地図と移動の不確かさを考慮した未知環境における移動ロボットの適応的速度制御

When autonomous mobile robot is navigating in an unknown environment, the difficulty is avoiding unexpected objects on its way to the goal. This paper presents a new parallel double-layered control system of an autonomous mobile to avoid static or dynamic objects in uncertain environments. Fuzzy control system is a simple way to lay out robot tracking rapidly. The fuzzy control system is consistency and consistency, and easily realized. Simulation results and experiments on the actual robot show that the proposed method can be efficiently applied to robot navigation in complex and unknown environments.

This paper proposed a PSO-based algorithm for parameters optimization of multi-robot formation navigation in unknown environment. In order to achieve formation navigation in unknown environment, each robot in formation adopts motor schema-based reactive control architecture which has four primitive behaviors called move_to_goal, keep_formation, avoid_static_obstacle and avoid_robot behaviors.The behavior output to direct the movement of robot is made by the combination of four primitive behaviors. Particle Swarm Optimization algorithm as an unsupervised learning method for a reactive control architecture greatly reduces the effort required to configure reactive control parameters of multi-robot formation system. The validity of this method is verified through computer simulations in different types of in environments by robot simulation software MissionLab.

Several algorithms have been developed to help guide mobile robots in unknown environments. Various kinds of Bug algorithms are available and each one these algorithms has an advantage over the others under different circumstances. This paper introduces a new approach, the Diligent-Bug (D-Bug) algorithm, which is developed to enable a collision free navigation of robots in an unknown 2-dimensional environment. Static obstacles of arbitrary shapes have been considered to evaluate the developed algorithm. This algorithm also enables robots to avoid getting stuck in both local and global loops.

The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

In this paper, the global adaptive speed tracking control algorithm with accurate parameters estimation of the surface mounted permanent magnet synchronous motors is presented. The adaptive controller is designed using second Lyapunov method and does not require over-parametrization. The algorithm may be used for fast self-commissioning of all motor parameters as well as adaptive speed controller with online identification of some parameters. During self-commissioning, in the first stage the stator resistance and inductance are estimated in motionless mode. Second stage provides identification of all mechanical parameters in speed control mode. Simulation results proof the effectiveness of the theoretical findings.

With the increase in tightening of carbon neutral policies, electric new energy vehicles have become a top priority in the development of the automotive industry. The integrated electric vehicle wheel (hub motor) can effectively optimize the vehicle structure and reduce the weight of the vehicle and reduce the energy consumption. At the same time, the feature that four tires can be driven separately is also conducive to the application of various new control algorithms. In this paper, a generalized adaptive PID control is experimented with the speed control of the hub motor, and a fuzzy control-based variable step method is proposed for the overshoot or even runaway problem caused by the inertia of the hub motor in the process of adaptive PID control. Experiments have demonstrated that this method can effectively reduce the amount of overshoot triggered by adaptive control and shorten the regulation time into a steady state.

We present an adaptive fast-approximation for sensor configuration which finds near-optimal placements and sensor field of views (FoV). The fast-approximation, either via partition-based or density-based cluster analysis, adapts based on the relation between statistical uncertainty of the path plan and environmental uncertainty. The sensor configurations are performed over regions of interest which most directly influence the path-planning efforts. These regions of interest can include exploratory paths by sampling the probabilistic environment model. The path-planning efforts aim to decide upon a path which minimizes an agent’s exposure to threats in an unknown static environment. The noisy sensor network observations are used to construct a threat field estimate using Gaussian Process Regression each iteration with a stationary kernel and heteroscedastic gaussian likelihood. The optimization of a task-driven information gain determines optimal sensor configurations when maximized. The numerical performance of the direct optimization and the adaptive cluster analysis method is presented. Finally, we show that the cluster centers can be utilized as a dimensionality reduction technique for FoV optimization whereby we only optimize FoV radial coverage.

The main problems with autonomous navigation system that uses GPS is the satellite outages and the mapping of the ambience in unknown environment. Techniques was presented to increase position accuracy and navigation robustness in unknown environment by means of collaborative navigation. When vehicle one is navigating, lines of sight to satellites will often be blocked, however, vehicle two at different locations can have different sets of visible satellites and act as virtual satellite. Through collaboration, the satellite information can be shared between vehicles; then, better navigation performance will be achieved. At the same time, vehicle one can map its environment and locate itself accurately even when GPS signal is artificially broken. Simulation results indicate increased accuracy when using the method under investigation. Collaborative autonomous navigation can help several relatively simple vehicles achieve the same performance as expensive and complex platforms.

The switched reluctance motor drive (SRD) has obtained great attention as an AC stepless speed control system due to its large regulating scope, low cost and ruggedness. However, its strong nonlinearity and multivariable characteristic make it difficult to control. To solve the problem, this paper presents an approach of adaptive PWM speed control for switched reluctance motors (SRM) based on RBF neural network. This method builds up a speed controller based on RBF neural network which has powerful approximating ability and fast convergence property. The controller is trained off-line in advance, and then with the motor's operation, the on-line training of it makes its parameters vary with the environment in order to improve the control performance. In addition, another RBF network is constructed to offer gradient parameters, which is needed by the on-line training, via on-line identification. The results of experiments prove that the approach has lots of advantages in response speed, control accuracy and adaptability.

