper compares the impact of Warehousing 4.0 and Warehouse Management Systems on key Lean Production tools, which shows how the introduction of new group management technologies for robotic platforms in Warehousing 4.0 and Warehouse Management Systems (WMS) affects the effectiveness of Lean Production tools such as Heijunka, Just-in-time, 5S. This suggests that the introduction of new models and methods of managing complex warehouses with high density and chaotic storage of products, through the use of mobile robotic autonomous systems, will significantly optimize the process of supply chain management in Smart Manufacturing.

Development of novel robotic platforms for mechanical stress induction, and their effects on plant morphology, elements, and metabolism

This research evaluates the effect on herbal crops of mechanical stress induced by two specially developed robotic platforms. The changes in plant morphology, metabolite profiles, and element content are evaluated in a series of three empirical experiments, conducted in greenhouse and CNC growing bed conditions, for the case of basil plant growth. Results show significant changes in morphological features, including shortening of overall stem length by up to 40% and inter-node distances by up to 80%, for plants treated with a robotic mechanical stress-induction protocol, compared to control groups. Treated plants showed a significant increase in element absorption, by 20–250% compared to controls, and changes in the metabolite profiles suggested an improvement in plants’ nutritional profiles. These results suggest that repetitive, robotic, mechanical stimuli could be potentially beneficial for plants’ nutritional and taste properties, and could be performed with no human intervention (and therefore labor cost). The changes in morphological aspects of the plant could potentially replace practices involving chemical treatment of the plants, leading to more sustainable crop production.

Human inspired robotic path planning and heterogeneous robotic mapping

<p>One of the biggest challenges facing robotics is the ability for a robot to autonomously navigate real-world unknown environments and is considered by many to be a key prerequisite of truly autonomous robots. Autonomous navigation is a complex problem that requires a robot to solve the three problems of navigation: localisation, goal recognition, and path-planning. Conventional approaches to these problems rely on computational techniques that are inherently rigid and brittle. That is, the underlying models cannot adapt to novel input, nor can they account for all potential external conditions, which could result in erroneous or misleading decision making. In contrast, humans are capable of learning from their prior experiences and adapting to novel situations. Humans are also capable of sharing their experiences and knowledge with other humans to bootstrap their learning. This is widely thought to underlie the success of humanity by allowing high-fidelity transmission of information and skills between individuals, facilitating cumulative knowledge gain. Furthermore, human cognition is influenced by internal emotion states. Historically considered to be a detriment to a person's cognitive process, recent research is regarding emotions as a beneficial mechanism in the decision making process by facilitating the communication of simple, but high-impact information. Human created control approaches are inherently rigid and cannot account for the complexity of behaviours required for autonomous navigation. The proposed thesis is that cognitive inspired mechanisms can address limitations in current robotic navigation techniques by allowing robots to autonomously learn beneficial behaviours from interacting with its environment. The first objective is to enable the sharing of navigation information between heterogeneous robotic platforms. The second objective is to add flexibility to rigid path-planning approaches by utilising emotions as low-level but high-impact behavioural responses. Inspired by cognitive sciences, a novel cognitive mapping approach is presented that functions in conjunction with current localisation techniques. The cognitive mapping stage utilises an Anticipatory Classifier System (ACS) to learn the novel Cognitive Action Map (CAM) of decision points, areas in which a robot must determine its next action (direction of travel). These physical actions provide a shared means of understanding the environment to allow for communicating learned navigation information. The presented cognitive mapping approach has been trained and evaluated on real-world robotic platforms. The results show the successful sharing of navigation information between two heterogeneous robotic platforms with different sensing capabilities. The results have also demonstrated the novel contribution of autonomously sharing navigation information between a range-based (GMapping) and vision-based (RatSLAM) localisation approach for the first time. The advantage of sharing information between localisation techniques allows an individual robotic platform to utilise the best fit localisation approach for its sensors while still being able to provide useful navigation information for robots with different sensor types. Inspired by theories on natural emotions, this work presents a novel emotion model designed to improve a robot's navigation performance through learning to adapt a rigid path-planning approach. The model is based on the concept of a bow-tie structure, linking emotional reinforcers and behavioural modifiers through intermediary emotion states. An important function of the emotions in the model is to provide a compact set of high-impact behaviour adaptations, reducing an otherwise tangled web of stimulus-response patterns. Crucially, the system learns these emotional responses with no human pre-specifying the behaviour of the robot, hence avoiding human bias. The results of training the emotion model demonstrate that it is capable of learning up to three emotion states for robotic navigation without human bias: fear, apprehension, and happiness. The fear and apprehension responses slow the robot's speed and drive the robot away from obstacles when the robot experiences pain, or is uncertain of its current position. The happiness response increases the speed of the robot and reduces the safety margins around obstacles when pain is absent, allowing the robot to drive closer to obstacles. These learned emotion responses have improved the navigation performance of the robot by reducing collisions and navigation times, in both simulated and real-world experiments. The two emotion model (fear and happiness) improved performance the most, indicating that a robot may only require two emotion states (fear and happiness) for navigation in common, static domains.</p>

