A Robust Visual-Aided Inertial Navigation Algorithm for Pedestrians

Real-time and robust state estimation for pedestrians is a challenging problem under the satellite denial environment. The zero-velocity-aided foot-mounted inertial navigation system, with the shortcomings of unobservable heading, error accumulation, and poorly adaptable parameters, is a conventional method to estimate the pose relative to a known origin. Visual and inertial fusion is a popular technology for state estimation over the past decades, but it cannot make full use of the movement characteristics of pedestrians. In this paper, we propose a novel visual-aided inertial navigation algorithm for pedestrians, which improves the robustness in the dynamic environment and for multi-motion pedestrians. The algorithm proposed combines the zero-velocity-aided INS with visual odometry to obtain more accurate pose estimation in various environments. And then, the parameters of INS have adjusted adaptively via taking errors between fusion estimation and INS outputs as observers in the factor graphs. We evaluate the performance of our system with real-world experiments. Results are compared with other algorithms to show that the absolute trajectory accuracy in the algorithm proposed has been greatly improved, especially in the dynamic scene and multi-motions trials.

Dynamical Analysis of a Navigation Algorithm

There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.

Fast Transfer Navigation for Autonomous Robots

Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released.

SINS/BDS Tightly-Coupled Integrated Navigation Algorithm for Hypersonic Vehicle

A tightly-coupled integrated navigation system (TCINS) for hypersonic vehicles is proposed when the satellite signals are disturbed. Firstly, the architecture of the integrated navigation system for the hypersonic vehicle is introduced. This system applies fiber SINS, BeiDou satellite receiver (BDS) and SOPC missile-born computer. Subsequently, the SINS mechanization for hypersonic vehicle is presented. The J2 model is employed for the normal gravity of the near space. An algorithm for updating the attitude, velocity and position is designed. State equations and measurement equations of SINS/BDS tightly-coupled integrated navigation for hypersonic vehicle are given, and a scheme of validity for satellite data is designed. Finally, the SINS/BDS tightly-coupled vehicle field tests and hardware-in-the-loop (HWIL) simulation tests are carried out. The vehicle field test and HWIL simulation results show that the heading angle error of tightly-coupled integrated navigation is within 0.2°, the pitch and roll angle errors are within 0.05°, the maximum velocity error is 0.3m/s, and the maximum position error is 10m.

Probabilistic Membrane Computing-Based SLAM for Patrol UAVs in Coal Mines

Patrol unmanned aerial vehicles (UAVs) in coal mines have high requirements for environmental perception. Because there are no GPS signals in a mine, it is necessary to use simultaneous localization and mapping (SLAM) to realize environmental perception for UAVs. Combined with complex coal mine environments, an integrated navigation algorithm for unmanned helicopter inertial measurement units (IMUs), light detection and ranging (LiDAR) systems, and depth cameras based on probabilistic membrane computing-based SLAM (PMC-SLAM) is proposed. First, based on an analysis of the working principle of each sensor, the mathematical models for the corresponding sensors are given. Second, an algorithm is designed for the membrane, and a probabilistic membrane system is constructed. The probabilistic SLAM map is constructed by sparse filtering. The experimental results show that PMC can further improve the accuracy of map construction. While adapting to the trend of intelligent precision mining in coal mines, this approach provides theoretical support and application practice for coal mine disaster prevention and control.

Hybrid Control of a Segway Platform Developed in MRDS

<p>A Segway RMP200 has been bought by Victoria University for the purpose of making an autonomous robot. The focus of this project was to create reusable services that use existing navigation algorithms to control the Segway within an indoor environment. A SICK LMS100 laser rangefinder was added to detect obstacles and allow localization of the Segway within a known map. A hybrid navigation algorithm consisting of an A* path planner with a dynamic window is used for motion planning and obstacle avoidance. The control system followed a Service Oriented Architecture implemented in Microsoft Robotics Studio using the C# .NET programming language. Four services were created during the project to interface with the SICK LMS100 scanner, control the Segway RMP200, implement the hybrid navigation algorithm and provide a graphic user interface for the system. Tests show that the Segway is able to navigate and maintain localisation within the operating environment by identifying and associating corner and door landmarks within the environment.</p>

Hybrid Control of a Segway Platform Developed in MRDS

<p>A Segway RMP200 has been bought by Victoria University for the purpose of making an autonomous robot. The focus of this project was to create reusable services that use existing navigation algorithms to control the Segway within an indoor environment. A SICK LMS100 laser rangefinder was added to detect obstacles and allow localization of the Segway within a known map. A hybrid navigation algorithm consisting of an A* path planner with a dynamic window is used for motion planning and obstacle avoidance. The control system followed a Service Oriented Architecture implemented in Microsoft Robotics Studio using the C# .NET programming language. Four services were created during the project to interface with the SICK LMS100 scanner, control the Segway RMP200, implement the hybrid navigation algorithm and provide a graphic user interface for the system. Tests show that the Segway is able to navigate and maintain localisation within the operating environment by identifying and associating corner and door landmarks within the environment.</p>

A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

In the near future, it’s expected that unmanned aerial vehicles (UAVs) will become ubiquitous surrogates for human-crewed vehicles in the field of border patrol, package delivery, etc. Therefore, many three-dimensional (3D) navigation algorithms based on different techniques, e.g., model predictive control (MPC)-based, navigation potential field-based, sliding mode control-based, and reinforcement learning-based, have been extensively studied in recent years to help achieve collision-free navigation. The vast majority of the 3D navigation algorithms perform well when obstacles are sparsely spaced, but fail when facing crowd-spaced obstacles, which causes a potential threat to UAV operations. In this paper, a 3D vision cone-based reactive navigation algorithm is proposed to enable small quadcopter UAVs to seek a path through crowd-spaced 3D obstacles to the destination without collisions. The proposed algorithm is simulated in MATLAB with different 3D obstacles settings to demonstrate its feasibility and compared with the other two existing 3D navigation algorithms to exhibit its superiority. Furthermore, a modified version of the proposed algorithm is also introduced and compared with the initially proposed algorithm to lay the foundation for future work.

A Gyroless Algorithm with Multi-Hypothesis Initialization for Projectile Navigation

Projectiles are subjected to a high acceleration shock at launch (20,000 g and higher) and can spin very fast. Thus, the components of onboard navigation units must therefore withstand such constraints in addition to being inexpensive. This makes only a few inertial sensors suitable for projectiles navigation. Particularly, rate gyroscopes which are gun-hardened and have an appropriate operating range are not widely available. On the other hand, magneto-resistive sensors are inexpensive and can satisfy both gun-hardening and operating range requirements, making them an alternative for angular estimation in guided projectiles. This paper presents a gyroless navigation algorithm for projectiles. The lack of gyroscope is handled by the usage of attitude kinematics computed over past attitude estimates of the filter, coupled with a measurement model based on magnetometer and GPS observations of the attitude. The observability of the attitude when considering non-calibrated magnetometers and its dependency on the initialization is addressed. Then, to cope with the initialization dependency of the filter, we proposed a multi-hypothesis initialization algorithm. In terms of performance, the algorithm is shown to provide a high-rate navigation solution with an interesting performance.