Optimizing autonomous navigation in unknown environments: A novel trap avoiding vector field histogram algorithm VFH+T

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.

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.

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.

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 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.

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.

Autonomously navigating a robot in an unknown scene comprises tasks such as mapping and localization from the percepts via sensors, safe motion-planning under partial knowledge of the scene, and finally, plan execution using the available motion primitives of the robot. Though many software systems are available for these sub-tasks, there is a lack of integrated software that support autonomous navigation. In this paper, we present an integrated software framework comprising of simultaneous localization and mapping module using visual odometry, a motion-planning module based on constraint-solving, and a plan-execution module particularly designed for a lizard-inspired quadruped robot. To demonstrate its efficacy, we present a number of navigation tasks to the software framework in various indoor simulation settings together with a model of the lizard-inspired quadruped robot and show the results.

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.

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.

Autonomous vehicle navigation in an unfamiliar environment is a challenging task. This investigation presents two algorithms for autonomous vehicle navigation in unknown environments with obstacles. Basic building blocks of the path-planning algorithms are the behaviour-based controller and nonlinear state estimator. Measurements from the sensor mounted on the vehicle are used as the input to the state estimator, whereas the estimated position of the vehicle with respect to the obstacle is the output. The estimate is then used to decide the possible control action from a family of behaviour-based controllers for navigating the vehicle. The first path-planning algorithm uses the extended Kalman filter (EKF) for estimating the vehicle position using measurements of the obstacle position. The second path-planning algorithm uses the unscented Kalman filter (UKF) to estimate the vehicle position. Our results indicate that UKF-based path-planning algorithm performs better than the EKF-based algorithm both in terms of performance and fuel consumption. This is indicated by a 9% reduction of the control effort. Path-planning algorithms presented in this investigation can be used to build reliable autonomous vehicle navigation systems.

Motion control of vehicles under uncertain, noisy, and discontinuous positioning is essential in autonomous navigation in unknown environments. This article suggests two methods for motion control, where the initial parameters of the on-line control are physically explainable, the resulting trajectory as well as the control parameters are asymptotically converging and glitches in the localization are handled robustly. The differences to a known method based on Lyapunov functions are discussed theoretically as well as in terms of actual motion measurements. Physical experiments with landbound vehicles show the reliability and limitations of these different methods in setups employing static attractors, systematically moving targets and fast, unpredictable moving targets in highly dynamical environments. Mainly due to the physical meaning of the control parameters the adaptation to actual kinematics and dynamics is significantly simplified.

In this paper, we propose a framework for online quadrotor motion planning for autonomous navigation in unknown environments. Based on the onboard state estimation and environment perception, we adopt a fast marching-based path searching method to find a path on a velocity field induced by the Euclidean signed distance field (ESDF) of the map, to achieve better time allocation. We generate a flight corridor for the quadrotor to travel through by inflating the path against the environment. We represent the trajectory as piecewise Bezier curves by using Bernstein polynomial basis and formulate the trajectory generation problem as typical convex programs. By using Bezier curves, we are able to bound positions and higher order dynamics of the trajectory entirely within safe regions. The proposed motion planning method is integrated into a customized light-weight quadrotor platform and is validated by presenting fully autonomous navigation in unknown cluttered indoor and outdoor environments. We also release our code for trajectory generation as an open-source package.

This research describes the development of an autonomous robotic triage system, carried out by a student through project-based and challenge-based learning methodologies, aimed at solving real-world problems using applied technologies. The system operated in three phases: environment exploration, victim detection through computer vision supported by autonomous navigation, and remote measurement of vital signs. The system incorporated SLAM algorithms for mapping and localization, YOLOv8 pose for human detection and posture estimation, and remote photoplethysmography (rPPG) for contactless vital-sign measurement. This configuration was integrated into a mobile platform (myAGV) equipped with a robotic manipulator (myCobot 280) and tested in scenarios simulating real emergency conditions. All three triage phases defined in this case study were executed continuously and autonomously, enabling navigation in unknown environments, human detection, and accurate positioning in front of victims to measure vital signs without human intervention. Although limitations were identified in low-light environments or in cases of facial obstruction, the modular ROS-based architecture was designed to be adaptable to other mobile platforms, thereby extending its applicability to more demanding scenarios and reinforcing its value as both an educational and technological solution in emergency response contexts.

Autonomous navigation in unknown environments with the intent of exploring all traversable areas is a significant challenge for robotic platforms. In this paper, a simple yet reliable method for exploring unknown environments is presented based on bio-inspired reactive control and metric-topological planning. The reactive control algorithm is modeled after the spatial decomposition of wide and small-field patterns of optic flow in the insect visuomotor system. Centering behaviour and small obstacle detection and avoidance are achieved through wide-field integration and Fourier residual analysis of instantaneous measured nearness respectively. A topological graph is estimated using image processing techniques on a continuous occupancy grid. Node paths are rapidly generated to navigate to the nearest unexplored edge in the graph. It is shown through rigorous field-testing that the proposed control and planning method is robust, reliable, and computationally efficient.

Robotic simulators play a crucial role in the development and testing of autonomous systems, particularly in the realm of Uncrewed Aerial Vehicles (UAV). However, existing simulators often lack high-level autonomy, hindering their immediate applicability to complex tasks such as autonomous navigation in unknown environments. This limitation stems from the challenge of integrating realistic physics, photorealistic rendering, and diverse sensor modalities into a single simulation environment. At the same time, the existing photorealistic UAV simulators use mostly hand-crafted environments with limited environment sizes, which prevents the testing of long-range missions. This restricts the usage of existing simulators to only low-level tasks such as control and collision avoidance. To this end, we propose the novel FlightForge UAV opensource simulator. FlightForge offers advanced rendering capabilities, diverse control modalities, and, foremost, procedural generation of environments. Moreover, the simulator is already integrated with a fully autonomous UAV system capable of long-range flights in cluttered unknown environments. The key innovation lies in novel procedural environment generation and seamless integration of high-level autonomy into the simulation environment. Experimental results demonstrate superior sensor rendering capability compared to existing simulators, and also the ability of autonomous navigation in almost infinite environments.