Autonomous Navigation and Mapping System for MRPTA Rover

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

An autonomous and secure navigation system is needed for the spacecraft in future deep space missions. X-ray pulsar offers the possibility of autonomous navigation anywhere in the solar system. This article introduces an analysis of the spacecraft autonomous navigation system accuracy, utilizing signals emitted from the X-ray pulsars. Based on the principle of the X-ray navigation, visibility of X-ray pulsar on L1 sun-observation orbit was simulated. The influence of the geometry distribution and the pulsar angle position error was analyzed. Error sources of the navigation system were listed, including the measurement error of the X-ray detector, transformation error of time-of-arrival (TOA), and the navigation calculation error, and the simulations of their parts were shown.

In the field of autonomous robotics, enhancing the navigation system of robots is a crucial aspect that directly impacts their performance. This study presents a novel approach to addressing this challenge with an Artificial Neural Network (ANN) model. The research focuses on improving the navigation capabilities of a differential drive robot using the line-following method for route tracking and the dead reckoning technique for localization. It investigates a differential drive robot model controlled with a PID controller and derives the transfer function of the PID model. Through simulations, it becomes apparent that the PID model exhibits a continuous overshoot in its response, which negatively affects the behaviour of the robot's wheels. Ordinarily, continuous manual tuning will be required to correctly tune the PID controller to a value where the overshoots will be negligible, and this could be onerous. To overcome this limitation, an ANN controller is proposed, leveraging the learning capabilities of the neural network. Data from the PID controller transfer function is utilized to train the ANN model, enabling it to understand patterns and relationships. The ANN controller is then substituted in place of the PID controller in the simulation. The results showcase a remarkable 13.1% improvement in the robot's wheel response, highlighting the transformative potential of this approach for revolutionizing autonomous robot navigation in industrial applications. By using the transfer function of the PID model to train an ANN model, this study offers a powerful framework for enhancing the navigation performance of a differential drive autonomous robot and shows performance improvements in control, flexibility, and adaptation to changing conditions. These discoveries have significant ramifications for the industry and will pave the way for intelligent and effective autonomous robot navigation systems. The research provides a comprehensive understanding of the challenges associated with the differential drive robot model controlled with a PID controller and offers a robust approach to how this can be alleviated. The significance of this study lies in its ability to address the continuous overshoot issue observed in the PID controller's response by training an ANN controller with data from the PID controller. The proposed approach minimizes overshoot and improves the robot's wheel response, ultimately enhancing its navigation capabilities. Overall, this study demonstrates the potential of an ANN model to revolutionize autonomous robot navigation in industrial applications. The notable improvement achieved in the robot's wheel response validates the effectiveness of this approach. Future research can further optimize this integrated approach in real-world scenarios, leading to intelligent and efficient autonomous robot navigation systems across diverse industrial settings.

This paper presents new algorithmic methods for accuracy improvement of autonomous inertial navigation systems of aircrafts. Firstly, an inertial navigation system platform and its nonlinear error model are considered, and the correction schemes are presented for autonomous inertial navigation systems using internal information. Next, a correction algorithm is proposed based on signals from precession angle sensors. A vector of reduced measurements for the estimation algorithm is formulated using the information about the angles of precession. Finally, the accuracy of the developed correction algorithms for autonomous inertial navigation systems of aircrafts is studied. Numerical solutions for the correction algorithm are presented by the adaptive Kalman filter for the measurement data from the sensors. Real data of navigation system Ts-060K are obtained in laboratory experiments, which validates the proposed algorithms.

An autonomous navigation and guidance system for MUSES-C asteroid landing

Recently, stereovision has appeared in robotics as a source of information for real-time mapping and path planning. In this paper, an intelligent motion system for mobile robots is designed and implemented using stereovision. The proposed system uses stereovision as a primary method for sensing the environment, and the system is able to navigate intelligently in an indoor environment with varying degrees of obstacle complexity. It creates noiseless and high-confidence 3D point clouds and uses these point clouds as an input for the mapping and path-planning modules. The proposed system was built by developing, enhancing, and integrating various techniques, modules and algorithms. The Stereovision-based Path-planning module is the integration of three main enhanced techniques: (1) the multi-baseline multi-view stereovision filter (MMSVF), (2) accurate floor detection and segmentation (AFDS), and (3) the intelligent gazing module (IGM). This Stereovision-based Path planning (MMSVF, IGM, and AFDS) was integrated with the Fuzzy Logic Motion Controller (FLMC). All techniques, modules and algorithms are implemented using a multi-threaded and client---server-based architecture. To prove the viability and robustness of our proposed system, we have integrated all components of the system into a fully functional mobile robot navigation system. We compared the performance of the main modules with that of similar modules in the literatures, and showed that our modules had better performance. Testing the whole system is more important than just testing each module individually. To the best of our knowledge, the literatures lack such testing. Hence, in this paper we present the performance of our complete integrated system in different environments using different parameters and different architectures.

