Navigation Experiments Research Articles

Empirical study of future image prediction for image-based mobile robot navigation

Recent image-based robotic systems use predicted future state images to control robots. Therefore, the prediction accuracy of the future state image affects the performance of the robot. To predict images, most previous studies assume that the camera captures the entire scene and that the environment is static. However, in real robot applications, these assumptions do not always hold. For example, if a camera is attached to a mobile robot, its view changes from time to time.In this study, we analyzed the relationship between the performance of the image prediction model and the robot’s behavior, controlled by an image-based navigation algorithm. Through mobile robot navigation experiments using front-faced and omni-directional cameras, we discussed the capabilities of the image prediction models and demonstrated their performance when applied to the image-based navigation algorithm.Moreover, to adapt to the dynamic changes in the environment, we studied the effectiveness of directing the camera to the ceiling. We showed that robust navigation can be achieved without using images from cameras directed toward the front or the floor, because these views can be disturbed by moving objects in a dynamic environment.

Realization of indoor and outdoor localization and navigation for quadruped robots

The quadruped robot has the advantages of flexible movement and large load because of the bionic leg structure. In order to improve their intelligence and autonomy in the complex environment, we build a highly integrated sensing system with lidar and an inertial measurement unit (IMU) on a quadruped robot. After the robot completes the construction of the map, point clouds are aligned using Normal Distribution Transform(NDT) registration to realize the localization of the robot. In the experimental part, we quantitatively tested the accuracy of outdoor localization and also completed indoor-outdoor navigation experiments for the quadruped robot. The experimental results validate the effectiveness of the perception system.

Water Target Recognition Method and Application for Unmanned Surface Vessels

Water target recognition is a critical challenge for the perception technology of unmanned surface vessels (USVs). In the application of USV, detection accuracy and the inference time both matter, while it is tough to strike a balance and single-frame water target detection behaves unstable in the video detection. To solve these problems, many strategies are applied to increase YOLOv4’s performance, including network pruning, the focal loss function, blank label training, and preprocessing with histogram normalization. The optimized detection method achieves a mean average precision (mAP) of 81.74% and a prediction speed of 26.77 frames per second (FPS), which meets the USV navigation requirements. To build the integrated USV-based system for water target recognition, a water target dataset containing 9936 images is created from offshore USV experiments in which the human-in-the-loop annotation and mosaic data augmentation methods are used. The issues of miss detection and false alarm can be considerably mitigated by cascading the Siamese-RPN tracking network, and the major color of a water target can be retrieved using a local contrast saliency color detection scheme. The system being tested is called “ME120” includes an embedded edge computing platform (Nvidia Jetson AGX Xavier). Finally, online dataset learning demonstrates the improved YOLOv4 achieves an increase of 66.98% in FPS at the cost of a decrease of 0.79% in mAP when compared with the original YOLOv4 and offline navigation experiments validate that our system achieves high recognition capability while maintaining a high degree of robustness.

A Reconstruction Filter for Saturated Accelerometer Signals Due to Insufficient FSR in Foot-Mounted Inertial Navigation System

In pedestrian inertial navigation, one possible placement of Inertial Measurement Units (IMUs) is on a footwear. This placement allows to limit the accumulation of navigation errors due to the bias drift of inertial sensors and is generally a preferable placement of sensors to achieve the highest precision of pedestrian inertial navigation. However, inertial sensors mounted on footwear experience significantly higher accelerations and angular velocities (10s of g and 1000s of deg/sec) during regular pedestrian activities than during more conventional navigation tasks, which could exceed Full Scale Range (FSR) of many commercial-off-the-shelf IMUs, therefore degrading accuracy of pedestrian navigation systems. This paper proposes a reconstruction filter to mitigate localization error in pedestrian navigation due to insufficient FSR of inertial sensors. The proposed reconstruction filter approximates immeasurable accelerometer’s signals with a triangular function and estimates the size of the triangles using a Gaussian Process (GP) regression. To evaluate performance of pedestrian navigation systems enhanced by the proposed reconstruction filter, we conducted two series of indoor pedestrian navigation experiments with a VectorNav VN-200 IMU and an Analog Device ADIS16497-3 IMU, which have accelerometer’s FSR of ±16 g and ±40 g, respectively. In the first series of experiments, forces experienced by the foot-mounted IMUs did not exceed the FSRs of the sensors, while in the second series, the forces surpassed the FSR of the VN-200 IMU and saturated the accelerometer’s readings. The saturated accelerometer’s readings reduced the accuracy of estimated positions using the VN-200 by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1.34}\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${3.37}\times $ </tex-math></inline-formula> along horizontal and vertical directions. When applying our proposed reconstruction filter to the saturated accelerometer’s measurements, the navigation accuracy along horizontal and vertical directions was increased by 5% and 50%, respectively, as compared to using unreconstructed signals.

