Vector Field Histogram Algorithm Research Articles

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, optimising 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.

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.

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.

Navigation of Mini Unmanned Aerial Vehicle in Unknown Environment

In this paper, two algorithms are proposed to navigate the mini unmanned aerial vehicle (MUAV) in unknown environment. The first algorithm is path planning algorithm, it proposed to force the MUAV to track a specific trajectory. The second algorithm is obstacle avoidance algorithm, it proposed to avoid the collision between the MUAV and borrower when the MUAV is maneuvering in unknown environment. The pure pursuit algorithm (PPA) is used to force the MUAV to follow the desired trajectory depends on the given waypoints. While the vector field histogram algorithm (VFHA) is used to avoid collision of the MUAV with obstacles block in the desired path. To keep the flight of MUAV without collision, the 3D laser sensors are used to collect the information from the surrounding environment. The GPS sensor is used to keep the MUAV on the specific path. The collected information from the two sensors are analysed by the controller of the MUAV to improve its performance. The Matlab program is used to implement the proposed algorithms and plot the trajectories of MUAV in the unknown algorithm. The Matlab program is connected with the gazebo program via robotic operating system (ROS) to simulate the visual movement of the MUAV in any unknown environment. The obtained results show the effectiveness of the proposed algorithm to flight the MUAV in different environments.

Robust Trajectory Tracking Control and Obstacles Avoidance Algorithm for Quadrotor Unmanned Aerial Vehicle

This paper addresses the designing of a robust controller for an automatic landing, trajectory tracking and take-off missions of quadrotor unmanned aerial vehicle (QUAV). This has been investigated where the QUAV’s dynamic model involves nonlinearity, uncertainties, and coupling which makes the QUAV has a very complex system. The proposed controller can control both the position and orientation in addition to control the driving motors. For controlling the position, an appropriate control signal is generated for adjusting the altitude of the QUAV in a working space. To achieve this, three adaptive fuzzy controllers have been designed for three-dimensional coordinates i.e. x, y and z axes. For orientation control, three proportional derivative integral controllers (PIDCs) are introduced to control pitch, roll and yaw angles and make them reaching the desired values. Moreover, PID controllers are proposed for controlling the four driving motors. The parameters of both fuzzy and PID controllers are tuned by using particle swarm optimization (PSO) algorithm which enables the selection of the optimal values for each controller. For comparison purposes, the adaptive fuzzy controllers in the first layer of the proposed control system are replaced with PIDCs to prove the effectiveness of the proposed control system. Furthermore, a Lyaounov theory is utilized for studying the stability of fuzzy controllers. The proposed control system is capable of guiding the QUAV to track the previously defined reference trajectories. For obstacle avoidance, a vector field histogram algorithm is used to avoid collision of the QUAV with obstructing obstacles that block the QUAV’s path.

Combing VFH with bezier for motion planning of an autonomous vehicle

Vector Field Histogram (VFH) is a method for mobile robot obstacle avoidance. However, due to the nonholonomic constraints of the vehicle, the algorithm is seldom applied to autonomous vehicles. Especially when we expect the vehicle to reach target location in a certain direction, the algorithm is often unsatisfactory. Fortunately, the Bezier Curve is defined by the states of the starting point and the target point. We can use this feature to make the vehicle in the expected direction. Therefore, we propose an algorithm to combine the Bezier Curve with the VFH algorithm, to search for the collision-free states with the VFH search method, and to select the optimal trajectory point with the Bezier Curve as the reference line. This means that we will improve the cost function in the VFH algorithm by comparing the distance between candidate directions and reference line. Finally, select the closest direction to the reference line to be the optimal motion direction.

미지 환경에서 이동로봇의 주행 알고리즘

본 논문에서는 자율이동로봇이 주위 환경을 알지 못하는 상황에서 목표점까지 안전하게 주행하게 하는 주행 알고리즘에 대해 연구한다. 주행 알고리즘에서 가장 고려해야할 부분이 장애물 회피 알고리즘인데, 본 논문에서는 장애물 회피 알고리즘인 VFH(Vector Field Histogram) 알고리즘과 퍼지 알고리즘을 조합하여 여러 가지 형태의 환경에서 최적의 성능을 내도록 한다. 로봇이 처한 환경에 따라 상위 레벨의 supervisor가 위의 두 가지 장애물 회피 알고리즘 중 적절한 것을 선택하도록 조합하고, 다양한 환경에서 모의실험을 수행함으로써 제안된 로봇주행 알고리즘의 성능을 검증한다. This paper deals with problems of safe and efficient navigation algorithm for autonomous mobile robots in unknown environments. Since the obstacle avoidance algorithms are very important in mobile robot navigation, two obstacle avoidance algorithms: VFH(vector field histogram) algorithm and a fuzzy algorithm are combined to have optimal performance in various environments. And a upper-level supervisor is to select the proper one from VFH algorithm and the fuzzy algorithm according to the situations the robot faces. Computer simulation results show the effectiveness of the proposed navigation algorithm for autonomous mobile robots.

Fusion of laser and visual data for robot motion planning and collision avoidance

In this paper, a method for inferring scene structure information based on both laser and visual data is proposed. Common laser scanners employed in contemporary robotic systems provide accurate range measurements, but only in 2D slices of the environment. On the other hand, vision is capable of providing dense 3D information of the environment. The proposed fusion scheme combines the accuracy of laser sensors with the broad visual fields of cameras toward extracting accurate scene structure information. Data fusion is achieved by validating 3D structure assumptions formed according to 2D range scans of the environment, through the exploitation of visual information. The proposed methodology is applied to robot motion planning and collision avoidance tasks by using a suitably modified version of the vector field histogram algorithm. Experimental results confirm the effectiveness of the proposed methodology.