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and positioning. In order to reduce the computational complexity and exclude the risk caused by Global Positioning System (GPS) outage, an autonomous navigation system based on distributed filtering is designed and realized. When GPS is available by monitoring the GPS integrity, sensor information from Strapdown Inertial Navigation System (SINS) and GPS is fused using a 7-state Conventional Kalman Filter (CKF) to estimate the full UAV state (position, velocity and attitude); when GPS is unavailable, sensor information from gyroscopes, accelerometers and magnetometer is fused using a 4-state Extended Kalman Filter (EKF) to estimate the attitude and heading of the UAV, and sensor information from SINS and vision positioning system is fused using a 7-state Incoordinate Interval Kalman Filter (IIKF) to estimate the position and velocity of the UAV. In addition, the second-order vertical channel damping loop is adopted to fuse measurements from a barometer with those of SINS, which suppresses the divergence of the vertical channel error and makes the altitude information calculated by SINS trustable. Both ground and flight experiments of the autonomous navigation system have been carried out. The test results show that the system can provide stabilized attitude information in long durations, and can realize the automatic flight control of UAV.

Path planning is a fundamental component in the development of robotics, autonomous navigation, and intelligent systems, playing a pivotal role in the functioning of smart cities. Within the realm of smart cities, where infrastructure is becoming increasingly interconnected, efficient path planning algorithms are essential for optimizing traffic flow, reducing congestion, and ensuring the seamless movement of people and goods. Among various path planning algorithms, the A* algorithm remains one of the most widely used approaches due to its completeness and optimality under consistent heuristics. However, traditional A* suffers from several limitations when applied to complex 3D environments, including uniform neighbor expansion, fixed-resolution grids, and overly simplistic heuristic functions. These drawbacks often lead to excessive computation, suboptimal paths, and failure in cluttered or large-scale scenarios. To address these challenges, we propose a direction aware and self-adaptive A* algorithm named DASA*, an enhanced A* based path planning framework. Firstly, we propose an adaptive hierarchical direction-aware neighborhood selection mechanism. Driven by a lightweight stagnation detector based on open-set growth rate, this mechanism enables seamless automatic switching of neighborhood expansion from narrow conical angles to full 26-neighbourhood coverage. This fundamentally resolves the search incompleteness inherent in traditional fixed-conical-angle directional A* algorithms at the mechanism level, significantly enhancing planning success rates and efficiency. Secondly, a resolution-adaptive search strategy dynamically adjusts planning granularity based on local obstacle density. It advances rapidly with coarse granularity in sparse areas while switching to fine granularity for precise obstacle avoidance in dense zones, achieving an optimal balance between efficiency and safety in heterogeneous environments. Moreover, the designed learnable heuristic plugin interface enables seamless integration of PPO models trained on spatio-semantic features. This provides more informative, purposeful, and forward-looking heuristic guidance, further accelerating convergence towards optimal paths. Finally, we have devised a delayed path adjustment strategy that simplifies trajectories by omitting redundant waypoints through a deferred update mechanism. Subsequently, cubic B-spline interpolation is applied to smooth the path, ensuring curvature continuity and effectively preventing abrupt turns and unnatural maneuvers. This enhances both the fluidity and kinematic plausibility of the trajectory. Subsequently, Extensive experiments in simulated 3D environments demonstrate that DASA* outperforms conventional A* and Other mainstream algorithms in terms of planning time, length of optimal path, and success rate. The proposed framework provides a practical and extensible foundation for real-world applications such as UAV navigation, mobile robotics, and autonomous inspection in complex terrains.

With the rapid development of UAV industry, autonomous UAV obstacle avoidance navigation has become a core problem in the field of UAV control. Based on recurrent neural networks, this paper proposes an LSTM-enhanced Layered-RSAC algorithm to construct a collaborative task execution and autonomous navigation system for UAV swarms. By constructing the autonomous navigation system of UAV, its accuracy is tested and the UAV operation situation index is examined. Through model training, the standard deviation of the a priori strategy with the best success rate of autonomous navigation is explored. The a priori strategy σ = 0.45 is taken as the initial value to verify the performance improvement of the Layered-RSAC algorithm. The results show that the Layered-RSAC algorithm reaches 90% navigation success rate at 50 training steps for the first time and stabilizes at 90% to 100% success rate after 100 training steps, which is significantly ahead of Prior-Policy, DDPG and SAC algorithms.

Autonomous and automatic home mess-cleanup robot is newly developed in this paper. Recently, the cleaning robot was commercialized to solve home cleaning labor problem but it was not so successful because it still had a problem of mess-cleanup, which was the clean-up of big trash and the arrangement of newspapers, clothes, etc. So, we develop a home mess-cleanup robot which can arrange object and throw away the big trash to perfectly overcome these problems. It needs functions of agile navigation and novel manipulation system for mess-cleanup. This Paper focuses on the navigation system. The autonomous navigation system has to be controlled for the full scanning of living room and for the precise tracking of the desired path. We use omni wheels which can move omni-directionally to develop agile navigation system. The manipulator has the functions to distinguish object between big trash and mess objects to arrange and carry them to destination in according to the object. We use the RFID system to sense object and know absolute location on the floor which has RFID tag. Finally, the good performance of the developed is confirmed through the results of the mess-cleanup experiments.

