A gain-scheduling control strategy and short-term path optimization with genetic algorithm for autonomous navigation of a sailboat robot

The sidewalk is often located in complex environments such as buildings or covered by dense trees. Satellite navigation systems are susceptible to the surrounding environment, resulting in multipath effects, resulting in reduced positioning accuracy. Therefore, the global satellite navigation system (GNSS) and inertial navigation system (INS) based on kalman filter (KF) algorithm are studied, and the navigation algorithm and system model suitable for pedestrians are given. Considering that the pedestrian speed is low, the satellite navigation system obtains the carrier orientation information (heading angle), which also produces a large error. Therefore, the pedestrian dead reckoning (PDR) correction method is adopted to make the positional accuracy of the integrated navigation continuously stable. This paper proposes a pedestrian navigation system solution based on a single navigation device.

One of the major successes in ITS technology lately has been in the area of Advanced Traveler Information Systems (ATIS). In particular, there are now many good navigation systems that can advise drivers on route selection within a roadway network. This type of navigation software is now available on the Internet that can be used for trip planning, as well as in on-board navigation systems that can calculate optimal routes based on current vehicle location. The primary role of these navigation systems is to help drivers find their destinations. More advanced navigation systems can now help drivers avoid congestion on the roadways based on receiving real-time roadway data information. To date, these navigation systems have embedded algorithms that attempt to minimize trip distance and/or travel time. However, many drivers are now becoming increasingly concerned with rising fuel costs and pollutant emissions. Therefore, it is desirable to create new "environmentally-friendly" navigation algorithms. Taking advantage of the latest navigation technology, we have developed new navigation techniques that focus on minimizing energy consumption and pollutant emissions. These methods combine sophisticated mobile-source energy and emission models with route minimization algorithms that are used for navigational purposes. These new methods have been applied in several Southern California case studies resulting in significant energy savings compared to standard navigation techniques.

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.

Mobile robot navigation has become as a challenging problem over the last few decades. Despite the significant advances in this field, mobile robot researchers have yet to reach a comfortable level of satisfaction and yet still finding an improved algorithm for robot's navigation. In this paper, we proposed investigate the cognitive mapping algorithm that is been implemented to the mobile robot. This mobile robot will be given some inputs which are horizontal and vertical value and start and goal point and a task which is to move from start point to goal point. Some obstacles are also located in the arena and the mobile robot must be able to avoid any given obstacles to reach the goal point. The performance of the cognitive mapping algorithm is compared with an edge follower navigation algorithm, which is an algorithm for a mobile robot to navigate by following the edges or the wall. Based on the experiments that have been performed the cognitive mapping algorithm can be an alternative way for the robot's navigation systems.

Mobile robot, especially wheeled mobile robot, with its simple mechanical structure and inherent agility, has attracted significant attentions for dynamic environment applications in the last two decades (Pin et al. 1994 and Purwin 2006). A general mobile robot Guidance, Navigation and Control (GNC) system is illustrated in the following figure. The main function of guidance system is to generate a feasible trajectory command, usually in multiple dimension of freedom (DOF) to achieve a robot task. The objective of control system is to drive the mobile robot following the commanded trajectory with acceptable tracking errors and stability margin. The function of robot navigation system is to provide accurate position, velocity and/or orientation information for the control system and guidance system. A stable and accurate navigation system is the bridge between the guidance system and control system of mobile robots, which ultimately determines the robot performance.

One of the technologies in the industrial world that utilizes robots is the delivery of goods in warehouses, especially in the goods distribution process. This is very useful, especially in terms of resource efficiency and reducing human error. The existing system in this process usually uses the line follower concept on the robot's path with a camera sensor to determine the destination location. If the line and destination are not detected by the sensor or camera, the robot's navigation system will experience an error. it can happen if the sensor is dirty or the track is faded. The aim of this research is to develop a robot navigation system for efficient goods delivery in warehouses by integrating odometry and Dijkstra's algorithm for path planning. Holonomic robot is a robot that moves freely without changing direction to produce motion with high mobility. Dijkstra's algorithm is added to the holonomic robot to obtain the fastest trajectory. by calculating the distance of the node that has not been passed from the initial position, if in the calculation the algorithm finds a shorter distance it will be stored as a new route replacing the previously recorded route. the distance traversed by the djikstra algorithm is 780 mm while a distance of 1100 mm obtains the other routes. The time for using the Djikstra method is proven to be 5.3 seconds faster than the track without the Djikstra method with the same speed. Uneven track terrain can result in a shift in the robot's position so that it can affect the travel data. The conclusion is that odometry and Dijkstra's algorithm as a planning system and finding the shortest path are very efficient for warehouse robots to deliver goods than ordinary line followers without Dijkstra, both in terms of distance and travel time.

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.