An Improved Geomagnetic Navigation Method Based on Two-Component Gradient Weighting

During the geomagnetic vector navigation, there are three attitude angles required for coordinate transformation in the process of geomagnetic vector elements’ calculation. However, the yaw angle provided by inertial navigation system (INS) has accumulated error which is inevitable and will seriously reduce navigation accuracy. An improved geomagnetic navigation method based on two-component gradient weighting (TCGW) is proposed in this paper. The horizontal and vertical geomagnetic components can be calculated with roll angle and pitch angle based on the implicit trigonometric theorem in the process of coordinate transformation. And the gradient weights are used as the coefficients to improve the accuracy and universality. The simulations based on four typical types have been performed, and the Monte Carlo simulation results indicated that the proposed method has higher matching accuracy than traditional vector ICCP (VICCP) method. Furthermore, the airborne navigation experiments have been carried out to verify the accuracy and effectiveness of the proposed method.

Development of Tele-Operated Underfloor Mobile Manipulator

In this study, we propose a tele-operated mobile manipulator for conducting underfloor work, including both inspection and repair. We focus on a caulking operation, and develop a mobile manipulator for finding and repairing cracks in a foundation. When designing the mobile manipulator, we consider a typical narrow underfloor environment, and perform detailed calculations. As a user interface, we furnish an image-pointing system for enabling us to operate the manipulator simply by clicking on a hand camera image. Then, we evaluate its performance based on navigation experiments, trajectory tracking experiments, and overall evaluation experiments.

Research on Innovative Self-Calibration Strategy for Error Parameters of Dual-Axis RINS

The rotational inertial navigation system (RINS) could greatly improve navigation accuracy by rotating the inertial measurement unit with gimbals. But it will also excite and amplify the corresponding error parameters, so it is more necessary to calibrate the typical error parameters accurately. Fortunately, the self-calibration strategy could be achieved conveniently in RINS. In this paper, an innovative self-calibration strategy for error parameters of dual-axis is proposed. The calibration strategy is designed that the inner and outer gimbals rotate at different angular speeds at the same time, and the whole calibration process lasts for 12 min. The relationship between the error parameters to be calibrated and the navigation error is derived, and the principle of error separation is analyzed, which proves the rationality of the strategy. The calibration strategy is verified by simulation and experiment, and the 21 error parameters of the system can be well estimated. The 3h static navigation experiment shows that the position error is less than 0.15nmile/h(CEP) after compensating the error parameters calibrated by the self-calibration strategy, and achieved the expected accuracy of the dual-axis RINS. Finally, it is proved that the calibration strategy has the obvious advantages of short calibration time and simple process, which is of great significance to improve the navigation accuracy of dual-axis RINS.

Equivariant filtering framework for inertial-integrated navigation

This paper proposes an Equivariant Filtering (EqF) framework for the inertial-integrated state estimation. As the kinematic system of the inertial-integrated navigation can be naturally modeled on the matrix Lie group SE2(3), the symmetry of the Lie group can be exploited to design an equivariant filter which extends the invariant extended Kalman filtering on the group-affine system and overcomes the inconsitency issue of the traditional extend Kalman filter. We firstly formulate the inertial-integrated dynamics as the group-affine systems. Then, we prove the left equivariant properties of the inertial-integrated dynamics. Finally, we design an equivariant filtering framework on the earth-centered earth-fixed frame and the local geodetic navigation frame. The experiments show the superiority of the proposed filters when confronting large misalignment angles in Global Navigation Satellite Navigation (GNSS)/Inertial Navigation System (INS) loosely integrated navigation experiments.

