Efficient Autonomous Navigation Research Articles

Design of a Mobile Industrial Robot Platform based on Fixed Cameras

This study presents a design for an autonomous mobile robot system that utilizes environmental cameras for navigation and obstacle avoidance. Operating within a known environment, the robot employs these cameras to facilitate path planning and to circumvent obstacles. The hardware setup includes fixed cameras strategically placed in the environment to pinpoint the robot’s location and to identify obstacles amidst varying conditions. A computer system is essential for processing the data captured by the cameras, and a wireless transmission system communicates operational instructions to the robot.For navigation, the study explores various algorithms for path planning, selecting the most efficient one through comparative analysis. Once the path is established, the robot’s instructions are computed under the designed control rate, employing the vehicle’s PD control method. This method is informed by the external camera’s simulation of the vehicle, ensuring accurate and responsive navigation.The project’s goal is to create a mobile robot platform that leverages an external camera as its primary sensor. This design promises efficient autonomous navigation and the capability to avoid static obstacles. The robot’s design is conceptualized using SolidWorks and brought to life through implementation in the Python and MATLAB programming languages, demonstrating the system’s practicality and adaptability in real-world scenarios. Additionally, the system’s design considers ease of integration and scalability to accommodate future technological advancements and new application requirements.

Lane Line Detection

Abstract: Lane line detection is a crucial component in the development of advanced driver assistance systems (ADAS) and autonomous vehicles. This research proposes a comprehensive approach to enhance the accuracy and robustness of lane line detection algorithms, addressing challenges such as varying road conditions, lighting conditions, and diverse road markings.The proposed methodology leverages computer vision techniques, including edge detection, color space transformations, and image filtering, to preprocess input images from onboard cameras. A novel algorithm for lane line extraction is introduced, combining feature extraction and geometric analysis to accurately identify lane markings. The algorithm adapts to different road scenarios by dynamically adjusting parameters based on environmental conditions. The proposed lane line detection system demonstrates promising results in terms of accuracy, adaptability, and real-time performance, showcasing its potential for practical implementation in autonomous vehicles and ADAS. The research contributes to the ongoing efforts in creating robust and reliable perception systems for safe and efficient autonomous navigation on roads.

Reinforcement imitation learning for reliable and efficient autonomous navigation in complex environments

Reinforcement imitation learning for reliable and efficient autonomous navigation in complex environments

Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation

Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation

Analysis of synchronous localization systems for UAVs urban applications

Unmanned-Aerial-Vehicles (UAVs) represent an active research topic over multiple fields for performing inspection, delivery and surveillance applications among other operations. However, achieving the utmost efficiency requires drones to perform these tasks without the need of human intervention, which demands a robust and accurate localization system for achieving a safe and efficient autonomous navigation. Nevertheless, currently used satellite-based localization systems like GPS are insufficient for high-precision applications, especially in harsh scenarios like indoor and deep urban environments. In these contexts, Local Positioning Systems (LPS) have been widely proposed for satisfying the localization requirements of these vehicles. However, the performance of LPS is highly dependent on the actual localization architecture and the spatial disposition of the deployed sensor distribution. Therefore, before the deployment of an extensive localization network, an analysis regarding localization architecture and sensor distribution should be taken into consideration for the task at hand. Nonetheless, no actual study is proposed either for comparing localization architectures or for attaining a solution for the Node Location Problem (NLP), a problem of NP-Hard complexity. Therefore, in this paper, we propose a comparison among synchronous LPS for determining the most suited system for localizing UAVs over urban scenarios. We employ the Cràmer–Rao-Bound (CRB) for evaluating the performance of each localization system, based on the provided error characterization of each synchronous architecture. Furthermore, in order to attain the optimal sensor distribution for each architecture, a Black-Widow-Optimization (BWO) algorithm is devised for the NLP and the application at hand. The results obtained denote the effectiveness of the devised technique and recommend the implementation of Time Difference Of Arrival (TDOA) over Time of Arrival (TOA) systems, attaining up to 47% less localization uncertainty due to the unnecessary synchronization of the target clock with the architecture sensors in the TDOA architecture.

