A bioinspired vision-based airflow sensing architecture for flow-guided robotic control

Robot navigation is one of the major fields of study in autonomous robotics (Oriolio, G., Ulivi, G. et al., 1998; Beetz, M., Arbuckle, T. et al., 2001; Wang, M. & Liu, J.N.K., 2004). In the present work, application of a multiresolutional filter for spatial information fusion in vision robot navigation is described. The novelty of the research is the enhanced estimation of the spatial sensory information in autonomous robotics by means of wavelet decomposition into multiresolutional levels and the fusion of the processed information while the processing takes place at the respective levels. Although wavelet-based information fusion is used in different applications (Hong, L., 1994; Hsin, H.C. & Li, A.C., 2006), its application in robotics is not common in literature. A wavelet-based filtering application in perceptual robotics is articulated earlier where human perception was central to the research. In the present work, optimal vision signal estimation for an autonomous robot is presented, where vision information is obtained by appropriate shaping of the visual sensory information and optimal estimation is obtained by a multiresolutional filter. One of the peculiarities of the research is essentially the wavelet-based dynamic filtering rather than static wavelet decomposition for image analysis or off-line decomposition for signal analysis. A multiresolutional dynamic filtering is a rather complex system. In this system vector form of wavelet decomposition is required as briefly mentioned by Hong (Hong, L., 1993) where the description of a multi-sensor fusion rather than wavelet decomposition was central to the study. In contrast to (Hong, L., 1993), in this work the multiresolutional dynamic filtering is central to the study together with the emphasis on application peculiarities in autonomous robotics; namely, several options for vector-wise operations are pointed out and one of the options is described in detail. Also, in autonomous robotics, the estimation of angular velocity is not a measurable quantity and it has to be estimated from the measurable state variables so that obstacle avoidance problem is taken care of. Therefore, the angular velocity estimation in real-time is a critical task in autonomous robotics and from this viewpoint the multiresolutional spatial information fusion process is desirable for enhanced robot performance. For the fusion process extended Kalman filtering can be used and is actually used in the present study. Perceptual vision employed in this study is one of the vision alternatives in robotics providing several essential possibilities characterizing the robot as to its performance. Especially perceptual robotics is gaining importance in the last decade as to its expected connection to human-like behaviour. The organization of the work is as follows. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

Simultaneous localization and mapping is a situation in which a mobile robot travels through an environment and concurrently makes a momentary map of the environment and uses that map to localize. The simultaneous localization and mapping is currently one of the most challenging problems in the field of autonomous mobile robots and providing a solution to SLAM may open doors to the world of truly autonomous robots. This paper provides a novel approach to Simultaneous Localization and Mapping problem based on estimation approach. The new approach is called Unscented HybridSLAM filter which addresses the linearization process of an autonomous mobile robot utilizing the second order Sterling polynomial interpolation specifically used for Unscented HybridSLAM algorithm. Using computer simulations, Unscented HybridSLAM and the associated theoretical interpolation is examined for a double-loop scenario and the efficacy of the Unscented HybridSLAM is validated.KeywordsSimultaneous Localization and Mapping (SLAM) problemEKFFastSLAMHybridSLAMUnscented hybridSLAMCluttered environmentDouble loop closingAbsolute error

In many occasions people need to go to certain places without having any prior knowledge about the environment. This situation may occur when the place is visited for the first time, or even when there is not any available map to situate us. In those cases, the signs of the environment are essential for achieving the goal. The same situation may happen for an autonomous robot. This kind of robots must be capable of solving this problem in a natural way. In order to do that, they must use the resources present in their environment. This paper presents a RFID-based signage system, which has been developed to guide and give important information to an autonomous robot. The system has been implemented in a real indoor environment and it has been successfully proved in the autonomous and social robot Maggie. At the end of the paper some experimental results, carried out inside our university building, are presented.

Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture Unified Network (SPAUN) neural model and neural navigation mechanism, we developed a brain-like biologically plausible mathematical model and applied it to robotic spatial navigation tasks. The proposed cognitive navigation framework adopts a one-dimensional ring attractor to model the head-direction cells, uses the sinusoidal interference model to obtain the grid-like activity pattern, and gets optimal movement direction based on the entire set of activities. The application of adaptive resonance theory (ART) could effectively reduce resource consumption and solve the problem of stability and plasticity in the dynamic adjustment network. This brain-like system model broadens the perspective to develop more powerful autonomous robotic navigation systems. The proposed model was tested under different conditions and exhibited superior navigation performance, proving its effectiveness and reliability.

Interactive robots not only need to react in predefined or deterministic scenarios but also learn and adapt in real-time, mirroring cognitive flexibility akin to human intelligence. Achieving this autonomy entails developing cognitive architectures that integrate reactive, deliberative and emergent capabilities. Thus, this paper presents MERLIN2, a hybrid cognitive architecture to generate, control, plan, and monitor behaviors in autonomous robots. This architecture combines reactive, deliberative, and emergent components, aiming to enhance adaptability in dynamic environments and make intelligent real-time decisions, thereby improving autonomy and performance. MERLIN2 comprises a deliberative system, based on a knowledge base and a symbolic planner; and a behavioral system composed of reactive components and several emergent components. It addresses core cognitive aspects like action selection, perception, memory, learning, reasoning, and explainability. MERLIN2 is evaluated in a simulated world and in the real world Carry My Luggage task from the RoboCup@Home. Therefore, the experimentation presented in this article showcases the architecture as a valid solution for autonomous robots.

In the future, autonomous robots are expected to operate in human environments. Therefore, the capability of these robots to recognize and interact with humans is important to be developed. This paper presents an overview of our work on autonomous patrolling humanoid robot. As an autonomous patrolling robot requires both navigation and cognition capabilities, novel hardware and software architecture are required. In this paper, multiple perception sensors and modular subsystem with different functions are developed. The modularity of the subsystems serves to complement each other, enabling the humanoid robot to complete the patrolling task.

Bionics has greatly improved the development of quadruped robot, in the paper we propose three bionic methods for the quadruped robot. These methods have been used for control method design, gait design and control architecture design. The quadruped robot control method has been divided into two parts, because the four-legged animals change their trot speed by adjustment of step length in swing phase and maintain their balance in support phase; control architecture is derived from the four-legged animals nerve system; gait of quadruped robot is based on event driven, and its trot gait is the same as German shepherd. Simulations and experiments are performed, which prove bionics design to be realizable for the quadruped robot.

In a large-scale epidemic, reducing direct contact among medical personnel, attendants and patients has become a necessary means of epidemic prevention and control. Intelligent vehicles and mobile robots in the hospital environment, such as disinfection vehicles, logistics vehicles, nursing robots, and guiding robots, play an important role in improving the operational efficiency of the medical system and promoting epidemic prevention and governance. Powerful capabilities of environmental spatial perception and reconstruction are the keys to accurate localization, navigation, and obstacle avoidance for intelligent vehicles and autonomous robots in such operations. Omnidirectional perception is becoming increasingly important and proliferative in autonomous vehicles and robots since its wide field of view significantly enhances the perception ability. However, the lack of dense and accurate 360° depth datasets has brought the challenge to the omnidirectional perception. In this paper, we propose a depth-sensing and reconstruction system to address this challenge in the large-scale indoor environment. First, we design an omnidirectional depth completion convolutional neural network model, in which a spherical normalized convolutional and the unit sphere area-based loss are introduced to extract features from cross-modal omnidirectional input with unequal sparsity and deal with the imbalanced data distribution and distortion in the panoramic input. In addition, we present a 3D reconstruction system by integrating our depth completion into omnidirectional localization and dense mapping. We evaluate our method on 360D large-scale indoor datasets and real-world sequences of a challenging hospital scene. Extensive experiments show that the proposed method outperforms the other state-of-the-art (SoTA) approaches in terms of depth completion and 3D reconstruction.