In the absence of GPS signals, Simultaneous Localization and Mapping (SLAM) technology enables unmanned systems to accomplish map construction and autonomous navigation in unknown environments. At present, there are mainly methods for SLAM based on sensors such as lidar, camera, ultrasonic, Inertial Measurement Unit (IMU) and odometer for location environment. However, there are certain limitations in using a single sensor for SLAM in unknown environment. In order to solve the above problems, this paper summarizes the key problems faced by mainstream SLAM at present, and proposes a SLAM method based on multi-sensor fusion, which can make full use of the advantages of various sensors from the hardware structure and make up for the shortcomings of using a single sensor. Finally, the proposed method was verified on the platform of “JAC Electric Vehicle”, and the experimental results show that the proposed method can effectively avoid the influence caused by signal weakening, improve the positioning accuracy, and the system has stronger robustness and better tracking performance.

This research is a study on improving information facilitated by common GPS based mobile application for navigation especially by the blind users. For a blind person, the main source of information is words and how to improve the effectiveness of the explanation using mobile application for them is tested in this research. The long term goal of technology is to create a movable, self-contained system that allows visually impaired people to navigate through unfamiliar environments without the assistance of guides The experiment for the research took place at MFB (Malaysian Foundation for the Blind) using existing GPS based mobile applications and the results were used to prove the hypothesis that the blind are at a considerable downside because of the absence of information and providing a detailed description of the environment could be used in the GPS navigation applications for the blind pedestrians to help them navigate independently and it would enable them to take up GPS for independent navigation in unknown environment. The information derived from the research could further help in creating and improving the semantics of the GPS based navigation technology for the blind pedestrians in an unknown environment.

This paper proposes a new method, minimum risk approach, to address the local path planning to escape from local minimum during goal-oriented robot navigation in unknown environments. This approach is theoretically proved to guarantee global convergence even in the long-wall, unstructured, cluttered, maze-like, and modified environments. The approach adopts a strategy of multi-behavior coordination, in which a novel path-searching behavior is developed to recommend the regional direction with minimum risk. The paper provides a fuzzy logic framework to implement the behavior design and coordination. It is verified by the simulated and real world tests.

Autonomous navigation in unknown environments holds significance for mobile robots. This article presents a novel efficient path planner, named E-Planner, which utilizes the visibility graph by the obstacle contour optimization and prioritized exploration mechanism. The E-Planner is designed for car-like mobile robots operating in unknown environments. In order to enhance the efficiency of constructing the visibility graph, an obstacle contour optimization strategy is devised to eliminate unnecessary vertices and edges. Furthermore, a breadth-first search algorithm is employed along with prioritized exploration and a timer mechanism to reduce the wastage of exploration space during the path searching process. Additionally, a pure tracking method based on dynamic sight distance is proposed to fulfill the kinematic constraints for Ackermann steering. To validate the effectiveness of these innovative approaches, simulations, and experiments were conducted. The results demonstrate that the proposed obstacle contour optimization strategy significantly reduces the time required for path search. Our method is mostly 25.3% faster than the FAR Planner, and the path is mostly 39.9% shorter, indicating substantial real-time potential. Further research could explore the applicability of these methods to other robotic systems.

The human brain is able to generate guidance strategies in unknown environments and improve performance over multiple trials. Such capabilities are challenging to implement in autonomous systems. This research uses a computational framework based on a spatial value function and invariants, described as interaction patterns, in the function to investigate human planning and learning in unknown environments. A simulation system was used for human guidance experiments in a simulated obstacle field unknown to the subjects before the experiments. The system recorded vehicle trajectory, control inputs, and human gaze over multiple trials, between specified start and goal locations. Human guidance policy is evaluated in comparison with an optimal control baseline, formulated using interaction patterns in a Dubins optimal solution. The subjects' control characteristics explain why exploratory behavior varies from one individual to the next. The authors utilize gaze spatial density and an information gain model, employing gaze location data, to investigate the visual cues that support guidance and navigation.

Interior permanent magnet synchronous motors (IPMSMs) which are driven by matrix converters have many advantages, including one‐stage power conversion, high efficiency, bidirectional power transfer, near unity input power factor, and low input current harmonics. However, the virtual DC‐link voltages of matrix‐converters vary from 0.866 to and the current commutations of matrix‐converters create serious three‐phase stator harmonic currents as well. Moreover, matrix‐converter‐based IPMSM drive systems, in general, have very large external loads, which require advanced control methods. To solve these problems, two adaptive controllers, including a model reference adaptive controller and a self‐tuning controller, are investigated in this article for matrix‐converter PMSM drive systems. From the experimental results, the proposed two adaptive speed controllers provide excellent dynamic responses, which are superior to PI speed controllers, including faster transient responses, better tracking abilities, and greater robustness. As a result, this proposed matrix‐converter‐based IPMSM drive system is very suitable in industrial applications.