Driving Innovation in Legged Robotics: How SciChart Powered MIT’s Force Sensor Breakthroughs, from Cheetah to Spot

The field of legged robotics is advancing rapidly, driven by innovations like MIT’s Cheetah, known for its high-speed agility, and Boston Dynamics’ Spot, a robot designed for real-world applications.

Designed to make robotic animals walk, run and traverse various terrains has not been an easy journey, but pioneering strides have been made. By pushing the hardware to its limits, MIT’s robotic cheetah has since broken the record for the fastest run recorded.

Central to these advancements is the development of multi-axis force sensors, which provide precise measurements of ground reaction forces.

These sensors, a product of MIT’s Biomimetic Robotics Lab, were visualized, calibrated, and optimized using SciChart—an industry-leading cross-platform charting library for high-performance telemetry dashboards and data visualization.

SciChart’s Role in Force Sensor Development

MIT’s Biomimetic Robotics Lab faced a critical challenge: designing a lightweight yet highly accurate force sensor to capture dynamic normal and shear forces during high-speed locomotion of their robotic animals.

The team developed a bio-inspired footpad sensor, embedding barometric pressure sensors into a polymeric layer. These detect changes in altitude and atmospheric pressure, supporting informed decisions to support consistent motion in various weather conditions. An example of where the robot would need to adapt is encountering an icy surface where there’s a risk of slipping. Quick detection of terrain changes will help robotic animals confidently navigate a broader range of environments.

This innovative design achieved:

• High Precision: Accurately measuring forces up to 300N vertically and ±80N horizontally.
• Robust Data Capture: Capturing ground reaction forces in real-time during rapid locomotion.

The volume and complexity of data required advanced visualization tools. SciChart’s Android charting library was up for the challenge.

With the ability to process millions of data points in real-time without slowdown—even on lower-grade hardware—it enabled researchers to analyze multi-axis sensor outputs with unparalleled speed and accuracy. This ultimately helped validate and optimize sensor performance.

From Data Streams to Actionable Insights

SciChart played a pivotal role in translating raw telemetry into actionable insights. The high-performance Android charting library empowered MIT researchers to:

• Visualize Real-Time Interactions: Monitor dynamic force responses during high-speed locomotion with precision.
• Optimize Sensor Design: Analyze footpad deformation under varying forces to refine sensor geometry.
• Enhance Calibration Workflows: Integrate seamlessly with neural networks for improved calibration and actionable force predictions.

This integration of advanced data visualization into sensor development workflows was instrumental in achieving high-performance robotics solutions.

Visualizing Multi-Axis Force Data

SciChart’s advanced telemetry dashboards allowed researchers to interact with high-frequency force data in real-time.

These intuitive visualizations helped:

• Map pressure distributions across footpads for enhanced design iterations.
• Analyze complex force dynamics during locomotion on varied terrains.
• Streamline validation processes for neural network-based calibration models.

By transforming dense telemetry data streams into actionable visuals, SciChart supported rapid decision-making, critical for advancing MIT’s robotics projects.

Bridging the Gap: From Cheetah to Spot

No longer the work of science fiction, the technologies pioneered in MIT’s Cheetah project laid the groundwork for broader advancements in legged robotics. This included Boston Dynamics’ Spot, a robot capable of traversing complex, real-world environments.

While Spot emerged from a separate lineage, the force-sensing principles and telemetry data visualization methodologies refined through SciChart are central to its success.

Spot has multiple use cases across sectors to improve health and safety and hazard investigations, including effortlessly navigating construction sites, factory floors and research labs to detect and monitor hazards.

Instead of sending your team to inspect potentially hazardous conditions, Spot can investigate on their behalf. With automated sensing, athletic intelligence, unlimited data collection capacities, and 360° perception, this agile robot is robust up to the task.

Spot can patrol a facility along a planned route to detect issues that could otherwise go unnoticed, either due to human error or lack of human resource.

Spot has already become part of the team at the National Grid, supporting employee safety and equipment uptime to help power the nation.

Advance the Future of Robotics with SciChart

From experimental platforms like Cheetah to real-world robotics like Spot, SciChart has transformed how researchers approach complex data visualization.

By enabling precise insights into sensor performance and calibration, SciChart empowers the robotics industry to push the boundaries of innovation. As legged robotics continues to evolve, SciChart remains at the forefront, driving the next generation of agile, adaptive systems with its 64-bit chart library.

SciChart’s contributions to MIT’s force sensor breakthroughs demonstrate its unparalleled value in high-performance telemetry data visualization. From Formula 1 to legged robotics, SciChart enables transformative innovations, ensuring researchers have the tools they need to achieve new heights in precision and performance.

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