Human inspired robotic path planning and heterogeneous robotic mapping

<p>One of the biggest challenges facing robotics is the ability for a robot to autonomously navigate real-world unknown environments and is considered by many to be a key prerequisite of truly autonomous robots. Autonomous navigation is a complex problem that requires a robot to solve the three problems of navigation: localisation, goal recognition, and path-planning. Conventional approaches to these problems rely on computational techniques that are inherently rigid and brittle. That is, the underlying models cannot adapt to novel input, nor can they account for all potential external conditions, which could result in erroneous or misleading decision making. In contrast, humans are capable of learning from their prior experiences and adapting to novel situations. Humans are also capable of sharing their experiences and knowledge with other humans to bootstrap their learning. This is widely thought to underlie the success of humanity by allowing high-fidelity transmission of information and skills between individuals, facilitating cumulative knowledge gain. Furthermore, human cognition is influenced by internal emotion states. Historically considered to be a detriment to a person's cognitive process, recent research is regarding emotions as a beneficial mechanism in the decision making process by facilitating the communication of simple, but high-impact information. Human created control approaches are inherently rigid and cannot account for the complexity of behaviours required for autonomous navigation. The proposed thesis is that cognitive inspired mechanisms can address limitations in current robotic navigation techniques by allowing robots to autonomously learn beneficial behaviours from interacting with its environment. The first objective is to enable the sharing of navigation information between heterogeneous robotic platforms. The second objective is to add flexibility to rigid path-planning approaches by utilising emotions as low-level but high-impact behavioural responses. Inspired by cognitive sciences, a novel cognitive mapping approach is presented that functions in conjunction with current localisation techniques. The cognitive mapping stage utilises an Anticipatory Classifier System (ACS) to learn the novel Cognitive Action Map (CAM) of decision points, areas in which a robot must determine its next action (direction of travel). These physical actions provide a shared means of understanding the environment to allow for communicating learned navigation information. The presented cognitive mapping approach has been trained and evaluated on real-world robotic platforms. The results show the successful sharing of navigation information between two heterogeneous robotic platforms with different sensing capabilities. The results have also demonstrated the novel contribution of autonomously sharing navigation information between a range-based (GMapping) and vision-based (RatSLAM) localisation approach for the first time. The advantage of sharing information between localisation techniques allows an individual robotic platform to utilise the best fit localisation approach for its sensors while still being able to provide useful navigation information for robots with different sensor types. Inspired by theories on natural emotions, this work presents a novel emotion model designed to improve a robot's navigation performance through learning to adapt a rigid path-planning approach. The model is based on the concept of a bow-tie structure, linking emotional reinforcers and behavioural modifiers through intermediary emotion states. An important function of the emotions in the model is to provide a compact set of high-impact behaviour adaptations, reducing an otherwise tangled web of stimulus-response patterns. Crucially, the system learns these emotional responses with no human pre-specifying the behaviour of the robot, hence avoiding human bias. The results of training the emotion model demonstrate that it is capable of learning up to three emotion states for robotic navigation without human bias: fear, apprehension, and happiness. The fear and apprehension responses slow the robot's speed and drive the robot away from obstacles when the robot experiences pain, or is uncertain of its current position. The happiness response increases the speed of the robot and reduces the safety margins around obstacles when pain is absent, allowing the robot to drive closer to obstacles. These learned emotion responses have improved the navigation performance of the robot by reducing collisions and navigation times, in both simulated and real-world experiments. The two emotion model (fear and happiness) improved performance the most, indicating that a robot may only require two emotion states (fear and happiness) for navigation in common, static domains.</p>

Development of Extrospective Systems for Mobile Robotic Vehicles.