Heterogeneous computing for low-cost robotic platforms

This study presents a novel approach to the design of cost-effective autonomous rovers using a heterogeneous computing architecture. The research focuses on the development and evaluation of an autonomous rover system, priced under $2 700 (USD), that can operate independently in various environments. The system’s performance was assessed in real-world conditions, demonstrating an average speed of 0.5 m/s, an energy consumption of 0.2 kWh, and a decision-making error rate below 5%. The results suggest that the use of heterogeneous computing architectures can lead to the development of affordable and efficient autonomous navigation systems. The paper concludes by discussing potential future research directions and the broader implications of these findings for the field of autonomous systems.

Efficient autonomous navigation for terrestrial insect-machine hybrid systems

While bio-inspired and biomimetic systems draw inspiration from living materials, biohybrid systems incorporate them with synthetic devices, allowing the exploitation of both organic and artificial advantages inside a single entity. In the challenging development of centimeter-scaled mobile robots serving unstructured territory navigations, biohybrid systems appear as a potential solution in the forms of terrestrial insect-machine hybrid systems, which are the fusion of living ambulatory insects and miniature electronic devices. Although their maneuver can be deliberately controlled via artificial electrical stimulation, these hybrid systems still inherit the insects’ outstanding locomotory skills, orchestrated by a sophisticated central nervous system and various sensory organs, favoring their maneuvers in complex terrains. However, efficient autonomous navigation of these hybrid systems is challenging. The struggle to optimize the stimulation parameters for individual insects limits the reliability and accuracy of navigation control. This study overcomes this problem by implementing a feedback control system with an insight into tunable navigation control for an insect-machine hybrid system based on a living darkling beetle. Via a thrust controller for acceleration and a proportional controller for turning, the system regulates the stimulation parameters based on the instantaneous status of the hybrid robot. While the system can provide an overall success rate of ∼ 71% for path-following navigations, fine tuning its control parameters could further improve the outcome’s reliability and precision to up to ∼ 94% success rate and ∼ 1/2 body length accuracy, respectively. Such tunable performance of the feedback control system provides flexibility to navigation applications of insect-machine hybrid systems.

Autonavi3at Software Interface to Autonomously Navigate on Urban Roads Using Omnidirectional Vision and a Mobile Robot


 
 
 
 
 
 
 The design of efficient autonomous navigation systems for mobile robots or autonomous vehicles is fundamental to perform the programmed tasks. Basically, two kind of sensors are used in urban road following: LIDAR and cameras. LIDAR sensors are highly accurate but expensive and extra work is needed for human understanding of the point cloud scenes; however, visual content is understood better by human beings, which should be used to develop human-robot interfaces. In this work, a computer vision-based urban road following software tool called AutoNavi3AT for mobile robots and autonomous vehicles is presented. The urban road following scheme proposed in AutoNavi3AT uses vanishing point estimation and tracking on panoramic images to control the mobile robot heading on the urban road. To do that, Gabor filters, region growing, and particle filters were used. In addition, laser range data are also employed for local obstacle avoidance. Quantitative results were achieved using two kind of tests, one uses datasets acquired at the Universidad del Valle campus, and field tests using a Pioneer 3AT mobile robot. As a result, important improvements in the vanishing point estimation of 68.26 % and 61.46 % in average were achieved, which is useful for mobile robots and autonomous vehicles when they are moving on urban roads.
 
 
 
 
 
 

A safe and efficient LIDAR-based navigation system for 4WS4WD mobile manipulators in manufacturing plants