Agricultural machinery GNSS/IMU-integrated navigation based on fuzzy adaptive finite impulse response Kalman filtering algorithm

This study uses global navigation satellite system (GNSS) positioning equipment and inertial measurement unit integrated with accelerometer and gyroscope to improve the accuracy and stability of the current agricultural machinery automatic navigation technology. Considering the actual motion state of agricultural machinery in operation, a fuzzy adaptive finite impulse response Kalman filter (FA-FIR-KF) algorithm was proposed to integrate position information and attitude information, and some necessary auxiliary optimization algorithms were introduced to make innovative improvements. The introduction of quaternion method can suppress the actual nonlinear problem of the agricultural machinery coordinate caused by the attitude angle. A fuzzy inference system was adopted to improve the adaptive adjustment ability to abnormal noise. A forgetting factor was adopted to reduce the system’s excessive dependence on prior statistical information, so that the system can quickly track the abrupt signal. The algorithm simulation program was written on MATLAB, and the performance and effect of the proposed algorithm were verified through simulation and farm experiments. Simulation results of artificially added noise simulation data show that the localization precision in the Xn, Yn, and Zn directions increases by 38.95%, 38.88%, and 32.99%, respectively. This finding indicates that the FA-FIR-KF algorithm can effectively suppress the Gaussian white noise of the GNSS received signal and improve the positioning accuracy of agricultural machinery. The reliability of this algorithm applied to the automatic navigation system was verified through a tractor straight-line navigation experiment. The tractor conducts an automatic navigation test at a speed of 0.8 m/s. Under the GNSS differential state, the average error and root mean square error (RMSE) are 1.074 and 1.396 cm in filtering case and 1.17 and 1.551 cm in nonfiltering case, respectively. Under the GNSS nondifferential state, the average error and RMSE are 2.097 and 2.72 cm in filtering case and 3.663 and 4.633 cm in nonfiltering case, respectively. Compared with the nonfiltering case, the average error and RMSE reduce by 8.21% and 9.99% in the differential state and 42.75% and 41.32% in the nondifferential state, respectively. Test results show that the proposed algorithm can make the agricultural machinery track the desired path more smoothly, stably, and accurately than in the nonfiltered case, and the tracking accuracy is at the centimeter level.

ZUPT-Aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Extended Kalman Filter (EKF) framework, which utilizes a time-varying measurement covariance matrix that is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Properties of EKF innovation sequences in the algorithm were studied, and two series of indoor pedestrian navigation experiments were conducted to demonstrate the navigation performance of the proposed system. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.

Choose your own route – supporting pedestrian navigation without restricting the user to a predefined route

ABSTRACT State-of-the-art mobile pedestrian navigation systems often employ GPS or other positioning methods for continuous tracking of users, and thus provide them with in-situ turn-by-turn route guidance along a desired route. However, studies have shown that user experience and acquisition of spatial knowledge decrease due to the “blind” following of such turn-by-turn navigation systems. This paper proposes a novel interface concept for mobile pedestrian navigation systems that provide navigation guidance without restricting the users to a predefined route. Specifically, the proposed novel user interface was based on the concept of the Potential Route Area (PRA), which defines a dynamic area consisting of all potential routes not longer than a certain detour the user is willing to accept. Within that area, the user can freely choose his/her own route and alter it anytime, and can still arrive at the destination within the desired detour tolerance. As a proof of concept, the proposed PRA-based system was then tested against a conventional turn-by-turn navigation system, represented by Google Maps, in a real-world navigation experiment, which revealed that the acquisition of spatial knowledge and user experience were substantially improved when using the PRA-based system. This can be explained by the fact that the PRA-based system provides its users with more freedom in choosing their own route.

Deep-Space Optical Navigation for M-ARGO Mission

The Miniaturised Asteroid Remote Geophysical Observer (M-ARGO) mission is designed to be ESA’s first stand-alone CubeSat to independently travel in deep space with its own electric propulsion and direct-to-Earth communication systems in order to rendezvous with a near-Earth asteroid. Deep-space Cubesats are appealing owing to the scaled mission costs. However, the operational costs are comparable to those of traditional missions if ground-based orbit determination is employed. Thus, autonomous navigation methods are required to favour an overall scaling of the mission cost for deep-space CubeSats. M-ARGO is assumed to perform an autonomous navigation experiment during the deep-space cruise phase. This paper elaborates on the deep-space navigation experiment exploiting the line-of-sight directions to visible beacons in the Solar System. The aim is to assess the experiment feasibility and to quantify the performances of the method. Results indicate feasibility of the autonomous navigation for M-ARGO with a 3σ accuracy in the order of 1000 km for the position components and 1 m/s for the velocity components in good observation conditions, utilising miniaturized optical sensors.

