Omnidirectional Forklift Trajectory Planning With Spline Speed Profiles
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Solution Overview
Problem
Conventional holonomic industrial trucks lack efficient kinodynamic movement planning, resulting in suboptimal path execution due to ignoring mass, braking, and acceleration aspects, leading to excessive travel time and inability to follow planned paths at higher speeds.
Innovation Solution
A method for a holonomic industrial truck that models paths using splines and assigns speed profiles, iteratively optimizing trajectories based on boundary conditions such as maximum speed, acceleration, and obstacle proximity, allowing for dynamic movement planning and continuous optimization to ensure safe and efficient path execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional holonomic forklifts use simple control devices without considering kinodynamic factors, then the control system is simple, but the vehicle cannot follow planned paths at higher speeds due to excessive speed and ignoring mass, braking, and acceleration distances
Solution Approach 1:
The control device dynamically adjusts speed profiles and trajectories by considering kinodynamic factors including vehicle mass, braking distances, and acceleration capabilities. The system continuously recalculates optimal paths and speed profiles during operation to maintain both high speed and accurate path following.
Solution Approach 2:
The control device uses feedback from sensors and vehicle state information to continuously monitor actual position, speed, and acceleration. This feedback is used to adjust the speed profile and trajectory in real-time, ensuring the vehicle follows the planned path accurately while operating at maximum safe speeds.
2Reliability
If conventional holonomic forklifts choose a maximum speed significantly below physical limits to fulfill all paths, then path following is reliable, but the vehicle requires considerably more time for movement
Solution Approach 1:
The system optimizes multiple parameters simultaneously including speed profile, acceleration rates, and trajectory curvature to find the optimal balance between speed and path following accuracy. By dynamically adjusting these parameters based on vehicle capabilities and path requirements, the system achieves faster travel times while maintaining reliable path following.
Solution Approach 2:
The control device dynamically optimizes the speed profile along the trajectory by considering the vehicle's mass, braking capabilities, and acceleration distances. This dynamic optimization allows the vehicle to travel at higher speeds than conventional systems while still reliably following the planned path, significantly reducing travel time.
3Ease of operation
If conventional forklifts use sequential sequences of circular paths, translations, and rotations, then the movement is simple to control, but the travel time is excessive and path efficiency is low
Solution Approach 1:
The control device segments the overall trajectory into multiple spline curves with control points, allowing complex omnidirectional movements to be broken down into manageable segments. Each segment can be independently optimized for speed and efficiency while maintaining overall path accuracy, improving both productivity and control capability.
Solution Approach 2:
The system replaces sequential circular paths and sharp transitions with smooth spline curves that provide continuous curvature. This allows the vehicle to maintain higher speeds through curved sections while following the desired path accurately, significantly improving travel efficiency without sacrificing control simplicity.
Data Source
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Figure 2
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AI summary
The method involves determining a path for movement of a holonomic/omnidirectional fork-lift truck (1) from a starting point to a terminal point. The determined path is modeled by bezier splines of fifth order. A trajectory for movement of the fork-lift truck along the path is determined with a determined velocity profile based on the modeled path. Another trajectory is determined based on the former trajectory and an iterative optimization prescription. The fork-lift truck is moved along the latter trajectory by a control device (8) of the fork-lift truck. An independent claim is also included for a holonomic/omnidirectional fork-lift truck.