Time-optimal Point-to-point Motion Planning: Two-stage Approach

Concetti Chiave

The author proposes a two-stage approach to time-optimal point-to-point motion planning, combining fixed and variable time grids for computational manageability and avoidance of interpolation errors. The integration with an asynchronous NMPC update scheme facilitates online replanning.

Sintesi

The content discusses a two-stage approach to time-optimal point-to-point motion planning in mechanical engineering. It compares this approach with existing methods, highlighting benefits such as computational manageability and error avoidance. The integration with an NMPC update scheme is demonstrated through numerical examples.
The paper addresses the challenges of transitioning systems from initial to terminal states efficiently while satisfying constraints. It introduces a novel two-stage approach that combines fixed and variable time grids to optimize control steps and prevent interpolation errors. By integrating this method with an NMPC update strategy, the authors demonstrate effective online replanning capabilities.
Key points include:

Introduction to time-optimal motion planning in various applications.
Discussion on direct approaches versus high-level geometric path planning.
Comparison of time scaling and exponential weighting approaches.
Proposal of a two-stage approach for optimal control problem formulation.
Integration with an asynchronous NMPC update strategy for handling computation delays.
Numerical examples showcasing autonomous navigation with collision avoidance.

Statistiche

The total trajectory times for the time scaling approach and the two-stage approach are 7.0428s and 7.0439s, respectively.
The minimal horizon length for the exponential weighting approach is N ∗ = 353, leading to a total trajectory time of N ∗ts = 7.06s.

Citazioni

"The proposed two-stage approach leverages fixed and variable time grids for computational manageability."
"The integration with an asynchronous NMPC update scheme facilitates online replanning effectively."

Approfondimenti chiave tratti da

Time-optimal Point-to-point Motion Planning

by Shuhao Zhang... alle arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03573.pdf

Domande più approfondite

How can the proposed two-stage approach be adapted for real-world applications beyond autonomous navigation

The proposed two-stage approach for time-optimal motion planning can be adapted for various real-world applications beyond autonomous navigation. One potential application is in industrial automation, where precise and efficient motion planning is crucial for robotic arms in manufacturing processes. By implementing the two-stage approach, manufacturers can optimize the movement of robots between different workstations or assembly lines, reducing cycle times and improving overall productivity. Additionally, this approach could be utilized in logistics and warehouse management systems to plan optimal paths for automated guided vehicles (AGVs) navigating through complex environments efficiently. The ability to handle fluctuating computation times with the integration of ASAP-MPC makes it suitable for dynamic environments where replanning needs to happen rapidly.

What counterarguments exist against using fixed versus variable time grids in motion planning

In the context of motion planning, there are arguments both for and against using fixed versus variable time grids. One counterargument against using a fixed time grid is that it may lead to suboptimal solutions when dealing with complex systems or scenarios requiring fine-grained control adjustments at specific points along the trajectory. Variable time grids offer more flexibility in capturing intricate system dynamics accurately by adjusting the temporal resolution based on system requirements dynamically. On the other hand, a fixed time grid simplifies computational complexity by maintaining a consistent structure throughout optimization iterations, making it easier to implement and manage but potentially sacrificing precision in certain cases where finer control granularity is necessary.

How does the concept of sparsity induced by L1-norm objectives impact trajectory optimization in discrete-time systems

The concept of sparsity induced by L1-norm objectives plays a significant role in trajectory optimization within discrete-time systems. In discrete-time model predictive control (MPC), utilizing L1-norm objectives promotes sparse solutions by encouraging some components of the objective function to become exactly zero during optimization iterations. This sparsity property leads to trajectories that exhibit abrupt changes at specific points rather than gradual transitions across all states continuously. As a result, L1-norm objectives facilitate simplified representations of optimal trajectories with fewer active elements while still achieving desired performance metrics such as reaching target states efficiently or avoiding obstacles effectively within constrained environments.