Piecewise linear continuous-curvature path planning for autonomous vehicles

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.

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

Applied Sciences ◽

10.3390/app11115057 ◽

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.

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On the Distributed Path Planning of Multiple Autonomous Vehicles Under Uncertainty Based on Model-Predictive Control and Convex Optimization

IEEE Systems Journal ◽

10.1109/jsyst.2020.3007795 ◽

2020 ◽

pp. 1-10

Author(s):

Seyed Mohsen Ahmadi Mousavi ◽

Behzad Moshiri ◽

Zainabolhoda Heshmati

Keyword(s):

Convex Optimization ◽

Path Planning ◽

Model Predictive Control ◽

Predictive Control ◽

Autonomous Vehicles

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A Computationally Efficient Predictive Controller for Lane Keeping of Semi-Autonomous Vehicles

Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems ◽

10.1115/dscc2014-6098 ◽

2014 ◽

Cited By ~ 3

Author(s):

Changchun Liu ◽

Chankyu Lee ◽

Andreas Hansen ◽

J. Karl Hedrick ◽

Jieyun Ding

Keyword(s):

Autonomous Vehicles ◽

Piecewise Linear ◽

Computational Cost ◽

Parametric Programming ◽

Linear Feedback ◽

Loop Model ◽

Computationally Efficient ◽

Feedback Function ◽

Predictive Controller ◽

Lane Keeping

Model predictive control (MPC) is a popular technique for the development of active safety systems. However, its high computational cost prevents it from being implemented on lower-cost hardware. This paper presents a computationally efficient predictive controller for lane keeping assistance systems. The controller shares control with the driver, and applies a correction steering when there is a potential lane departure. Using the explicit feedback MPC, a multi-parametric nonlinear programming problem with a human-in-the-loop model and safety constraints is formulated. The cost function is chosen as the difference between the linear state feedback function to be determined and the resultant optimal control sequence of the MPC problem solved off-line given the current state. The piecewise linear feedback function is obtained by solving the parametric programming with an approximation approach. The effectiveness of the controller is evaluated through numerical simulations.

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Path Planning of Multiple UAV’s in an Environment of Restricted Regions

Dynamic Systems and Control, Parts A and B ◽

10.1115/imece2005-79682 ◽

2005 ◽

Author(s):

Madhavan Shanmugavel ◽

Antonios Tsourdos ◽

Rafal Zbikowski ◽

Brian White

Keyword(s):

Path Planning ◽

Path Length ◽

Mathematical Property ◽

Multiple Uavs ◽

Safety Margins ◽

Pythagorean Hodograph ◽

Offset Curves ◽

Safety Constraints ◽

Continuous Curvature ◽

Ph Curve

This paper describes a novel idea of path planning for multiple UAVs (Unmanned Aerial Vehicles). The path planning ensures safe and simultaneous arrival of the UAVs to the target while meeting curvature and safety constraints. Pythagorean Hodograph (PH) curve is used for path planning. The PH curve provides continuous curvature of the paths. The offset curves of the PH paths define safety margins around and along each flight path. The simultaneous arrival is satisfied by generation of paths of equal lengths. This paper highlights the mathematical property — changing path-shape and path-length by manipulating the curvature and utilises this to achieve the following constraints: (i) Generation of paths of equal length, (ii) Achieving maximum bound on curvature, and, (iii) Meeting the safety constraints by offset paths.

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