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Control and Dynamics of Mobile Robots
Research Guide
What is Control and Dynamics of Mobile Robots?
Control and Dynamics of Mobile Robots is the field of engineering that develops methods for modeling, stabilizing, and directing the motion of wheeled, omnidirectional, and other nonholonomic robotic platforms under constraints like trajectory tracking and environmental disturbances.
The field encompasses 30,492 papers on techniques including adaptive control, feedback control, sliding mode control, path planning, and neural networks for robust stabilization. Nonholonomic constraints and dynamic modeling form core challenges addressed through layered architectures and higher-order methods. Growth data over the past five years is not available.
Topic Hierarchy
Research Sub-Topics
Nonholonomic Constraints in Mobile Robot Control
This sub-topic addresses kinematic and dynamic models incorporating nonholonomic constraints for wheeled mobile robots. Researchers develop control laws to handle underactuation.
Trajectory Tracking Control for Mobile Robots
This sub-topic focuses on algorithms ensuring mobile robots follow desired paths with minimal error under disturbances. Techniques include backstepping and Lyapunov-based designs.
Sliding Mode Control in Mobile Robotics
This sub-topic explores higher-order sliding mode controllers for robust stabilization and tracking in mobile robots. Studies emphasize chattering reduction and finite-time convergence.
Path Planning for Nonholonomic Mobile Robots
This sub-topic develops optimal path planners accounting for nonholonomic kinematics, using methods like RRT and lattice planning. Research integrates obstacle avoidance.
Neural Network Control for Mobile Robots
This sub-topic investigates neural networks for adaptive control, approximation of nonlinear dynamics, and learning-based navigation in mobile robots.
Why It Matters
Control and dynamics enable mobile robots to perform tasks in warehouses, search-and-rescue operations, and indoor navigation, where precise trajectory tracking and robust stabilization prevent failures under disturbances. Brooks (1986) introduced a layered control system that allows asynchronous modules to handle increasing competence levels, influencing practical deployments in autonomous vehicles. Isidori (1989) provided nonlinear control frameworks applied in thousands of robotic systems for feedback stabilization, while Mellinger and Kumar (2011) demonstrated minimum snap trajectory generation for quadrotors, achieving agile maneuvers in constrained spaces with 2194 citations reflecting its impact on drone delivery and inspection industries.
Reading Guide
Where to Start
"A robust layered control system for a mobile robot" by Rodney A. Brooks (1986), as it introduces a practical, modular architecture for building robot competence levels with simple asynchronous modules, accessible before diving into mathematical theory.
Key Papers Explained
Isidori (1989) in "Nonlinear Control Systems" lays the mathematical foundation for feedback stabilization, which Brooks (1986) applies practically in layered mobile robot control. Utkin (1992) in "Sliding Modes in Control and Optimization" and Levant (2003) in "Higher-order sliding modes, differentiation and output-feedback control" build robust methods atop Isidori's nonlinear frameworks for handling disturbances. Murray, Li, and Sastry (2017) in "A Mathematical Introduction to Robotic Manipulation" connects kinematics and dynamics to these controls, while Mellinger and Kumar (2011) exemplify trajectory methods in quadrotors.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Research continues on adaptive control and neural networks for nonholonomic constraints, with no recent preprints available. Frontiers emphasize robust stabilization and path planning extensions from classics like Fliess et al. (1995) flatness and Lyapunov (1992) stability.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Nonlinear Control Systems | 1989 | — | 7.9K | ✕ |
| 2 | A robust layered control system for a mobile robot | 1986 | IEEE Journal on Roboti... | 7.7K | ✕ |
| 3 | A Mathematical Introduction to Robotic Manipulation | 2017 | — | 6.7K | ✕ |
| 4 | Sliding Modes in Control and Optimization | 1992 | — | 6.5K | ✕ |
| 5 | Higher-order sliding modes, differentiation and output-feedbac... | 2003 | International Journal ... | 3.6K | ✕ |
| 6 | Flatness and defect of non-linear systems: introductory theory... | 1995 | International Journal ... | 3.0K | ✕ |
| 7 | Sur le mouvement d'un liquide visqueux emplissant l'espace | 1934 | Acta Mathematica | 3.0K | ✓ |
| 8 | The general problem of the stability of motion | 1992 | International Journal ... | 2.4K | ✕ |
| 9 | Minimum snap trajectory generation and control for quadrotors | 2011 | — | 2.2K | ✕ |
| 10 | Stability of Motion | 1967 | — | 2.2K | ✕ |
Frequently Asked Questions
What is a layered control system for mobile robots?
A layered control system builds competence levels with asynchronous modules communicating over low-bandwidth channels. "A robust layered control system for a mobile robot" by Rodney A. Brooks (1986) describes this architecture for operating at increasing levels of robot capability. It supports simple computational instances per module.
How does sliding mode control apply to mobile robots?
Sliding mode control enforces motion on a discontinuity set for finite-time convergence and robustness to disturbances. "Sliding Modes in Control and Optimization" by Vadim Utkin (1992) establishes this method for optimization and control tasks. Higher-order extensions in Levant (2003) enable precise output-feedback with infinite-frequency switching characteristics.
What role do nonholonomic constraints play in robot dynamics?
Nonholonomic constraints limit robot velocities, such as no sideways motion in wheeled platforms, requiring specialized trajectory tracking. The field addresses these through feedback control and path planning. Techniques like flatness in Fliess et al. (1995) linearize such systems via endogenous feedback.
How is stability analyzed in mobile robot control?
Stability of motion uses Lyapunov methods to ensure robust operation under uncertainties. "The general problem of the stability of motion" by A. M. Lyapunov (1992) provides foundational criteria. Hahn (1967) in "Stability of Motion" extends these for dynamic systems.
What is trajectory generation for quadrotor robots?
Minimum snap trajectory generation minimizes high-order derivatives for smooth paths in 3D constrained environments. "Minimum snap trajectory generation and control for quadrotors" by Daniel Mellinger and Vijay Kumar (2011) handles attitude excursions from hover. It supports quadrotor control in indoor settings.
What are flat systems in nonlinear robot control?
Flat systems are nonlinear equivalents to linear ones via endogenous feedback, subsumed by a linearizing output. "Flatness and defect of non-linear systems: introductory theory and examples" by Michel Fliess et al. (1995) defines them for controllability extensions. They apply to nonholonomic mobile robot motion.
Open Research Questions
- ? How can higher-order sliding modes be extended to compensate for unmatched uncertainties in omnidirectional mobile robots?
- ? What endogenous feedback designs achieve flatness for trajectory tracking under real-time nonholonomic constraints?
- ? How do layered architectures scale to multi-robot systems with dynamic modeling of inter-agent disturbances?
- ? Which nonlinear control frameworks minimize snap for hybrid wheeled-quadrotor platforms in cluttered environments?
- ? How can Lyapunov stability criteria be adapted for neural network-based adaptive control in uncertain robot dynamics?
Recent Trends
The field maintains 30,492 works with no specified five-year growth rate; no recent preprints or news from the last 12 months indicate steady reliance on established papers.
Classics like Brooks with 7710 citations and Isidori (1989) with 7897 citations continue dominating citations.
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