What type of path planning approach is the Vector field histogram?
Best insight from top research papers
The Vector Field Histogram (VFH) is a real-time path planning method for mobile robots that uses sensors to detect obstacles and navigate around them while moving towards a target . It constructs a vector field histogram based on observed obstacles and uses an objective function to determine the optimal sector for navigation . The VFH method is effective in avoiding obstacles and enables safe and efficient motion towards the destination . It has been implemented and evaluated for its robustness and performance in simulations and real-world experiments . The VFH method is suitable for mobile robots with low computational cost, as it has a fast computing time . Overall, the VFH method is a successful approach for obstacle avoidance and path planning in various environments .
Answers from top 4 papers
More filters
| Papers (4) | Insight |
|---|---|
| The Vector Field Histogram (VFH) is a real-time motion planning method for mobile robots. | |
23 Apr 2014 7 Citations | The Vector Field Histogram (VFH) is a real-time path planning method used for obstacle avoidance in autonomous search-and-rescue robots. |
15 Jul 2021 | The paper proposes a fuzzy planner using a vector field histogram in polar coordinates for robot navigation in dense crowds. |
| The paper does not explicitly mention the type of path planning approach used by the Vector Field Histogram (VFH) algorithm. |
Related Questions
What is the difference between PRM and RRT path planning?5 answersThe key difference between Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT) path planning lies in their approach to path generation. PRM focuses on capturing the environment's connectivity efficiently, reducing dataset generation time significantly. In contrast, RRT rapidly explores the configuration space by generating feasible paths without considering planning costs. PRM leverages neural networks to guide exploration, resulting in faster planning than traditional RRT. On the other hand, RRT methods like RRT-Connect and pRRT aim to reduce exploration time by forming multiple trees and introducing goal biasing for faster convergence. The fusion of RRT with A* algorithm in RRT-A* enhances path planning quality by considering path cost and reducing the number of nodes in the generated paths.
How does Vector Field Histogram path planning work?5 answersVector Field Histogram (VFH) path planning works by dividing the robot's surroundings into sectors and creating a histogram of obstacles in polar coordinates. The method aims to steer the robot around obstacles while moving towards a target point. Various improvements have been proposed to enhance VFH, such as incorporating Bezier Curves to guide the robot in the desired direction. Additionally, a closest distance VFH method has been developed, where obstacle avoidance regions are determined based on barrier distances from the robot's central point. These methods utilize different strategies like fuzzy inference systems, A* algorithms, and optimal trajectory point selection to ensure safe and efficient robot navigation in diverse environments.
What is the best path planning algorithm for a robot?5 answersThe best path planning algorithm for a robot depends on the specific requirements and constraints of the task. Several algorithms have been proposed in the literature. One approach is the RRT*FN algorithm, which optimizes memory and introduces a heuristic sampling strategy to improve search efficiency. Another option is the RRT_Connect algorithm, which optimizes the searched nodes and planned paths, reducing search iterations and nodes. Additionally, the roadmap approach, cell decomposition, and artificial potential fields are classical approaches that can be used in various environmental situations. Reinforcement learning algorithms, such as the improved reinforcement learning algorithm, can also be effective in path planning tasks, increasing convergence speed and stability. Finally, the improved genetic algorithm addresses issues of path smoothness, number of turns, and local optimal solutions, making it more suitable for robot movement.
What are the different methods for path planning for data harvesting with UAVs?5 answersDifferent methods for path planning for data harvesting with UAVs have been proposed in the literature. One approach involves using reinforcement learning (RL) algorithms to learn optimal trajectories for UAVs in the presence of jamming attacks. Another method is to formulate the path planning problem as an integer linear programming (ILP) problem and use algorithms such as genetic algorithms to find optimal paths while considering obstacles and flight time constraints. Particle swarm optimization (PSO) has also been used to evaluate optimal flight paths for UAVs in the presence of realistic ocean models and various constraints. Additionally, state-of-the-art RL methods have been leveraged to design flight trajectories for multiple energy-constrained UAVs to improve information freshness in data collection. These methods provide different approaches to address the path planning problem for data harvesting with UAVs.
Is Neural network used in path planning?3 answersNeural networks are used in path planning for mobile robots. These networks are trained to navigate robots among static and moving obstacles, using potential fields for obstacle avoidance. They can process inputs such as distance sensor readings, relative position from the target, environmental data, and navigation data to generate output scores representing possible locations in the vicinity of the vehicle. Neural networks can also be used for traversability estimation in challenging terrain conditions, by fusing depth images and roll and pitch measurements. The trained networks can provide reliable traversability estimates and be used in incremental path planning. Additionally, neural networks in path planning algorithms can adapt to dynamic environments, generating continuous, smooth, and optimal paths that respond quickly to fast-changing conditions.
What are the advanced path planning algorithms?5 answersAdvanced path planning algorithms include Dijkstra, A*, A*/w Bounding Box, Jump Point Search (JPS), Simple Subgoal (SS), and RRT*-FDWA. These algorithms have been developed to improve the speed and efficiency of path planning in various applications such as AI, robotics, and computer games. They offer advantages such as faster solution times, smoother paths, and the ability to handle dynamic environments. RRT* is a leading solution in path planning and has been enhanced through the utilization of vertex-based networks, resulting in more efficient path planning with significant speed improvements. Additionally, an improved RRT algorithm has been proposed for generating paths in large-scale complex environments, achieving faster convergence speed, shorter search paths, and better smoothness.