Vector Field Histogram Research Articles

A Design of Three-Dimensional Spatial Path Planning Algorithm Based on Vector Field Histogram.

In this paper, we present a novel three-dimensional spatial path planning algorithm based on the Vector Field Histogram* (VFH*) approach, specifically tailored for underwater robotics applications. Our method leverages the strengths of VFH* in obstacle avoidance while enhancing its capability to handle complex three-dimensional environments. Through extensive simulations, we demonstrate the superior performance of our algorithm compared to traditional methods, such as RS-RRT algorithm. Our results show significant improvements in terms of computational efficiency and path optimality, making it a viable solution for real-time path planning in dynamic underwater environments.

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

The Vector Field Histogram Plus (VFH+) algorithm is a cornerstone in robotic navigation, renowned for its efficiency and straightforward implementation across a multitude of environments. Despite its widespread utility, the algorithm's inherent limitations in handling complex obstacle entrapments necessitate refinement. This paper presents an advanced iteration, designated as VFH + T, which incorporates sophisticated memory-based trap recognition and avoidance mechanisms. This enhancement facilitates dynamic adjustment of navigation strategies through the integration of geometrical rules that retrospectively inform path planning decisions. Moreover, the VFH + T algorithm intricately melds the robotic platform's kinematic and dynamic constraints, optimizing real-time navigational commands based on both current sensory input and historical environmental interactions. Empirical simulations validate the enhanced algorithm's proficiency in circumventing navigational traps, improving operational safety and efficiency. Comparative analysis with VFH+ and VFH* algorithms show up to 17 % reduction in traveling distance due to the trap-avoidance technique during navigation. This advancement holds significant implications for enhancing autonomous navigation technologies in various practical applications, from self-driving vehicles to robotic aids in logistics and service industries.

Navigation Techniques and Algorithms for Mobile Robots and Automated Guided Vehicles

Mobile robots are self-contained devices that can navigate and move around their surroundings. They observe their environment and create maps using different sensors such as lidar, Global Positioning System (GPS), and cameras. Automated Guided Vehicles (AGVs) are vehicles that can move independently utilizing different approaches such as underground cables, laser scanners, or GPS systems. This research investigates several navigation methodologies and algorithms used in AGVs and mobile robots. It focusses on the advantages of integrating lidar technology with other sensors as well as how it is used in local navigation techniques. We look at two motion planning techniques for obstacle avoidance: the Artificial Potential Field (APF) approach and the Vector Field Histogram (VFH) algorithm. Two effective techniques for finding the best routes are discussed: the A* algorithm and Dijkstra's algorithm. The various AGV types and their navigation systems (such as wired, guide, laser, vision-based, and gyro-based) are also examined in the study. Neural networks and fuzzy logic are investigated as AGV control techniques, with line following and obstacle avoidance as examples of their use. The study emphasizes how crucial precise and dependable navigation systems are to the effective and secure operation of mobile robots and AGVs.

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving system for obstacle avoidance based on a trained reinforcement learning (RL) algorithm. The system takes the desired direction and speed from the user via a joystick and the obstacle distribution from a LiDAR placed in front of the wheelchair. Considering both inputs, the system outputs a pair of forward and rotational speeds that ensure obstacle avoidance while being as close as possible to the user commands. We validated it through simulations and compared it with a vector field histogram (VFH). The preliminary results show that the RL algorithm does not disruptively alter the user intention, reduces the number of collisions, and provides better door passages than a VFH; furthermore, it can be integrated on an embedded device. However, it still suffers from higher jerkiness.

