Search Results (35)

This paper presents an intelligent navigation wearable device for visually impaired individuals. The system aims to improve their independent travel capabilities and reduce the negative emotional impacts associated with visible disability indicators in travel tools. It employs an RGB-D camera and an inertial measurement unit (IMU) sensor to facilitate real-time obstacle detection and recognition via advanced point cloud processing and YOLO-based target recognition techniques. An integrated intelligent interaction module identifies the core obstacle from the detected obstacles and translates this information into multidimensional auxiliary guidance. Users receive haptic feedback to navigate obstacles, indicating directional turns and distances, while auditory prompts convey the identity and distance of obstacles, enhancing spatial awareness. The intuitive vibrational guidance significantly enhances safety during obstacle avoidance, and the voice instructions promote a better understanding of the surrounding environment. The device adopts an arm-mounted design, departing from the traditional cane structure that reinforces disability labeling and social stigma. This lightweight mechanical design prioritizes user comfort and mobility, making it more user-friendly than traditional stick-type aids. Experimental results demonstrate that this system outperforms traditional white canes and ultrasonic devices in reducing collision rates, particularly for mid-air obstacles, thereby significantly improving safety in dynamic environments. Furthermore, the system’s ability to vocalize obstacle identities and distances in advance enhances spatial perception and interaction with the environment. By eliminating the cane structure, this innovative wearable design effectively minimizes social stigma, empowering visually impaired individuals to travel independently with increased confidence, ultimately contributing to an improved quality of life. Full article

The increasing use of Unmanned Aerial Vehicles (UAVs) demands enhanced flight safety systems. This study presents the development of an affordable and efficient Sense and Avoid (S&A) system for small fixed-wing UAVs, typically under 25 kg and fly at speeds of up to 15 m/s. The system integrates multiple non-cooperative sensors, two ultrasonic sensors, two laser rangefinders, and one LiDAR, along with a Pixhawk 6X flight controller and a Raspberry Pi CM4 companion computer. A collision avoidance algorithm utilizing the Vector Field Histogram method was implemented to process sensor data and generate real-time trajectory corrections. The system was validated through experiments using a ground rover, demonstrating successful obstacle detection and avoidance with real-time trajectory updates at 10 Hz. Full article

Mobility limitations due to congenital disabilities, accidents, or illnesses pose significant challenges to the daily lives of individuals with disabilities. This study presents a novel design for a multifunctional intelligent vehicle, integrating head recognition, eye-tracking, Bluetooth control, and ultrasonic obstacle avoidance to offer an innovative mobility solution. The smart vehicle supports three driving modes: (1) a nostril-based control system using MediaPipe to track displacement for movement commands, (2) an eye-tracking control system based on the Viola–Jones algorithm processed via an Arduino Nano board, and (3) a Bluetooth-assisted mode for caregiver intervention. Additionally, an ultrasonic sensor system ensures real-time obstacle detection and avoidance, enhancing user safety. Extensive experimental evaluations were conducted to validate the effectiveness of the system. The results indicate that the proposed vehicle achieves an 85% accuracy in nostril tracking, over 90% precision in eye direction detection, and efficient obstacle avoidance within a 1 m range. These findings demonstrate the robustness and reliability of the system in real-world applications. Compared to existing assistive mobility solutions, this vehicle offers non-invasive, cost-effective, and adaptable control mechanisms that cater to a diverse range of disabilities. By enhancing accessibility and promoting user independence, this research contributes to the development of inclusive mobility solutions for disabled and elderly individuals. Full article

This paper presents a new approach for enhancing autonomous vehicle navigation and obstacle avoidance based on the integration of reinforcement learning with multiple sensors for navigation. The proposed system is designed to enable a reinforcement learning decision algorithm capable of making real-time decisions in aiding the adaptive capability of a vehicle. This method was tested on a prototype vehicle with navigation based on a Ublox Neo 6M GPS and a three-axis magnetometer, while for obstacle detection, this system uses three ultrasonic sensors. The use of a model-free reinforcement learning algorithm and use of an effective sensor for obstacle avoidance (instead of LiDAR and a camera) provides the proposed system advantage in terms of computational requirements, adaptability, and overall cost. Our experiments show that the proposed method improves navigation accuracy substantially and significantly advances the ability to avoid obstacles. The prototype vehicle adapts very well to the conditions of the testing track. Further, the data logs from the vehicle were analyzed to check the performance. It is this cost-effective and adaptable nature of the system that holds some promise toward a solution in situations where human intervention is not feasible, or even possible, due to either danger or remoteness. In general, this research showed how the application of reinforcement learning combined with sensor fusion enhances autonomous navigation and makes vehicles perform more reliably and intelligently in dynamic environments. Full article

