Emergency path planning for autonomous vehicles using elastic band theory

2004 ◽  
Author(s):  
J. Hilgert ◽  
K. Hirsch ◽  
T. Bertram ◽  
M. Hiller
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Elastic Band ◽  
Band Theory

scholarly journals Smooth Path Planning for Robot Docking in Unknown Environment with Obstacles

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Peng Cui ◽  
Weisheng Yan ◽  
Rongxin Cui ◽  
Jiahui Yu
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Integrated Approach ◽  
Tree Structure ◽  
Elastic Band ◽  
Band Theory ◽  
Smooth Path ◽  
Feasibility Evaluation ◽  
Simulation Results ◽  
Feasible Path

This paper presents an integrated approach to plan smooth path for robots docking in unknown environments with obstacles. To determine the smooth collision-free path in obstacle environment, a tree structure with heuristic expanding strategy is designed as the foundation of path planning in this approach. The tree employs 3D Dubins curves as its branches and foundation for path feasibility evaluation. For the efficiency of the tree expanding in obstacle environment, intermediate nodes and collision-free branches are determined inspired by the elastic band theory. A feasible path is chosen as the shortest series of branches that connects to the docking station after the sufficient expansion of the tree. Simulation results are presented to show the validity and feasibility of the proposed approach.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment

2021 ◽  
Vol 11 (11) ◽  
pp. 5057
Author(s):  
Wan-Yu Yu ◽  
Xiao-Qiang Huang ◽  
Hung-Yi Luo ◽  
Von-Wun Soo ◽  
Yung-Lung Lee
Keyword(s):  
Path Planning ◽  
Task Allocation ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamic Task ◽  
The Core ◽  
Dynamic Task Allocation ◽  
Vehicle Technology ◽  
Future Work ◽  
Complicated Task

The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.

An A* based path planning approach for autonomous vehicles

2021 ◽  
Author(s):  
Karthikeyan Mayilvaganam ◽  
Anmol Shrivastava ◽  
Prabhu Rajagopal
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Planning Approach

scholarly journals Diversity Maintenance for Efficient Robot Path Planning

2020 ◽  
Vol 10 (5) ◽  
pp. 1721
Author(s):  
Petar Ćurković ◽  
Lovro Čehulić
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Degrees Of Freedom ◽  
Population Based ◽  
Difficult Problem ◽  
Fitness Sharing ◽  
Diversity Maintenance ◽  
Novelty Search ◽  
High Level ◽  
Path Planner

Path planning is present in many areas, such as robotics, video games, and unmanned autonomous vehicles. In the case of robots, it is a primary low-level prerequisite for the successful execution of high-level tasks. It is a known and difficult problem to solve, especially in terms of finding optimal paths for robots working in complex environments. Recently, population-based methods for multi-objective optimization, i.e., swarm and evolutionary algorithms successfully perform on different path planning problems. Knowing the nature of the problem is hard for optimization algorithms, it is expected that population-based algorithms might benefit from some kind of diversity maintenance implementation. However, advantages and potential traps of implementing specific diversity maintenance methods into the evolutionary path planner have not been clearly spelled out and experimentally demonstrated. In this paper, we fill this gap and compare three diversity maintenance methods and their impact on the evolutionary planner for problems of different complexity. Crowding, fitness sharing, and novelty search are tailored to fit specific problems, implemented, and tested for two scenarios: mobile robot operating in a 2D maze, and 3 degrees of freedom (DOF) robot operating in a 3D environment including obstacles. Results indicate that the novelty search outperforms the other two methods for problem domains of higher complexity.

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