Path planning for unmanned load–haul–dump machines based on a VHF_A* algorithm

This study aimed to address the low search efficiency, long planning time, and unsmooth path problems in narrow and curved underground mining cave environments using traditional path planning algorithms. Thus, a new path planning method (VFH_A *) based on VFA and A* is proposed for unmanned load–haul–dump (LHD) operating in underground mines. First, the trajectory of the LHD articulation point is considered, the nodes are extended using this point, and collision detection is performed. Notably, the extended nodes neither collide nor conform to the trajectory of the LHD. Second, the vector field histogram (VFH) algorithm is introduced, and the steering and collision threat costs are combined with the comprehensive cost function of the traditional A* algorithm to form the VFH_A* algorithm. Thus, the node with the lowest comprehensive cost is selected for expansion, and redundant nodes are eliminated from the searched node paths. Third, the path is smoothed via Bezier interpolation. This ensures that the LHD operates smoothly and prevents excessive changes in the articulation angle. The proposed method was evaluated using above-ground and underground simulations. Compared with A* and GA algorithms, the VFH_A* algorithm significantly improved the search efficiency and can efficiently generate safe and smooth task paths in different scenarios. The planning time for each scenario was reduced by 85%. Finally, tracking experiments were conducted on the planned task paths of 1.5 and 2.5 m/s, indicating that the tracking error was less than 0.23 m. Overall, the planned path meets the requirements of unmanned LHD, indicating that the proposed method can adapt to practical applications.

Obstacle avoidance and collision avoidance of UAV swarm based on improved VFH algorithm and information sharing strategy

An obstacle avoidance and collision avoidance approach based on an improved VFH algorithm with Apollonius circle and information sharing strategy is put forward with the goal of guiding UAV swarms in formation flight in an unknown complex environment. For the second-order consensus protocol, a cooperative control law with the virtual leader is presented. For collision avoidance, the vector field histogram algorithm with Apollonius circle is proposed, and the obstacle sharing strategy is proposed to avoid obstacles in advance and solve the formation separation problem. The Apollonius circle is introduced to prevent the entanglement problem brought on by symmetry conflicts, and this approach aims to address the problem of collision avoidance among UAVs by updating the polar coordinate histogram based on the VFH algorithm in accordance with the motion state and relative position of other UAVs in the communication neighborhood. The equivalent detection range of UAVs in the swarm connected topology can be increased by sharing the locations of identified obstacles, improving total obstacle avoidance. Also, to reduce repetitive data interactions and communication loads when sharing obstacle information, a data offset mechanism is proposed, which stores the obstacle information in a chronological queue and sets a data offset in the queue for each UAV in the communication neighborhood to indicate the degree of synchronization with the data in the queue. Finally, simulation results show the effectiveness of the algorithm in collision avoidance and real-time obstacle avoidance of UAV swarm. Additionally, a data offset mechanism is suggested, which stores obstacle information in a chronological queue and sets a data offset in the queue for each UAV in the communication neighborhood to represent the degree of synchronization with the data in the queue, to reduce repetitive data interactions and communication loads when sharing obstacle information. Finally, simulation results demonstrate the algorithm’s effectiveness in real-time obstacle avoidance and collision avoidance for UAV swarms.

Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach

In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight.

Autonomous aerial obstacle avoidance using LiDAR sensor fusion.

The obstacle avoidance problem of unmanned aerial vehicle (UAV) mainly refers to the design of a method that can safely reach the target point from the starting point in an unknown flight environment. In this paper, we mainly propose an obstacle avoidance method composed of three modules: environment perception, algorithm obstacle avoidance and motion control. Our method realizes the function of reasonable and safe obstacle avoidance of UAV in low-altitude complex environments. Firstly, we use the light detection and ranging (LiDAR) sensor to perceive obstacles around the environment. Next, the sensor data is processed by the vector field histogram (VFH) algorithm to output the desired speed of drone flight. Finally, the expected speed value is sent to the quadrotor flight control and realizes autonomous obstacle avoidance flight of the drone. We verify the effectiveness and feasibility of the proposed method in 3D simulation environment.

Localization and Navigation System for Indoor Mobile Robot

Visually impaired people usually find it hard to travel independently in many public places such as airports and shopping malls due to the problems of obstacle avoidance and guidance to the desired location. Therefore, in the highly dynamic indoor environment, how to improve indoor navigation robot localization and navigation accuracy so that they guide the visually impaired well becomes a problem. One way is to use visual SLAM. However, typical visual SLAM either assumes a static environment, which may lead to less accurate results in dynamic environments or assumes that the targets are all dynamic and removes all the feature points above, sacrificing computational speed to a large extent with the available computational power. This paper seeks to explore marginal localization and navigation systems for indoor navigation robotics. The proposed system is designed to improve localization and navigation accuracy in highly dynamic environments by identifying and tracking potentially moving objects and using vector field histograms for local path planning and obstacle avoidance. The system has been tested on a public indoor RGB-D dataset, and the results show that the new system improves accuracy and robustness while reducing computation time in highly dynamic indoor scenes.