One of the critical challenges in mobile robotics is obstacle avoidance, ensuring safe navigation in dynamic environments. In this sense, this work presents a comparative study of two intelligent control approaches for mobile robot obstacle avoidance based on a fuzzy architecture. The first approach is a neuro-fuzzy interface that combines neural networks’ learning capabilities with fuzzy logic’s rule-based reasoning, offering a flexible and adaptable control strategy. The second is a classic Mamdani fuzzy system that relies on human-defined fuzzy rules, providing an intuitive approach to control. A key contribution of this work is the development of a fast comprehensive, model-based dataset for neural network training generated without the need for real sensor data. The results show the evaluation of these two systems’ performance, robustness, and computational efficiency using low-cost ultrasonic sensors on a Pioneer 3DX robot within the Coppelia Sim environment. Full article

In the current study, which focuses on the operational safety problem in intelligent three-dimensional garages, an obstacle avoidance measurement and control scheme for the AGV parking robot is proposed. Under the premise of high-precision distance detection using Kalman filtering, a mathematical model of a brushless DC (BLDC) motor with full-speed range hybrid control is established. MATLAB/Simulink (R2022a) is used to build the control model, which has dual closed-loop vector-controlled motors in the low- to medium-speed range, with photoelectric encoders for speed feedback. The simulation results show that, at lower to medium speeds, the maximum overshoot of the output response curve is 1.5%, and the response time is 0.01 s. However, at higher speeds, there is significant jitter in the speed output waveform. Therefore, the speed feedback is switched to a sliding mode observer (SMO) instead of the original speed sensor at high speeds. Experiments show that, based on the SMO, the problem of speed waveform jitter at high motor speeds can be significantly improved, and the BLDC motor system has strong robustness. The above shows that the motor speed under the full-speed range hybrid control system can meet the AGV control and safety requirements. Full article

Autonomous navigation in dynamic environments is a significant challenge in robotics. The primary goals are to ensure smooth and safe movement. This study introduces a control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). It enhances autonomous robot navigation in dynamic environments with a focus on collision-free path planning. The strategy uses a path-planning technique to develop a trajectory that allows the robot to navigate smoothly while avoiding both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experimental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model’s capability in ensuring collision-free navigation, employing a path-planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, particularly in terms of computational efficiency and navigational smoothness. Full article

People with upper limb disabilities or high quadriplegia have extremely high requirements for the maneuverability and functionality of power wheelchairs. Normal wheelchairs cannot meet travel tasks, while smart customized wheelchairs are expensive and cannot be popularized. Therefore, a novel extension control instrument for power wheelchairs with low cost, strong scalability, and convenient usage is proposed, which can realize the control of the wheelchair by sensing a change of head posture. The device is divided into a head motion sensing unit (HMSU) and a wheelchair assistance control unit (WACU). The mapping relationship between the head attitude and the subject’s motion intention is established. The inertial measurement module in the HMSU collects the head attitude data and uses the Kalman filtering method to obtain the accurate Euler angle. The WACU is fixed on the original controller of the wheelchair. The joystick is inserted into the extended control mechanism and controlled, instead of the hand, through a 2-degree-of-freedom servo system combined with the pinion and rack push rod structure, thus controlling the movement of the wheelchair. In proceeding, the system can also detect the distance of objects in the environment in real time through the three-direction (front, left, right) ultrasonic ranging sensors installed on the WACU, with a certain obstacle avoidance function. The prototype experiments prove that the extension control instrument developed in this paper based on the Kalman filter can quickly and accurately identify head motion and accurately control the movement of the wheelchair. It is easy to operate and has strong universality, which presents a new low-cost solution for the travel of patients with disabilities. Full article

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm’s data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes. Full article

Ultrasound is a powerful and versatile technology that has been applied extensively in medicine and scientific research. The development of miniature underwater robots focuses on achieving specific tasks, such as surveys and inspections in confined spaces. However, traditional sonar has limited use in micro underwater robots due to its large size and heavy power demands. Conversely, capacitive micromechanical ultrasonic transducers (CMUTs) offer various advantages, including a wide bandwidth, compact size, and integration feasibility. These attributes make CMUTs a candidate for obstacle avoidance in micro underwater robots. Hence, a novel CMUT structure using Si-Si bonding is proposed. In this design, a membrane isolation layer replaces the cavity bottom isolation layer, simplifying the process and improving bond reliability. A finite element model of the CMUT was constructed in COMSOL and numerically assessed for the CMUT’s operating frequency, collapse voltage, and submerged depth. The CMUT, manufactured using micro-electro-mechanical system (MEMS) technology, undergoes waterproofing with PDMS—A material with similar acoustic impedance to water and corrosion resistance. Underwater tests reveal the CMUT’s resonant frequency in water as approximately 2 MHz, with a −3 dB bandwidth of 108.7%, a transmit/receive beam width of 7.3°, and a standard deviation of measured distance from the true distance of less than 0.05. These outcomes suggest that CMUTs hold promise in obstacle avoidance applications for fish-shaped underwater robots. Full article