The Fourth Industrial Revolution has accelerated the integration of robotics and artificial intelligence (AI) into workplaces, creating opportunities to merge technology with real-world practice. In forensic pathology, workforce shortages and the dangers of investigating hazardous death scenes highlight the need for innovation. This study explored the use of a mobile, agile quadruped robot to investigate hanging death scenes, assessing its ability to navigate, capture evidence, and reduce human risk. The research focused on low-risk hanging death scenes as a starting point, with the aim of later extending to more hazardous environments such as chemical-related scenes, fire deaths, and mass disasters. The quadruped robot demonstrated advanced mobility, allowing routine inspection tasks and the collection of visual and environmental data with accuracy, safety, and frequency. The study reviewed existing literature on robotic applications in forensic investigations, developed an initial procedure for scene evaluation, and tested the approach on selected hanging cases. The findings discuss advantages, limitations, and lessons learned, offering insight into the potential role of robotics in forensic death scene work. While promising, technical and operational challenges remain before robotics can be fully integrated into routine forensic practice.

Autonomous navigation is an important feature that allows the robot to move independently from a point to another without a tele-operator. This feature makes mobile robots useful in many tasks that require transportation, exploration, surveillance, guidance, inspection …etc. Furthermore, autonomous robots deal with real time environments that tend to be complex, nonlinear and partially observed. They also operate with limited memory resources and tight time constraints. In this paper, we present an investigation related to mobile robot navigation. We first compare a group of classification algorithms using real traces of wall following robot navigation. Then we focus on the k Nearest Neighbors (KNN) algorithm to improve it and help it be more applicable in autonomous robot navigation. We applied a Nearest Neighbor set reduction technique to help reduce the high running time of KNN. The results indicate that KNN is a competing algorithm especially after decreasing the running time significantly by a factor of 19 and combining that with the KNN’s existing features. Results are further improved by applying an attribute selection method.

Quadruped robot, because of its excellent terrain adaptability and flexibility, has significant advantages in exploring unknown environments and performing complex tasks, so it has received widespread attention in scientific research and industrial fields in recent years. In this paper, the quadruped robot is studied from the aspects of structural design, and the advantages and disadvantages of different types of configurations are compared and analyzed, and then the typical motion control methods of the quadruped robot are introduced. combined with the research results at home and abroad, the problems and challenges encountered by the quadruped robot are analyzed. Finally, the development process of the quadruped robot is summarized and summarized, and the future development direction is prospected.

Increase of effectiveness of navigation in the case of autonomous mobile robots with limited sensory systems integrated that operate in different types of production facilities is considered in this research work. The challenge is to define a reliable solution in a cost-effective design. The research methodology integrates TRIZ within the framework of voice-of-use-table-performance function deployment (VOUT-PFD) design planning framework. The key user requirements and engineering specifications defined with VOUT-PFD have been analyzed in terms of correlations. For the identified sets of negative correlations, TRIZ Contradiction Matrix has been considered to formulate the generic areas for inventive problem-solving. Using the method of weighted analysis of interdependencies (AIDA), the compatible TRIZ vectors have been selected for guiding the design of the artificial intelligence (AI) algorithm. These vectors have been introduced in the framework of Complex System Design Technique (CSDT) in relation with generic modules of the AI system (algorithm, related inputs from the sensors and mechanical limitations of the robotic system) in order to design the navigation solution. In this article eight areas of possible improvements using AI algorithms have been found and the first area of research, which represents the robot construction is further detailed. The major result of this paper is that it shows a structured way in which inventive problem-solving thinking can lead to possible improvement areas regarding navigation of autonomous mobile robots (AMRs) in industrial environment.

Path Control of Quadruped Robot through Convolutional Neural Networks

Rethinking the Ph.D. dissertation in botany: Widening the circle.

The role of a robot is to save money and manpower in the real world. When the robot accomplishes this role to realize an autonomous function, unification of the system is a central issue. Hence, it is a subject to construct the control architecture as an unified method of intelligent control. Some research works about the error recovery ststem are carried out on manipulators, manufacturing systems, computer networks and so on. However, the hierarchical error recovery system has not been studied so much for autonomous mobile robots. In this paper, we propose a hierarchical control architecture HALAS (Hierarchical Adaptive and Learning Architecture System) as a control architecture for an autonomous mobile robot. The main feature of HALAS is the hierarchical error recovery function. We applied HALAS to an air conditioning equipment inspection robot. The validity of the proposed system is shown through some experimental results.