2D LiDAR Based Reinforcement Learning for Multi-Target Path Planning in Unknown Environment

Global path planning techniques have been widely employed in solving path planning problems, however they have been found to be unsuitable for unknown environments. Contrarily, the traditional Q-learning method, which is a common reinforcement learning approach for local path planning, is unable to complete the task for multiple targets. To address these limitations, this paper proposes a modified Q-learning method, called Vector Field Histogram based Q-learning (VFH-QL) utilized the VFH information in state space representation and reward function, based on a 2D LiDAR sensor. We compared the performance of our proposed method with the classical Q-learning method (CQL) through training experiments that were conducted in a simulated environment with a size of 400 square pixels, representing a 20-meter square map. The environment contained static obstacles and a single mobile robot. Two experiments were conducted: experiment A involved path planning for a single target, while experiment B involved path planning for multiple targets. The results of experiment A showed that VFH-QL method had 87.06% less training time and 99.98% better obstacle avoidance compared to CQL. In experiment B, VFH-QL method was found to have an average training time that was 95.69% less than that of the CQL method and 83.99% better path quality. The VFH-QL method was then evaluated using a benchmark dataset. The results indicated that the VFH-QL exhibited superior path quality, with efficiency of 94.89% and improvements of 96.91% and 96.69% over CQL and SARSA in the task of path planning for multiple targets in unknown environments.

Game theoretical trajectory planning enhances social acceptability of robots by humans

Since humans and robots are increasingly sharing portions of their operational spaces, experimental evidence is needed to ascertain the safety and social acceptability of robots in human-populated environments. Although several studies have aimed at devising strategies for robot trajectory planning to perform safe motion in populated environments, a few efforts have measured to what extent a robot trajectory is accepted by humans. Here, we present a navigation system for autonomous robots that ensures safety and social acceptability of robotic trajectories. We overcome the typical reactive nature of state-of-the-art trajectory planners by leveraging non-cooperative game theory to design a planner that encapsulates human-like features of preservation of a personal space, recognition of groups, sequential and strategized decision making, and smooth obstacle avoidance. Social acceptability is measured through a variation of the Turing test administered in the form of a survey questionnaire to a pool of 691 participants. Comparison terms for our tests are a state-of-the-art navigation algorithm (Enhanced Vector Field Histogram, VFH) and purely human trajectories. While all participants easily recognized the non-human nature of VFH-generated trajectories, the distinction between game-theoretical trajectories and human ones were hardly revealed. Our results mark a strong milestone toward the full integration of robots in social environments.

A Study on the Usage of Robot Navigation to Determine Robot’s Own Position in its Frame of Reference and then to Plan a Path towards Some Goal Location

In this study, the issues and opportunities can be achieved with the help of a robotic navigation system. The determination of a robotic navigational system is able to ensure the proper development of different understandings which are achieved with the help of different problems as these can solve different issues. Navigational robots are used in different fields and provide great growth in these fields which are effective and promotional developments are understood. Drones are used for different navigational improvements such as mapping which provide accuracy in the system as these are beneficial for gaining growth in goal achievement through different AI-based software systems. Secondary qualitative research is done for the completion of the research which is able to provide proper knowledge in this research in the field of robot navigation. Nowadays navigation can be easily accessible with the help of GPS systems as people are able to get the location of any place from anywhere on the internet. These facilities are ensuring proper development in the business process of companies as different businesses achieve their profitability with the help of a robotic navigational system that has gained progressive development in the research. The understanding of the robot's own location is able to develop the quality of the robot's ability which are helping to ensure planning toward the goal location. Real-time navigation is creating different ways for aiming towards navigational path planning which are helping to achieve material handling, patrolling, disaster relief, and rescue operations. Keywords : Artificial potential field, autonomous navigation, Dijkstra, extended Kalman filter, Global Navigation Satellite System, Global Positioning System, hybridization techniques, LIDAR, local navigation system, near fields communication, Robotic navigation, Vector field histograms.