A method for algorithmic correction of the inertial navigation system of an aircraft under conditions of autonomous operation of the inertial navigation system is considered. Compensation for navigation system errors is implemented by using a genetic algorithm. To increase the accuracy of building an error model in the genetic algorithm, the «shaking» method is used. The results of mathematical modeling demonstrated the used modification effectiveness of the genetic algorithm for correcting the inertial navigation system. Keywords genetic algorithm, inertial navigation system, prediction, errors, «shaking» method

Autonomous systems have demonstrated high performance in several applications. One of the most important is localisation systems, which are necessary for the safe navigation of autonomous cars or mobile robots. However, despite significant advances in this field, there are still areas open for research and improvement. Two of the most important challenges include the precise traversal of a bounded route and emergencies arising from the breakdown or failure of one or more sensors, which can lead to malfunction or system localisation failure. To address these issues, auxiliary assistance systems are necessary, enabling localisation for a safe return to the starting point, completing the trajectory, or facilitating an emergency stop in a designated area for such situations. Motivated by the exploration of applying artificial intelligence to pose estimation in a navigation system, this article introduces a monocular visual odometry method that, through teach and repeat, learns and autonomously replicates trajectories. Our proposal can serve as either a primary localisation system or an auxiliary assistance system. In the first case, our approach is applicable in scenarios where the traversing route remains unchanged. In the second case, the goal is to achieve a safe return to the starting point or to reach the end point of the trajectory. We initially utilised a publicly available dataset to showcase the learning capability and robustness under different visibility conditions to validate our proposal. Subsequently, we compared our approach with other well‐known methods to assess performance metrics. Finally, we evaluated real‐time trajectory replication on a ground robot, both simulated and real, across multiple trajectories of increasing complexity.

Medial axis-based machine-vision system for orchard robot navigation

The fusion of multi-sensor data can provide more accurate and reliable navigation performance than single-sensor methods. However, the general Federated Kalman Filter (FKF) is not suitable for large changes of complex nonlinear systems parameters and is not optimized for effective information sharing coefficients to estimate navigation preferences. This study concerns research on the FKF method for a nonlinear adaptive model based on an improved Genetic Algorithm (GA) for the Strapdown Inertial Navigation System (SINS) / Celestial Navigation System (CNS) / Global Positioning System (GPS) integrated multi-sensor navigation system. An improved fitness function avoids the premature convergence problem of a general GA and decimal coding improves its performance. The improved GA is used to build the adaptive FKF model and to select the optimized information sharing coefficients of the FKF. An Unscented Kalman Filter (UKF) is used to deal with the nonlinearity of integrated navigation system. Finally, a solution and implementation of the system is proposed and verified experimentally.

PurposeIn response to the navigation challenges faced by coal mine tunnel inspection robots in semistructured underground intersection environments, many current studies rely on structured map-based planning algorithms and trajectory tracking techniques. However, this approach is highly dependent on the accuracy of the global map, which can lead to deviations from the predetermined route or collisions with obstacles. To improve the environmental adaptability and navigation precision of the robot, this paper aims to propose an adaptive navigation system based on a two-dimensional (2D) LiDAR.Design/methodology/approachLeveraging the geometric features of coal mine tunnel environments, the clustering and fitting algorithms are used to construct a geometric model within the navigation system. This not only reduces the complexity of the navigation system but also optimizes local positioning. By constructing a local potential field, there is no need for path-fitting planning, thus enhancing the robot’s adaptability in intersection environments. The feasibility of the algorithm principles is validated through MATLAB and robot operating system simulations in this paper.FindingsThe experiments demonstrate that this method enables autonomous driving and optimized positioning capabilities in harsh environments, with high real-time performance and environmental adaptability, achieving a positioning error rate of less than 3%.Originality/valueThis paper presents an adaptive navigation system for a coal mine tunnel inspection robot using a 2D LiDAR sensor. The system improves robot attitude estimation and motion control accuracy to ensure safe and reliable navigation, especially at tunnel intersections.

In the recent decade, robotic exploration is emerging as a vast research platform as it prolongs to be a challenging task, especially in unknown dynamic environments. Exploration plays a vital role in collecting data from the areas inaccessible to the human. The operation of manual exploration systems in complex environments is cumbersome due to ineffective noise filtering, poor visibility and shuffling of map characteristics. In order to overcome such drawbacks, our approach focuses on the development of autonomous robotic exploration by designing a robust SLAM-based system, incorporating tri-layered mapping technique for automatic map generation and a new geometrical boundary-based path-planning technique to improve the mobility of the robot during navigation. The proposed path-planning algorithm yields better trajectory planning and obstacle avoidance using the pose, odometry and orientation parameters of the robot. The performance of the developed robot is evaluated through real-time experimentation, and it is evident from the results that, the proposed approach is preferable for autonomous exploration applications.

Aiming at the problem of positioning and navigation of mobile robots indoors, this paper designs and implements related functions based on Robot Operating System and laser radar. The core controller of the robot adopts an industrial computer, and is equipped with a laser radar to acquire information about the surrounding environment and develop related algorithms on the Robot Operating System. Realize Simultaneous Localization and Mapping function based on Cartographer algorithm, path planning based on optimal path algorithm and navigation function based on particle filter algorithm. The real machine experiment proves that the system can efficiently perform real-time synchronous positioning and map construction and navigation functions.

We exploit the complementary strengths of vision and pro-prioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully utilize this capability, we need a high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy in varying environments. We achieve this by using proprioceptive feedback to ensure the safety of the planned path by sensing unexpected obstacles like glass walls, terrain properties like slipperiness or softness of the ground and robot properties like extra payload that are likely missed by vision. The navigation system uses onboard cameras to generate an occupancy map and a corresponding cost map to reach the goal. A fast marching planner then generates a target path. A velocity command generator takes this as input to generate the desired velocity for the walking policy. A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator. We show superior performance compared to wheeled robot baselines, and ablation studies which have disjoint high-level planning and low-level control. We also show the real-world deployment of VP-Nav on a quadruped robot with onboard sensors and computation. Videos at https://navigation-locomotion.github.io