Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

Abstract

Path planning obtains the trajectory from one point to another with the robot's kinematics model and environment understanding. However, as the localization uncertainty through the odometry sensors is inevitably affected, the position of the moving path will deviate further and further compared to the original path, which leads to path drift in GPS denied environments. This article proposes a novel path planning algorithm based on Dijkstra to address such issues. By combining statistical characteristics of localization error caused by dead-reckoning, the replanned path with minimum cumulative error is generated with uniforming distribution in the searching space. The simulation verifies the effectiveness of the proposed algorithm. In a real scenario with measurement noise, the results of the proposed algorithm effectively reduce cumulative error compared to the results of the conventional planning algorithm.

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot

“…The previously best-visited position of the ith particle is denoted by P i , and the best particle in the swarm is denoted by P g . The update of the particle's position is accomplished by the following two equations: Equation ( 9) calculates a new velocity for each particle based on its previous velocity, and (10) updates each particle's position in the search space [92,95].…”

“…The work in [ 9 ] used DA to define vehicle routes on toll roads. Path planning is in a localization-insecure environment based on the Dijkstra method in [ 10 ]. Dijkstra was used to determine the shortest distance between cities on the island of Java [ 11 ].…”

Research on Path Planning with the Integration of Adaptive A-Star Algorithm and Improved Dynamic Window Approach

“…As an essential component of mobile robot automatic control, path planning algorithms have attracted a series of research studies by scholars in recent years. The commonly used global path planning algorithms for mobile robots include the Dijkstra algorithm [1,2], the A-star algorithm [3][4][5], the RRT algorithm [6][7][8], etc. The commonly used local path planning algorithms include the artificial potential field method [9,10], the Dynamic Window Approach [11], etc.…”

Obstacle avoidance motion planning for space redundant manipulator based on improved RRT algorithm

“…Currently, motion planning algorithms commonly used mainly include artificial potential field (APF) methods [2][3], search-based methods [4] [5] and sampling-based methods [6] [7]. APF adjusts the motion trajectory according to the obstacle information, which is good in real-time, but it may get trapped in local minima in complex environments.…”