Collision-Free Platooning Control of Mobile Robots with Set-Theoretic Predictive Control

Core Concepts

Proposing a control solution for collision-free platooning of mobile robots using set-theoretic predictive control.

Abstract

The paper introduces a control solution for achieving collision-free platooning control of mobile robots. It focuses on leader-follower dynamics, feedback linearization, and model predictive control to ensure trajectory tracking and collision avoidance. The proposed strategy is validated through experiments with Khepera IV differential drive robots. Key concepts include input constraints, feedback linearization, set-theoretic reachability arguments, and trajectory tracking.

Stats

Maximum angular velocity: Ω = 1200[steps/sec] Sampling time: Ts = 0.15 Wheel radius: R = 0.021[m] Axis length: D = 0.1047[m]

Quotes

"Model Predictive Control has been successfully applied to the control of vehicular platoons." "Feedback Linearization is a well-established linearization technique capable of transforming nonlinear models into equivalent linear ones."

Key Insights Distilled From

Collision-Free Platooning of Mobile Robots through a Set-Theoretic Predictive Control Approach

by Suryaprakash... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.08942.pdf

Deeper Inquiries

How does the proposed strategy compare to existing solutions in terms of computational complexity

The proposed strategy in the context provided combines feedback linearization and set-theoretic Model Predictive Control (MPC) to address the platooning control problem for mobile robots. In terms of computational complexity, the proposed solution offers advantages over existing solutions. By utilizing a simplified linearized model through feedback linearization, the system's dynamics are transformed into an equivalent linear form, reducing the computational burden compared to nonlinear formulations. Additionally, by incorporating Set-Theoretic MPC, stability requirements, tracking performance, and input constraints can be directly integrated into the control design while maintaining computational efficiency.

What are the implications of assuming bounded reference trajectories for real-world applications

Assuming bounded reference trajectories in real-world applications has significant implications for system performance and safety. Bounded reference trajectories ensure that planned paths do not exceed predefined limits or boundaries during operation. This constraint is crucial for avoiding collisions between agents in a platooning formation and ensuring safe navigation within confined spaces or dynamic environments. However, it also imposes restrictions on maneuverability and adaptability to unforeseen obstacles or changes in operating conditions. Real-world implementations must carefully balance the benefits of bounded trajectories with flexibility requirements to handle diverse scenarios effectively.

How can the concept of set-theoretic reachability be extended to other robotic systems beyond platooning

The concept of set-theoretic reachability demonstrated in platooning systems can be extended to various other robotic systems beyond just vehicle formations. By leveraging sets such as Robust One Step Controllable Sets (ROSC) and Robust One Step Reachable Sets (ROSR), controllers can ensure robustness against disturbances while guaranteeing trajectory tracking within specified bounds. This approach can be applied to multi-agent coordination tasks like swarm robotics, cooperative manipulation tasks involving multiple robotic arms or drones working together collaboratively while maintaining safety distances from each other.