Unraveling the Truth Behind Robot chassis Failures

In today's era of increasingly widespread intelligent robots, whether in logistics delivery, security patrols, agricultural operations, or specialized rescue missions, the stability of robotic chassis—as the core mobile platform—directly impacts project success and personnel safety. Yet in reality, robot “rollover” incidents remain all too common—cases of slope overturns, loss of control during sharp turns, and sudden tip-overs on flat surfaces continue to raise industry concerns.

As a robot chassis supplier, our extensive accident analysis reveals that center-of-gravity design failures are the primary cause of accidents.

Customers adding their own equipment—such as top-mounted gimbals, side-mounted battery packs, or robotic arms—can easily disrupt the original mechanical balance. Overweight upper equipment raises the overall center of gravity, while imbalanced layouts cause the center of gravity to shift horizontally. This is not merely an issue of “excess weight,” but systemic failure: When the robot navigates turns, slopes, or uneven terrain, the displaced center of gravity combines with centrifugal force/gravity, causing the static stability angle (SSA) to rapidly diminish and instantly breach the tipping threshold. More dangerously, equipment sliding on slopes may trigger chain reactions of loss of control, transforming static imbalance into irreversible safety incidents.

Inadequate terrain and environmental adaptability constitute the second major risk.

In our market research report, over 70% of users have experienced operational interruptions in real-world scenarios due to chassis adaptability flaws: waterlogged floors in logistics warehouses, muddy slopes in agricultural fields, and gravel obstacles during emergency rescues. Yet many customers initially prioritize cost-effectiveness, opting for chassis with limited perception capabilities, rigid drive control algorithms, and overly rigid mechanical structures. When unexpected obstacles or terrain changes occur, the system fails to respond promptly to environmental shifts, ultimately causing slippage, overturns, or getting stuck.

Defects in sensor fusion create perception blind spots.

LiDAR is highly susceptible to interference in rainy or foggy conditions, and in severe cases, it cannot accurately identify obstacle contours. Cameras lose detail under intense light conditions (such as direct midday sunlight or high-intensity warehouse lighting) due to lens flare and sensor overexposure. Conversely, in low-light environments (like nighttime or dim corners), a sharp drop in signal-to-noise ratio produces blurred images, preventing visual algorithms from reliably detecting path boundaries or moving objects; Calibration errors in IMUs (inertial measurement units) stem from temperature drift or mechanical vibration. Their accumulated angular velocity and acceleration errors cause the robot's attitude estimation to gradually deviate from its actual state—for example, misjudging its tilt angle during turns. Ultrasonic sensors are highly sensitive to sound-absorbing materials (e.g., foam packaging, carpets, or soft obstacles). When acoustic energy is absorbed by such materials, it fails to reflect effectively, resulting in reduced detection range or complete failure.

When these sensors are simultaneously exposed to their respective weaknesses in critical operational scenarios—such as outdoor inspections in rainy weather or high-intensity lighting conditions during peak logistics warehouse hours—the failure of a single sensor is amplified by the absence of cross-validation. This ultimately leads to the collapse of the robotic system's comprehensive environmental judgment capabilities —it may misinterpret rain or fog as physical obstacles and abruptly halt, or overlook actual obstacles in low light and collide at high speed, completely losing the environmental perception redundancy essential for safe operation.

A typical example is during peak logistics periods: when dust obscures or fallen cardboard boxes cover the LiDAR sensors, the system may misjudge the area as “obstacle-free” and collide with personnel at high speed, or suddenly brake to a halt, causing production line paralysis.

The frequent occurrence of robot “crashes” is no accident, but rather the combined result of three systemic vulnerabilities: center-of-gravity imbalance, environmental adaptability flaws, and sensor fusion failures. As a chassis supplier, we understand that stability is the lifeline for robot deployment—it must extend beyond laboratory parameters and withstand rigorous real-world tests against mud, slopes, and unexpected obstacles.