End-to-end UAV obstacle avoidance decision based on deep reinforcement learning

Aiming at the problem that the traditional UAV obstacle avoidance algorithm needs to build offline three-dimensional maps, discontinuous speed control and limited speed direction selection, we study the end-to-end obstacle avoidance decision method of UAV continuous action output based on DDPG(deep deterministic policy gradient) deep reinforcement learning algorithm. Firstly, an end-to-end decision control model based on DDPG algorithm is established. The model can output continuous control variables, namely UAV obstacle avoidance actions, according to the continuous state information perceived. Secondly, the training verification is carried out on the platform of UE4 + Airsim. The results show that the model can realize the end-to-end UAV obstacle avoidance decision. Finally, the 3DVFH(three dimensional vector field histogram) obstacle avoidance algorithm model with the same data source is compared and analyzed. The experiment shows that DDPG algorithm has better optimization effect on the obstacle avoidance trajectory of UAV.

Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics

Autonomous ground vehicle convoys heavily rely on wireless communications to perform leader-follower operations, which make them particularly vulnerable to denial-of-service attacks such as jamming. To mitigate the effects of jamming on autonomous convoys, this paper proposes a behavior-based architecture, called the Behavior Manager, that utilizes layered costmaps and vector field histogram motion planning to implement motor schema behaviors. Using our proposed Behavior Manager, multiple behaviors can be created to form a convoy controller assemblage capable of continuing convoy operations while under a jamming attack. To measure the performance of our proposed solution to jammed autonomous convoying, simulated convoy runs are performed on multiple path plans under different types of jamming attacks, using both the assemblage and a basic delayed follower convoy controller. Extensive simulation results demonstrated that our proposed solution, the Behavior Manager, can be leveraged to dramatically improve the robustness of autonomous convoys when faced with jamming attacks and can be further extended due to its modular nature to combat other types of attacks through the development of additional behaviors and assemblages. When comparing the performance of the Behavior Manager convoy to that of the basic convoy controller, improvements were seen across all jammer types and path plans, ranging from 13.33% to 86.61% reductions in path error.

Intelligent Vector Field Histogram based collision avoidance method for AUV

Intelligent Vector Field Histogram (IVFH) is proposed to accomplish the collision avoidance of Autonomous Underwater Vehicle (AUV) in underwater three-dimensional dynamic space. The directed cuboid static obstacle environment model is introduced to improve the efficiency of collision avoidance decision-making. The candidate direction set is composed of relative heading and relative pitch. According to the obstacle distribution of local environment, the traveling fitness of each candidate direction is calculated from the perspectives of safety and rapidity, so as to realize the collision avoidance of AUV in three-dimensional dynamic environment. IVFH method can adaptively adjust the parameters that affect the collision avoidance action amplitude according to the obstacle distance and size. The collision avoidance velocity is adjusted by fuzzy logic according to the environment conditions, so as to ensure that AUV can fully perceive the environment and then complete the collision avoidance decision-making. The simulation results show that IVFH method can overcome the threshold sensitivity of traditional VFH method, escape from the trap area, and realize collision avoidance in three-dimensional dynamic complex obstacle environment. The proposed IVFH collision avoidance method has been verified by simulations, as well as lake and sea experiments, demonstrating its effectiveness and practicability.

USV collision hazard assessment and track planning algorithm

To improve the autonomous collision avoidance capability of an unmanned surface vehicle (USV) under complex working conditions, this study proposes a local track planning algorithm for USVs based on collision hazard assessment. This algorithm uses hierarchical analysis and discrepancy method to determine the collision risk index (CRI) values of different encounter scenarios, and then introduces fuzzy inference theory to construct a quantitative collision hazard assessment model based on the principle of combining subjective and objective assessment and the pessimistic principle of class classification, which provides a decision basis for a collision avoidance plan. In response to the unexpected encounter, an improved vector field histogram algorithm is proposed to use the CRI value of the obstacle as the key parameter of the histogram, solve the restricted area by removing the grid model and threshold improvement, establish the cost function to calculate the optimal sailing interval under the rules and dynamics constraints of the Convention on International Regulations for Preventing Collisions at Sea (COLREGS), and realise the local target point by tracking local track planning during autonomous collision avoidance. Simulation and on-lake experiments demonstrate the effectiveness of the collision hazard assessment model and local track planning algorithm proposed in this paper.