Optimizing Matching Area for Underwater Gravity-Aided Inertial Navigation Based on the Convolution Slop Parameter-Support Vector Machine Combined Method

This paper focuses on the selection of matching areas in the gravity-aided inertial navigation system. Firstly, the Sobel operator was used in convolution of the gravity anomaly map to obtain the feature map. The convolution slope parameters were constructed by combining the feature map and the gravity anomaly map. The characteristic parameters, such as the difference between convolution rows and columns, convolution variance of the feature map, the pooling difference, and range of the gravity anomaly map, were combined. Based on the support vector machine algorithm, the convolution slope parameter-support vector machine combined method is proposed. Second, we selected the appropriate training sample set and set parameters to verify. The results show that compared with the pre-calibration results, the classification accuracy of the test set is more than 92%, which proves that the convolution slope parameter-support vector machine combined method can effectively distinguish between the suitable and the unsuitable area. Thirdly, we applied this method to another region. The navigation experiment was performed in the split-matching area. The average positioning error was better than 100 m, and the correct rate was more than 90%. The results show that sailing in the selected area can accurately match the trajectory and reduce the positioning error.

OpenMaze: An open-source toolbox for creating virtual navigation experiments.

Incorporating 3D virtual environments into psychological experiments offers an innovative solution for balancing experimental control and ecological validity. Their flexible application to virtual navigation experiments, however, has been limited because accessible development tools best support only a subset of desirable task design features. We created OpenMaze, an open-source toolbox for the Unity game engine, to overcome this barrier. OpenMaze offers researchers the ability to conduct a wide range of first-person spatial navigation experiment paradigms in fully customized 3D environments. Crucially, because all experiments are defined using human-readable configuration files, our toolbox allows even those with no prior coding experience to build bespoke tasks. OpenMaze is also compatible with a variety of input devices and operating systems, broadening its possible applications. To demonstrate its advantages and limitations, we review and contrast other available software options before providing an overview of our design objectives and walking the reader through the process of building an experiment in OpenMaze.

An ARCore-Based Augmented Reality Campus Navigation System

Currently, the route planning functions in 2D/3D campus navigation systems in the market are unable to process indoor and outdoor localization information simultaneously, and the UI experiences are not optimal because they are limited by the service platforms. An ARCore-based augmented reality campus navigation system is designed in this paper in order to solve the relevant problems. Firstly, the proposed campus navigation system uses ARCore to enhance reality by presenting 3D information in real scenes. Secondly, a visual inertial ranging algorithm is proposed for real-time locating and map generating in mobile devices. Finally, rich Unity3D scripts are designed in order to enhance users’ autonomy and enjoyment during navigation experience. In this paper, indoor navigation and outdoor navigation experiments are carried out at the Lingang campus of Shanghai University of Electric Power. Compared with the AR outdoor navigation system of Gaode, the proposed AR system can achieve increased precise outdoor localization by deploying the visual inertia odometer on the mobile phone and realizes the augmented reality function of 3D information and real scene, thus enriching the user’s interactive experience. Furthermore, four groups of students have been selected for system testing and evaluation. Compared with traditional systems, such as Gaode map or Internet media, experimental results show that our system could facilitate the effectiveness and usability of learning on campus.

Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm

This paper presents an autonomous navigation system for cost-effective magnetic-assisted colonoscopy, employing force-based sensors, an actuator, a proportional–integrator controller and a real-time heuristic searching method. The force sensing system uses load cells installed between the robotic arm and external permanent magnets to derive attractive force data as the basis for real-time surgical safety monitoring and tracking information to navigate the disposable magnetic colonoscope. The average tracking accuracy on magnetic field navigator (MFN) platform in x-axis and y-axis are 1.14 ± 0.59 mm and 1.61 ± 0.45 mm, respectively, presented in mean error ± standard deviation. The average detectable radius of the tracking system is 15 cm. Three simulations of path planning algorithms are presented and the learning real-time A* (LRTA*) algorithm with our proposed directional heuristic evaluation design has the best performance. It takes 75 steps to complete the traveling in unknown synthetic colon map. By integrating the force-based sensing technology and LRTA* path planning algorithm, the average time required to complete autonomous navigation of a highly realistic colonoscopy training model on the MFN platform is 15 min 38 s and the intubation rate is 83.33%. All autonomous navigation experiments are completed without intervention by the operator.

