The "locomotion genes" of a mobile robot lie deep within its chassis architecture-whether threading through rack-dense warehouse aisles or trudging over muddy, rugged open-pit terrain, the choice of chassis drive model directly defines the robot's motion envelope. From the minimalism of two-wheel differential drive to the precision control of AGV drive wheels, from the brute obstacle-conquering power of tracks to the graceful translation of omnidirectional wheels, each chassis type reflects a compromise among mechanical design, control algorithms, and scene requirements. Anchored in technical principles and illustrated with industry case studies, this article systematically breaks down the performance characteristics and adaptation logic of mainstream chassis architectures, providing decision-makers and developers with a clear reference.
I. Two-Wheel Differential Chassis: The Bedrock of Low-Cost Indoor Navigation
The two-wheel differential model relies on independent speed control of the left and right wheels. Using the differential equations
(v=VL+VR2v=2VL+VR, ω=VR−VLlω=lVR−VL)
it achieves steering without a mechanical steering mechanism. Its structural simplicity and low cost make it a go-to for indoor service robots. For example, the Ecovacs DEEBOT X2 vacuum cleaner employs two-wheel differential drive plus a passive caster wheel, allowing flexible turns in spaces as low as 8 cm. However, its under-constrained nature (no lateral movement) complicates path planning, and odometry drift must be compensated via LiDAR-SLAM or visual-inertial fusion. Recent advances-like dynamic torque distribution algorithms and optimized unsprung-mass designs-have significantly improved anti-slip performance on tiled or carpeted floors.
II. Four-Wheel Differential Chassis: The "Mechanical Beast" for Rough Terrain
A four-wheel independent-drive model uses distributed motor control to achieve formidable terrain adaptability. Take Clearpath Robotics' Husky unmanned ground vehicle: each hub carries an electric motor capable of 1,200 Nm peak torque, paired with a central differential lock and adjustable suspension to climb 40° slopes and traverse 25 cm trenches. Since steering is achieved by differential wheel speeds (no mechanical steering linkage), mechanical losses are reduced-but control algorithms must solve four‐wheel speeds and steering kinematics in real time to prevent slip-induced trajectory errors. In agriculture, John Deere's autonomous tractor leverages four-wheel differential drive with RTK GNSS positioning to achieve centimeter-level accuracy between crop rows.
III. Ackermann Chassis: The "Traditional Innovator" for High-Speed Scenarios
Based on conventional automotive steering geometry, the Ackermann model uses a larger inner-wheel steering angle than the outer wheel to minimize lateral slip, with rear-wheel drive delivering smooth propulsion. TuSimple's self-driving trucks adopt optimized Ackermann geometry to achieve a 15 m turning radius at 80 km/h. Recent evolutions-steer-by-wire (SBW) integrated with rear-wheel active steering (RWS)-are key: Mercedes-Benz eActros trucks use a 5° rear steering angle to shrink turning radius by 20%, crucial for tight loading-dock maneuvers. Yet, like all non-holonomic systems, true lateral translation is still absent and must be addressed in higher-level path planning.
IV. Mecanum-Wheel Chassis: The "Omni-Ghost" of Tight Spaces
By mounting 45° rollers around each wheel, a Mecanum chassis achieves full planar omnidirectional motion-each wheel must spin forward or backward in paired patterns. KUKA's OmniMove AGV uses this to lift multi-ton aircraft parts and position them with 0.1 mm precision in assembly halls. However, roller wear is a significant issue: after 2,000 h of continuous operation, positioning errors can exceed 3 mm, necessitating regular calibration and online friction-coefficient estimation.
V. Omnidirectional-Wheel Chassis: The "Dust-Free Dancer" in Precision Settings
True omnidirectional wheels mount rollers perpendicular to the wheel hub, fully decoupling X/Y motion. Nikon's semiconductor-factory material-handling robots use a three-wheel, 120° layout in cleanrooms to perform vibration-free lateral translations that protect wafers. Compared to Mecanum, payload capacity is lower (typically < 500 kg) but ground-flatness tolerance is higher. At the control level, inverse-kinematics matrices must be solved to synthesize precise wheel‐speed vectors, placing high demands on onboard compute.
VI. AGV Drive-Wheel Chassis: The "All-Round Performer" in Industrial Logistics
An AGV drive wheel integrates drive and steering into a single module, allowing each wheel's angle and speed to be controlled independently for holonomic motion:
Four AGV drive wheels: Siasun's HCR series supports zero-radius turning and lateral drifting, ideal for high-dynamic line-side delivery in automotive plants.
Dual AGV drive wheels: Geek+'s P800 robot achieves 10 cm positioning accuracy in 3.5 m-wide aisles, at 40% lower cost than a four-wheel system.
Single AGV drive wheel: Hikvision's MV series counterbalanced forklift uses a "crank-link" design to maintain traction on uneven floors.
Current trends focus on ultra-slim and high-power-density modules-for example, Jiateng's V-shaped drive wheel is only 85 mm thick yet handles 1.2 t loads.
VII. Tracked Chassis: The "Survival Expert" for Extreme Terrain
Steel or rubber tracks spread ground pressure over large areas, excelling in swamps, snow, and sand. Russia's Uran-6 demining robot uses an adjustable track-tensioner to adapt to gravel or mud, keeping slip rates below 5%. However, track steering consumes three times more energy than straight-line motion, and indoor ground surfaces risk damage. Hybrid chassis (track + wheels) offer a compromise: China Electronics Technology Group's "Qilin" robot switches via hydraulics between highway speeds and off-road obstacle conquering.
Conclusion
From the simplicity of two-wheel differential to the precision of multi-AGV drive-wheel holonomics, mobile-robot chassis have evolved into intelligent bodies fusing mechanics, electronics, and AI. Developers must transcend mere specs and judge fit by scenario: whether the need is millimeter-level lateral precision or kilometer-scale endurance; rugged impact resistance or maximum spatial efficiency. Only by matching chassis architecture to operational logic can a robot's full potential be unleashed. And while the "one-chassis-fits-all" concept may arrive with future material and drive breakthroughs, today's mastery demands knowing each solution's boundaries.