An Autonomous Control Framework of Unmanned Helicopter Operations for Low-Altitude Flight in Mountainous Terrains

Low-altitude flight in mountainous terrains is a difficult flight task applied in both military and civilian fields. The helicopter has to maintain low altitude to realize search and rescue, reconnaissance, penetration, and strike operations. It contains complex environment perception, multilevel decision making, and multi-objective flight control; thus, flight is currently mainly conducted by human pilots. In this work, a control framework is implemented to realize autonomous flight for unmanned helicopter operations in an unknown mountainous environment. The identification of targets and threats is introduced using a deep neural network. A 3D vector field histogram method is adopted for local terrain avoidance based on airborne Lidar sensors. In particular, we propose an intuitive direct-viewing method to judge and change the visibilities of the helicopter. On this basis, a finite state machine is built for decision making of the autonomous flight. A highly realistic simulation environment is established to verify the proposed control framework. The simulation results demonstrate that the helicopter can autonomously complete flight missions including a fast approach, threat avoidance, cover concealment, and circuitous flight operations similar to human pilots. The proposed control framework provides an effective solution for complex flight tasks and expands the flight control technologies for high-level unmanned helicopter operations.

Multi-Level Control System for an Intelligent Robot that is Part of a Group

The article is devoted to the application of a group of robotic complexes for military purposes. The current state of control systems of single robotic complexes does not allow solving all the tasks assigned to the robot. The analysis of methods of controlling a group of robots in combat conditions is carried out. The necessity of using a multi-level control system for an intelligent combat robot is justified. A multi-level control system for an intelligent robot is proposed. Such a system assumes the possibility of controlling the robot in one of four modes: remote, supervisory, autonomous and group. Moreover, each robot, depending on the external conditions and its condition, can be in any control mode. The application of the technique is shown by the example of the movement of a group of robots with an interval along the front. The problem of the movement of slave robots behind the leader is considered. When forming the robot control algorithm, the method of finite automata was used. The algorithm controls the movement of the RTK in various operating modes: group control mode and autonomous movement mode. In the group control mode, the task is implemented: movement for the leader. For the state of "Movement in formation", an algorithm for forming the trajectory of the movement of guided robots was implemented. An algorithm for approximating the Bezier curve was used. It allows you to build a trajectory for the slave robot. On the basis of the obtained trajectory, the angular and linear velocity were calculated. In the autonomous control mode, two tasks are solved: moving to a given point and avoiding obstacles. Vector Field Histogram was used as an algorithm for detouring an obstacle, which determines the direction of movement without obstacles. The state of "Movement to a given point" is based on Pure Pursuit as a simple and reliable algorithm for solving such problems. A computer model of the movement of a group of robots was developed. The model is implemented in the MATLAB program using the Simulink and Mobile Robotics Simulation Toolbox libraries. Several different variants of the movement of the RTK group are modeled, which differ from each other in the initial location of the robots and the position of obstacles. The conducted computer simulation showed the efficiency and effectiveness of the proposed method of RTC control.

Human Following for Outdoor Mobile Robots Based on Point‐Cloud's Appearance Model

In this paper, we propose a point-cloud-based algorithm for human-following robots to detect and follow the target person in a complex outdoor environment. Specifically, we exploit AdaBoost to train a binary classifier in a designed feature space based on sparse point-cloud to distinguish the target person from other objects. Then a particle filter is applied to continuously track the target's position. Motivated by the interference of obstacles in long-distance human-following scenarios, a motion plan algorithm based on vector field histogram is adopted. Experiments are carried out both on the dataset we collected and in real application scenarios. The results show that our algorithm has the ability of real-time target detection and tracking, and is robust to deal with complex situations in outdoor environments.

Intelligent Obstacle Avoiding AGV Using Vector Field Histogram and Supervisory Control

Automated Guided Vehicles are used to help the process of carrying materials from one place to another. AGVs are widely used in the materials handling process in different industries hence, the reasoning behind their extremely wide usage in industries such as Automotive, manufacturing, Medical, etc. This research deals with the obstacle avoidance of Automated Guided Vehicles (AGV). This paper propose an algorithm that helps to build obstacle-free trajectories for AGV that leave the original track and again get back to the original track by avoiding the obstacle. The new path also have to act according to the dynamic & kinematic constraints of the AGV. The proposed method will be validated using simulations.