The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°. Full article

Within the domain of robotic non-destructive testing (NDT) of complex structures, the existing methods typically utilise an offline robot-path-planning strategy. Commonly, for robotic inspection, this will involve full coverage of the component. An NDT probe oriented normal to the component surface is deployed in a raster scan pattern. Here, digital models are used, with the user decomposing complex structures into manageable scan path segments, while carefully avoiding obstacles and other geometric features. This is a manual process that requires a highly skilled robotic operator, often taking several hours or days to refine. This introduces several challenges to NDT, including the need for an accurate model of the component (which, for NDT inspection, is often not available), the requirement of skilled personnel, and careful consideration of both the NDT inspection method and the geometric structure of the component. This paper addresses the specific challenge of scanning complex surfaces by using an automated approach. An algorithm is presented, which is able to learn an efficient scan path by taking into account the dimensional constraints of the footprint of an ultrasonic phased-array probe (a common inspection method for NDT) and the surface geometry. The proposed solution harnesses a digital model of the component, which is decomposed into a series of connected nodes representing the NDT inspection points within the NDT process—this step utilises graph theory. The connections to other nodes are determined using nearest neighbour with KD-Tree optimisation to improve the efficiency of node traversal. This enables a trade-off between simplicity and efficiency. Next, movement restrictions are introduced to allow the robot to navigate the surface of a component in a three-dimensional space, defining obstacles as prohibited areas, explicitly. Our solution entails a two-stage planning process, as follows: a modified three-dimensional flood fill is combined with Dijkstra’s shortest path algorithm. The process is repeated iteratively until the entire surface is covered. The efficiency of this proposed approach is evaluated through simulations. The technique presented in this paper provides an improved and automated method for NDT robotic inspection, reducing the requirement of skilled robotic path-planning personnel while ensuring full component coverage. Full article

In the construction industry, the process of carrying heavy loads from one location to another by means of a crane is inevitable. This reliance on cranes to carry heavy loads is more obvious when it comes to high-rise building construction. Depending on the conditions and requirements on-site, various types of construction lifting equipment (i.e., cranes) are being used. As off-site construction (OSC) is gaining more traction recently, cranes are becoming more important throughout the construction project as precast concrete (PC) members are major components of OSC calling for lifting work. As a result of the increased use of cranes on construction sites, concerns about construction safety as well as the effectiveness of existing load collision prevention systems are attracting more attention from various parties involved. Besides the inherent risks associated with heavy load lifting, the unpredictable movement of on-site workers around the crane operation area, along with the presence of blind spots that obstruct the crane operator’s field-of-view (FOV), further increase the accident probability during crane operation. As such, the need for a more reliable and improved collision avoidance system that prevents lifted loads from hitting other structures and workers is paramount. This study introduces the application of deep learning-based object detection and distance measurement sensors integrated in a complementary way to achieve the stated need. Specifically, the object detection technique was used with the application of an Internet Protocol (IP) camera to detect the workers within the crane operation radius, whereas ultrasonic sensors were used to measure the distance of surrounding obstacles. Both applications were designed to work concurrently so as to prevent potential collisions during crane lifting operations. The field testing and evaluation of the integrated system showed promising results. Full article

At a disaster site, unforeseen circumstances can severely limit the activities of rescue workers. The best solution is for a cooperative team of robots and rescue workers to complete the rescue work. Therefore, in this paper, we propose a simple and low-cost sensing and control architecture for a search and rescue assistant robot using a thermal infrared sensor array, an ultrasonic sensor, and a three-axis accelerometer. In the proposed architecture, we estimate the location of human survivors using a low-cost thermal IR sensor array and generate and control the trajectory of approaching the searched human survivors. Obstacle avoidance and control are also possible through 3D position estimation of obstacles using 1D ultrasonic sensor integration. In addition, a three-axis accelerometer is used to estimate the tilt angle of the robot according to terrain conditions, and horizontal control of the storage box angle is performed using this feature. A prototype robot was implemented to experimentally validate its performance and can be easily constructed from inexpensive, commonly available parts. The implementation of this system is simple and cost-effective, making it a viable solution for search and rescue operations. The experimental results have demonstrated the effectiveness of the proposed method, showing that it is capable of achieving a level storage box and identifying the location of survivors while moving on a sloped terrain. Full article

One of the challenges in modern neuroscience is creating a brain-on-a-chip. Such a semiartificial device based on neural networks grown in vitro should interact with the environment when embodied in a robot. A crucial point in this endeavor is developing a neural network architecture capable of associative learning. This work proposes a mathematical model of a midscale modular spiking neural network (SNN) to study learning mechanisms within the brain-on-a-chip context. We show that besides spike-timing-dependent plasticity (STDP), synaptic and neuronal competitions are critical factors for successful learning. Moreover, the shortest pathway rule can implement the synaptic competition responsible for processing conditional stimuli coming from the environment. This solution is ready for testing in neuronal cultures. The neuronal competition can be implemented by lateral inhibition actuating over the SNN modulus responsible for unconditional responses. Empirical testing of this approach is challenging and requires the development of a technique for growing cultures with a given ratio of excitatory and inhibitory neurons. We test the modular SNN embedded in a mobile robot and show that it can establish the association between touch (unconditional) and ultrasonic (conditional) sensors. Then, the robot can avoid obstacles without hitting them, relying on ultrasonic sensors only. Full article