Compact autonomous navigation system (CANS)

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.

Initiated and financed by the Canadian Space Agency, Micro-Rover Platform with Tooling Arm (MRPTA) project has targeted the development of a robotic system with remote control and autonomous navigation capabilities for testing a large variety of analogous planetary missions. Although developed for planetary exploration, the navigation system is highly suitable to such areas of application as construction and in mining. Autonomous navigation system is capable of moving the platform to predefined position(s)/orientation(s) while continuously mapping the terrain, assessing its traversability and choosing most appropriate path to follow. Designed according to the JAUS (Joint Architecture for Unmanned Systems) framework, the system consists of several interconnected components. Following components are of particular importance: Pose Estimator (PE), Terrain Evaluator (TE), Map Manager (MP), and Path Planner (PP). The PE uses a minimal sensor configuration consisting of an azimuth gyroscope, inclinometer and wheel odometry. The PE provides reliable pose estimates in spite of pronounced slippage by employing extensive use of fused data. The TE uses a nodding laser scanner which continuously sweeps the area in front of the platform and constructs a “traversability grid” (TG) of the surrounding area. Multiple TGs are constantly constructed along the platform motion path are combined into a terrain map maintained by the MP. The PP uses this map to compute a motion path Obtained path is executed by the motion controller and remains current until it is contradicted by the most recent map – at which point it is re-planned. This paper addresses integration issues, lessons learned and also outlines the possible applications for construction and mining.

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.

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.

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.

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.

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.

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.

There are high expectations for autonomous personal mobility vehicles (PMVs) to support the mobility of older people. Autonomous navigation systems are being developed to assist mobility in public areas with mixed pedestrian traffic, such as airports and shopping malls. For autonomous navigation of PMVs, achieving both comfort and efficiency, even in crowded environments, is important. In this study, we focused on the characteristic of passenger tolerance, in which a passenger’s discomfort is relatively small concerning an approaching pedestrian. The objective was to propose an efficient autonomous navigation method without increasing passenger discomfort, considering the characteristics of passenger tolerance. First, the passenger tolerance characteristics were clarified through data analysis of a previous study’s dataset and a newly collected dataset. Next, a path-planning method considering the characteristics was proposed, and the proposed method was evaluated by numerical simulations. The evaluation results showed that the proposed method has a potential to achieve efficient autonomous navigation in crowded environments.

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.

Simultaneous localization and mapping (SLAM) is an important system for making fully autonomous robot navigation system. A robot must have a good understanding of the environment for navigation. Mobile robot using fixed laser range finder can only detect obstacle on a particular plane level. This may lead to missing of important obstacles that are both above or below the laser scan level. This will cause the map generated to be inaccurate and collision may happen during autonomous navigation. The Microsoft Kinect sensor can replace a laser range finder in a mobile robot as a low cost alternative while providing 3D data. Using depth image data from Kinect, the obstacles between ground surface and 500mm high are projected to horizontal surface, and converted to laser scan data. This will include the entire obstacles that the robot might collide into. Simulations and experiments have been conducted to test out the method in SLAM process in an indoor environment. Both simulation and experimental results show that the proposed method is capable of getting more detailed map compared to the normal 2D map.

This study continues the series of papers devoted to the approach to solving the problem of autonomous navigation of a spacecraft (SC) in a geostationary orbit at all stages of its lifecycle: final ascent, transfer to the operating longitude, remaining at the operating longitude, removal from the orbit, and burial. In [1], we formulated the concept of autonomous navigation of a spacecraft at the stage of its final ascent to a geostationary orbit. This stage is most complicated from the technical point of view, taking into account the nonlinearity of the applied mathematical models, the influence of uncontrollable factors of various physical nature, and the necessity of the application of promising hardware (electric low-thrust engine, onboard receiver of signals from global navigation satellite systems, intersatellite communication channels, etc.). Taking this into account, the only constructive method for proving the viability and efficiency (taking into account the requirements formulated by the customer who placed the order) of the developed concept of the autonomous navigation at the final ascent stage in the mathematical simulation of this process. The subject of this study is to demonstrate the capabilities of the developed concept of autonomous navigation from the point of view of achieving the required level of reliability and accuracy in solving the navigation problem at the final ascent stage. This demonstration is based on the analysis of the results of imitating the simulation of the process of a spacecraft's final ascent to a geostationary orbit obtained using the specially developed software complex.

This paper presents the usage of LQR-based intelligent path tracking methods to control the autonomous navigation system of an Unmanned Surface Robot (USR) in sea-clutter environmental fields. Since considerable interest in the Autonomous Navigation System (ANS) of USR in sea warfare has increased, the stability of the Autonomous Navigation System (ANS) control becomes of the utmost importance. When the USR moves in sea environments, the control stability of autonomous navigation systems becomes a major concern. The Path Tracking Controller (PTC) of USRs is one of the high potential subsystems that can be further improved to achieve more accurate, robust, and comfortable tracking performance. The stability of the PTC should be taken into consideration while performing the path tracking task in autonomous navigation. To solve the automatic waypoint tracking stability of independently-developed PTCs, a waypoint tracking controller is designed based on the Linear Quadratic Regulator (LQR) theory with the kinematic model of USRs. Then, a simulation analysis of the designed waypoint tracking controller is performed, and the results are compared at two different speeds. Additionally, complex waypoint tracking tests under realistic sea conditions are performed based on the designed waypoint tracking controller. Results show good tracking performance, which indicates that the designed waypoint tracking controller can adapt to high-speed USRs.

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.

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