Autonomous navigation of Satellite and constellation has series of benefits, such as to reduce operation cost and ground station workload, to avoid the event of crises of war and natural disaster, to increase spacecraft autonomy, and so on. Autonomous navigation satellite is independent of ground station support. Many systems are developed for autonomous navigation of satellite in the past 20 years. Along them American MANS (Microcosm Autonomous Navigation System) [1] of Microcosm Inc. and ERADS [2]，[3] (Earth Reference Attitude Determination System) of Honeywell Inc. are well known. The systems anticipate a series of good features of autonomous navigation and aim low cost, integrated structure, low power consumption and compact layout. The ERADS is an integrated small 3-axis attitude sensor system with low cost and small volume. It has the Earth center measurement accuracy higher than the common IR sensor because the detected ultraviolet radiation zone of the atmosphere has a brightness gradient larger than that of the IR zone. But the ERADS is still a complex system because it has to eliminate many problems such as making of the sapphire sphere lens, birefringence effect of sapphire, high precision image transfer optical fiber flattener, ultraviolet intensifier noise, and so on. The marginal sphere FOV of the sphere lens of the ERADS is used to star imaging that may be bring some disadvantages., i.e. , the image energy and attitude measurements accuracy may be reduced due to the tilt image acceptance end of the fiber flattener in the FOV. Besides Japan, Germany and Russia developed visible earth sensor for GEO [4]、[5]. Do we have a way to develop a cheaper/easier and more accurate autonomous navigation system that can be used to all LEO spacecraft, especially, to LEO small and micro satellites? To return this problem we provide a new type of the system—CANS (Compact Autonomous Navigation System) [6].

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments.

Recently, many research programs have investigated the concept of intelligent vehicles and their integration in the city of tomorrow. The aim is to develop an intelligent transportation system based on a fleet of fully automated cars designed for short trips at low speed in urban areas. This system will offer advantages of high flexibility, efficiency, safety, and thus, will improve the quality of life in our cities including but not restricted to protection of the environment, better management of parking areas, and others. One of the key functions that such transportation system must achieve concerns the notion of autonomous navigation of vehicles. To reach this goal, we are working on vehicle environment perception using passive and active sensor technologies. In this chapter, we address an artificial-vision based environment perception system for autonomous vehicle navigation. We are particularly interested in obstacle detection using stereo vision, image road line tracking for vehicle road, line following and landmarks recognition for local positioning. The developed techniques are implemented and have been tested using a fully automated vehicle platform for autonomous navigation in urban areas.

Over the last few years, the increasing number of autonomous systems has required stronger, smarter, and more context-aware frameworks that can work effectively in uncertain and dynamic settings. This research proposes a multisensor fusion architecture based on deep learning for autonomous control and navigation systems. The method combines complementary sensor inputs like LiDAR, radar, and vision data using a layered pipeline, including data acquisition, preprocessing, fusion, and neural decision-making. The combination strategy improves environmental perception, and real-time object detection, path planning, and decision output are accomplished using convolutional and recurrent neural networks. Experiment results indicate significant system accuracy, response time, and adaptability improvements compared with the conventional modular control structures. The proposed approach also demonstrates enhanced capabilities in more complex environments that require adequate obstacle perception and real-time path search. Despite the computational difficulties due to readout characteristics and issues of interpretability, the results show encouraging evidence towards the scalable and safe deployment of autonomous systems. Future development and the development of lighter AI models, explainable decision mechanisms, and approaches for domain adaptation are described in the conclusion of this work.

Legged robots have the potential to navigate in more challenging terrain than wheeled robots do. Unfortunately, their control is more difficult because they have to deal with the traditional mapping and path planning problems, as well as foothold computation, leg trajectories and posture control in order to achieve successful navigation. Many parameters need to be adjusted in real time to keep the robot stable and safe while it is moving. In this paper, we will present a new framework for a quadruped robot, which performs goal-oriented navigation on unknown rough terrain by using inertial measurement data and stereo vision. This framework includes perception and control, and allows the robot to navigate in a straight line forward to a visual goal in a difficult environment. The developed rough terrain locomotion system does not need any mapping or path planning: the stereo camera is used to visually guide the robot and evaluate the terrain roughness and an inertial measurement unit (IMU) is used for posture control. This new framework is an important step forward to achieve fully autonomous navigation because in the case of problems in the SLAM mapping, a reactive locomotion controller is always active. This ensures stable locomotion in rough terrain, by combining direct visual feedback and inertial measurements. By implementing this controller, an autonomous navigation system has been developed, which is goal-oriented and overcomes disturbances from the ground, the robot weight, or external forces. Indoor and outdoor experiments with our quadruped robot show the effectiveness and the robustness of this framework.