Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles

Abstract

With the continuous development of robotics and artificial intelligence, robots are being increasingly used in various applications. For traditional navigation algorithms, such as Dijkstra and A , many dynamic scenarios in life are difficult to cope with. To solve the navigation problem of complex dynamic scenes, we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present. The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output. The algorithm enhances the robots' ability to actively avoid obstacles while retaining the adaptability of traditional methods.

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

“…This is based on the principle of probability filtering and uses the laser sensor data to construct an environment map and estimate the robot's position in real time [17]. By dividing the map into grids, Gmapping can efficiently deal with large-scale environments, and because of the use of particle filtering technology, it also performs well in nonlinear and dynamic environments, making it an important tool in the field of autonomous navigation and map building [18]. At the same time, for the particle dissipation problem, an adaptive resampling strategy is put forward to improve computational efficiency.…”

“…This transformation relationship between coordinate systems is of great significance in the field of image processing and computer vision, and it is used to accurately measure and analyze the position and size information in the image. If the homogeneous coordinate of a point in space in the world coordinate system is , and the homogeneous coordinate in the camera coordinate system is , according to the standard transformation relationship in the field of image processing and computer vision, the transformation between the pixel coordinate system and the world coordinate system can be expressed as (18) In the formula, , represents the equivalent focal length. is the focal length; and are the physical sizes of each pixel in the x-axis and y-axis directions of the imaging coordinate system, respectively.…”

Path Planning Techniques for Real-Time Multi-Robot Systems: A Systematic Review

“…Different methodologies have been employed alongside cell decomposition algorithms, including the Dijkstra algorithm and the Simulated Annealing (SA) approach. In [61], A* and potential field [67], A* and reinforcement learning [63], A* and the Dynamic Window Algorithm [66], Theta* and dipole field with the dynamic window approach [64] are explored. Other studies concentrated on merging various optimization strategies to address the complexities in path planning, for instance, integrating the Wolf Swarm Algorithm with the artificial potential field (WSA-APF) [62], the kidneyinspired algorithm and Sine-Cosine Algorithm (KA-SCA) [70], Artificial Bee Colony and Evolutionary Programming (ABC-EP) [71], the Modified Hyperbolic Gravitational Search Algorithm and Dynamic Window Approach (MGSA-DWA) [72], the Self-Organizing Migrating Algorithm and Particle Swarm Optimization (SOMA-PSO) [73], the Grey Wolf Optimizer and Whale Optimizer Algorithm (GWO-WOA) [69], and the Dynamic Window Approach (DWA) and Teaching-Learning-Based Optimization (TLBO) [68].…”

An Autonomous Navigation Strategy Based on Improved Hector SLAM With Dynamic Weighted A* Algorithm

“…In [33], the search efficiency of the A * algorithm was not only increased with the search nodes number changing, but also the paths were smoothed. In [34], The Gmapping algorithm was carried out to construct the environmental map and the navigation tasks were completed by using the A * algorithm. Li and Li [35] successfully completed the navigation in the dense orchard environment using the improved A * algorithm.…”

“…According to equation (34), the linear Bezier curve defined by two points does not include a slope factor, forming a line segment where the start and end points are specified by the first and last points. The linear Bezier curve can be specified in the form of P (t) = (x (t) , y (t)) as follow:…”