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Low-Complexity Cooperative Payload Transportation for Nonholonomic Mobile Robots Under Scalable Constraints

Renhe Guan, Yuanzhe Wang, Tao Liu, Yan Wang

TL;DR

The paper tackles cooperative payload transport by nonholonomic robots under scalable constraints, aiming for real-time operation without global maps. It introduces a distributed leader-follower scheme that combines constant-time trajectory generation and constraint-preserving tracking, leveraging curvilinear follower coordinates and a multi-process control framework. Key contributions include constant-time leader trajectory generation, constant-time follower trajectory generation, and a constraint-preservation strategy with linear time complexity, validated by simulations and real-robot experiments. The approach achieves scalable constraint handling, robust obstacle avoidance, and payload protection, offering a practical, real-time alternative to centralized optimization methods like HQP or mixed-integer programming.

Abstract

Cooperative transportation, a key aspect of logistics cyber-physical systems (CPS), is typically approached using dis tributed control and optimization-based methods. The distributed control methods consume less time, but poorly handle and extend to multiple constraints. Instead, optimization-based methods handle constraints effectively, but they are usually centralized, time-consuming and thus not easily scalable to numerous robots. To overcome drawbacks of both, we propose a novel cooperative transportation method for nonholonomic mobile robots by im proving conventional formation control, which is distributed, has a low time-complexity and accommodates scalable constraints. The proposed control-based method is testified on a cable suspended payload and divided into two parts, including robot trajectory generation and trajectory tracking. Unlike most time consuming trajectory generation methods, ours can generate trajectories with only constant time-complexity, needless of global maps. As for trajectory tracking, our control-based method not only scales easily to multiple constraints as those optimization based methods, but reduces their time-complexity from poly nomial to linear. Simulations and experiments can verify the feasibility of our method.

Low-Complexity Cooperative Payload Transportation for Nonholonomic Mobile Robots Under Scalable Constraints

TL;DR

The paper tackles cooperative payload transport by nonholonomic robots under scalable constraints, aiming for real-time operation without global maps. It introduces a distributed leader-follower scheme that combines constant-time trajectory generation and constraint-preserving tracking, leveraging curvilinear follower coordinates and a multi-process control framework. Key contributions include constant-time leader trajectory generation, constant-time follower trajectory generation, and a constraint-preservation strategy with linear time complexity, validated by simulations and real-robot experiments. The approach achieves scalable constraint handling, robust obstacle avoidance, and payload protection, offering a practical, real-time alternative to centralized optimization methods like HQP or mixed-integer programming.

Abstract

Cooperative transportation, a key aspect of logistics cyber-physical systems (CPS), is typically approached using dis tributed control and optimization-based methods. The distributed control methods consume less time, but poorly handle and extend to multiple constraints. Instead, optimization-based methods handle constraints effectively, but they are usually centralized, time-consuming and thus not easily scalable to numerous robots. To overcome drawbacks of both, we propose a novel cooperative transportation method for nonholonomic mobile robots by im proving conventional formation control, which is distributed, has a low time-complexity and accommodates scalable constraints. The proposed control-based method is testified on a cable suspended payload and divided into two parts, including robot trajectory generation and trajectory tracking. Unlike most time consuming trajectory generation methods, ours can generate trajectories with only constant time-complexity, needless of global maps. As for trajectory tracking, our control-based method not only scales easily to multiple constraints as those optimization based methods, but reduces their time-complexity from poly nomial to linear. Simulations and experiments can verify the feasibility of our method.

Paper Structure

This paper contains 28 sections, 1 theorem, 14 equations, 10 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. System and Scheme Overview
  3. Structure of Cooperative Transportation System
  4. Constraints of Transportation System
  5. Payload Protection Constraints
  6. Individual Collision Avoidance Constraints
  7. System Collision Avoidance Constraints
  8. Velocity Constraints
  9. Sensor Range Constraints
  10. Other Scalable Constraints
  11. Problem Description
  12. Main Structure of Proposed Scheme
  13. Cooperative Trajectory Generation
  14. Leader Trajectory Generation
  15. Follower Trajectory Generation
  16. ...and 13 more sections

Key Result

Theorem 1

For each follower $i$ in the system, it can preserve all intra-process constraints, i.e. $\beta_i \le 1$ and inter-process constraints on velocities in Equation (eq2e), if strategies in Algorithm algorithm_3 are applied and $\beta_i\le \beta_t$ is satisfied initially.

Figures (10)

  • Figure 1: The cooperative transportation system in obstacle environments
  • Figure 2: Top view and front view of the transportation system
  • Figure 3: Flowchart of transportation framework, where yellow boxes represent robot process and red boxes are judgment conditions
  • Figure 4: The diagram of leader trajectory generation
  • Figure 5: Robot trajectories in the simulation environment, $\rho_{io}$ and $\rho_{il}$
  • ...and 5 more figures

Theorems & Definitions (2)

  • Remark 1
  • Theorem 1