<p>Extrospection is the process of receiving knowledge of the outside world through the senses. On robotic platforms this is primarily focussed on determining distances to objects of interest and is achieved through the use of ranging sensors. Any hardware implemented on mobile robotic platforms, including sensors, must ideally be small in size and weight, have good power efficiency, be self-contained and interface easily with the existing platform hardware. The development of stable, expandable and interchangeable mobile robot based sensing systems is crucial to the establishment of platforms on which complex robotic research can be conducted and evaluated in real world situations. This thesis details the design and development of two extrospective systems for incorporation in the Victoria University of Wellington's fleet of mobile robotic platforms. The first system is a generic intelligent sensor network. Fundamental to this system has been the development of network architecture and protocols that provide a stable scheme for connecting a large number of sensors to a mobile robotic platform with little or no dependence on the existing hardware configuration of the platform. A prototype sensor network comprising fourteen infrared position sensitive detectors providing a short to medium distance ranging system (0.2 - 3 m) with a 360' field of view has been successfully developed and tested. The second system is a redesign of an existing prototype full-field image ranger system. The redesign has yielded a smaller, mobile version of the prototype system capable of ranging medium to long distances (0 - 15 m) with a 22.2' - 16.5' field-of-view. This ranger system can now be incorporated onto mobile robotic platforms for further research into the capabilities of full-field image ranging as a form of extrospection on a mobile platform.</p>

Development of Extrospective Systems for Mobile Robotic Vehicles.

<p>Extrospection is the process of receiving knowledge of the outside world through the senses. On robotic platforms this is primarily focussed on determining distances to objects of interest and is achieved through the use of ranging sensors. Any hardware implemented on mobile robotic platforms, including sensors, must ideally be small in size and weight, have good power efficiency, be self-contained and interface easily with the existing platform hardware. The development of stable, expandable and interchangeable mobile robot based sensing systems is crucial to the establishment of platforms on which complex robotic research can be conducted and evaluated in real world situations. This thesis details the design and development of two extrospective systems for incorporation in the Victoria University of Wellington's fleet of mobile robotic platforms. The first system is a generic intelligent sensor network. Fundamental to this system has been the development of network architecture and protocols that provide a stable scheme for connecting a large number of sensors to a mobile robotic platform with little or no dependence on the existing hardware configuration of the platform. A prototype sensor network comprising fourteen infrared position sensitive detectors providing a short to medium distance ranging system (0.2 - 3 m) with a 360' field of view has been successfully developed and tested. The second system is a redesign of an existing prototype full-field image ranger system. The redesign has yielded a smaller, mobile version of the prototype system capable of ranging medium to long distances (0 - 15 m) with a 22.2' - 16.5' field-of-view. This ranger system can now be incorporated onto mobile robotic platforms for further research into the capabilities of full-field image ranging as a form of extrospection on a mobile platform.</p>

Comparison of Perioperative Outcomes Using the da Vinci S, Si, X, and Xi Robotic Platforms for BABA Robotic Thyroidectomy

Background and Objectives: Robotic thyroidectomy via the bilateral axillo-breast approach (BABA), first introduced in Korea in 2008, has become a standard method of thyroid removal worldwide. The introduction of robotic surgical systems has enabled more patients to benefit from BABA robotic thyroidectomy, with good postoperative and excellent cosmetic results. To date, no studies have compared the benefits of the four currently available da Vinci robotic systems (S, Si, X, and Xi) for BABA robotic thyroidectomy. To determine the da Vinci model most suitable for BABA robotic thyroidectomy, the present study compared the perioperative outcomes in patients who underwent BABA robotic thyroidectomy using the four da Vinci models. Materials and Methods: This retrospective study evaluated outcomes in patients (n = 750) who underwent BABA robotic thyroidectomy using the four da Vinci systems from 2013 to 2019. The clinicopathologic data, including operation time, were compared. Substudy A compared the da Vinci models S and Si from 2013 to 2017, and substudy B compared models Si, X, and Xi from 2018 to 2019. Results: Substudy A, comparing the da Vinci S and Si systems, found no statistically significant differences between the two groups, whereas substudy B found that operation time was shorter in patients who underwent BABA robotic thyroidectomy with the da Vinci Xi system than with the Si and X systems. Conclusions: The da Vinci model Xi system can benefit patients undergoing BABA robotic thyroidectomy by shortening the operation time.