The safe and efficient autonomous navigation of mobile manipulators is still challenging in harsh manufacturing environments with dynamic obstacles and narrow spaces. This paper addresses that challenge by proposing an industrial-grade LIDAR-based navigation system for four-wheel-steering and four-wheel-driving mobile manipulators and focuses on enhancing the system’s safety and efficiency in manufacturing plants. On one hand, using an efficient soft-evidence unscented distance filter, a robust localization method is presented to eliminate dynamic interference. The variation results of scan matching are ingeniously applied to formulate a practical localization-failure-detection strategy, therefore improving localization safety under confusing working conditions. On the other hand, an efficient path-graph-based trajectory-planning method is proposed, which can instantly generate a trajectory in a safe and effective search space. Considering the regional connectivity and movement characteristics of the 4WS4WD mobile manipulator, a novel path graph is designed to construct the search space. Even in a large-scale scenario, a robot can obtain the desired trajectory within 5 ms. Ultimately, some core modules, such as a safety module and a fault-tolerant control module are integrated to form a comprehensive navigation system. The simulated and real experimental results prove the excellent performance of our navigation system, which has been successfully applied in many real-world scenarios.

Efficient autonomous navigation for planetary rovers with limited resources

AbstractRovers operating on Mars require more and more autonomous features to fulfill their challenging mission requirements. However, the inherent constraints of space systems render the implementation of complex algorithms an expensive and difficult task. In this paper, we propose an architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the ExoMars path following navigation approach to enhance the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform significantly longer traverses planned by operators on the ground. The efficient navigation approach has been implemented and tested during field test campaigns on a planetary analogue terrain. The experiments evaluated the proposed architecture by autonomously completing several traverses of variable lengths while avoiding hazards. The approach relies only on the optical Localization Cameras stereo bench, a sensor that is found in all current rovers, and potentially allows for computationally inexpensive long‐range autonomous navigation in terrains of medium difficulty.

Design and implementation of a 3D scanning platform for mobile robotic applications

In an efficient autonomous navigation and exploration, the robots should sense the environment as exactly as possible in real-time and act correctly on the basis of the acquired 3D data. In this paper, we present the design and implementation of a scanning platform, which can be used for both outdoor and indoor mobile robot navigation and mapping. A 3D scanning platform based on a 2D laser rangefinder was designed in a compact way with a maximum field of view for fast and accurate mapping. The designed mechanism provides 360° degree horizontal by 240° degree vertical field of view. The maximum resolution is 0.36° degrees in elevation and variable in azimuth. The proposed low cost compact design is tested by scanning a physical environment with known dimensions to show that it can be used as a precise and reliable high quality 3D sensor for autonomous mobile robots.

Design and Implementation of a 3D Range Scanner for Mobile Robots

In an efficient autonomous navigation and exploration, the robots should sense the environment as exactly as possible in real-time and act correctly on the basis of the acquired 3D data. Laser scanners have been used for the last 30 years for mobile robot navigation. However, they often did not enough speed, accuracy and field of view. In this paper we present the design and implementation of a scanning platform, which can be used for both outdoor and indoor mobile robot navigation and mapping. A 3D scanning platform based on a 2D laser rangefinder was designed in compact way for fast and accurate mapping with maximum field of view. The range finder is rotated around the vertical axis to extract the 3D indoor information. However, the scanner is designed to be placed in any direction on a mobile robot. The designed mechanism provides 360º degree horizontal by 240º degree vertical field of view. The maximum resolution is 0.36º degrees in elevation and variable in azimuth (0.1 degrees if scanning platform is set to complete a 360º degree rotation in 3.6 seconds). The proposed low cost compact design is tested by scanning a physical environment with known dimensions to show that it can be used as a precise and reliable high quality 3D sensor for autonomous mobile robots.

Learning Long-range Terrain Perception for Autonomous Mobile Robots

Long-range terrain perception has a high value in performing efficient autonomous navigation and risky intervention tasks for field robots, such as earlier recognition of hazards, better path planning, and higher speeds. However, Stereo-based navigation systems can only perceive near-field terrain due to the nearsightedness of stereo vision. Many near-to-far learning methods, based on regions' appearance features, are proposed to predict the far-field terrain. We proposed a statistical prediction framework to enhance long-range terrain perception for autonomous mobile robots. The main difference between our solution and other existing methods is that our framework not only includes appearance features as its prediction basis, but also incorporates spatial relationships between terrain regions in a principled way. The experiment results show that our framework outperforms other existing approaches in terms of accuracy, robustness and adaptability to dynamic unstructured outdoor environments.