Hamiltonian coordination primitives for decentralized multiagent navigation

We focus on decentralized navigation among multiple non-communicating agents in continuous domains without explicit traffic rules, such as sidewalks, hallways, or squares. Following collision-free motion in such domains requires effective mechanisms of multiagent behavior prediction. Although this prediction problem can be shown to be NP-hard, humans are often capable of solving it efficiently by leveraging sophisticated mechanisms of implicit coordination. Inspired by the human paradigm, we propose a novel topological formalism that explicitly models multiagent coordination. Our formalism features both geometric and algebraic descriptions enabling the use of standard gradient-based optimization techniques for trajectory generation but also symbolic inference over coordination strategies. In this article, we contribute (a) HCP (Hamiltonian Coordination Primitives), a novel multiagent trajectory-generation pipeline that accommodates spatiotemporal constraints formulated as symbolic topological specifications corresponding to a desired coordination strategy; (b) HCPnav, an online planning framework for decentralized collision avoidance that generates motion by following multiagent trajectory primitives corresponding to high-likelihood, low-cost coordination strategies. Through a series of challenging trajectory-generation experiments, we show that HCP outperforms a trajectory-optimization baseline in generating trajectories of desired topological specifications in terms of success rate and computational efficiency. Finally, through a variety of navigation experiments, we illustrate the efficacy of HCPnav in handling challenging multiagent navigation scenarios under homogeneous or heterogeneous agents across a series of environments of different geometry.

A system-level calibration method including temperature-related error coefficients for a strapdown inertial navigation system

The calibration of error coefficients for accelerometers and laser gyros is an effective way to improve the navigation precision of strapdown inertial navigation systems. The calibration parameters often change with temperature. This paper proposes a system-level calibration method including temperature-related error coefficients. The method includes an improved 18-step calibration scheme with temperature being changed by using a thermal chamber. A 42-dimensional Kalman filter is applied to estimate the error parameters including the bias, scale factor errors, installation errors and temperature-related error coefficients of accelerometers. This method has the great advantage of simplifying the calibration procedure and is widely applicable to all temperature-related error coefficients. Compared with the traditional calibration method at different temperature points, the calibration time of the proposed method is shortened by 24 h. The feasibility of this method is verified by simulations and navigation experiments. The results of navigation experiments show that the maximum positioning errors in pure inertial navigation decrease by approximately 30% after temperature compensation.

The Multi-Dimensional Actions Control Approach for Obstacle Avoidance Based on Reinforcement Learning

In robotics, obstacle avoidance is an essential ability for distance sensor-based robots. This type of robot has axisymmetrically distributed distance sensors to acquire obstacle distance, so the state is symmetrical. Training the control policy with a reinforcement learning method is a trend. Considering the complexity of environments, such as narrow paths and right-angle turns, robots will have a better ability if the control policy can control the steering direction and speed simultaneously. This paper proposes the multi-dimensional action control (MDAC) approach based on a reinforcement learning technique, which can be used in multiple continuous action space tasks. It adopts a hierarchical structure, which has high and low-level modules. Low-level policies output concrete actions and the high-level policy determines when to invoke low-level modules according to the environment’s features. We design robot navigation experiments with continuous action spaces to test the method’s performance. It is an end-to-end approach and can solve complex obstacle avoidance tasks in navigation.

Practical constrained output feedback formation control of underactuated vehicles via the autonomous dynamic logic guidance

This paper investigates the practical leader-follower formation control issue of underactuated vehicles. To achieve the waypoints-based formation navigation, the autonomous dynamic logic (ADL) guidance is proposed by incorporating the marine practice into the virtual ship-based formation guidance strategy. In the proposed guidance, only a dominant virtual leader is required for constructing the waypoints-based formation reference framework, which shows the simplicity and the practicability. As for the control part, a constrained output feedback algorithm is developed by means of the linear extended state observer (LESO). By constructing the augmented variable, the model uncertainty and unknown disturbances are integrated to be estimated and compensated together. In addition, a second-order dynamic auxiliary system is designed to handle the problem of actuator saturation, where two additional saturation compensation terms are introduced to stabilize the kinematics and the kinetics error dynamics, respectively, and the smoothness of constrained control signals can be guaranteed owing to the modification of Gaussian error function. Using the Lyapunov direct method, all signals in the closed-loop system are proved to be semi-global uniformly ultimately bounded (SGUUB). Finally, two simulation experiments, including the comparative experiment and the formation navigation experiment in the presence of simulated ocean disturbances, are carried out to illustrate the feasibility and the superiority of proposed scheme.