Solutions for Industry
Creating innovative solutions for Industry
Squishy Robotics’ solutions can help improve your company’s ROI. Industries and municipalities are quickly adopting Smart Cities and Industrial Internet of Things (IIoT) technologies to improve safety, efficiency, and productivity. Squishy Robotics can help integrate artificial intelligence (AI) and other machine learning that can help teams increase operational effectiveness, prioritize tasks and maintenance processes, and lead to better problem-solving solutions for manufacturing, transportation, petrochemical, and agriculture.
Optimal Placement of Rapidly Deployable Mobile Sensor Robots
Modern inspection and emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) as remote sensors to provide rapid improved situational awareness. Ground-based sensors are an integral component of overall situational awareness platforms, as they can provide longer-term persistent monitoring that aerial drones are unable to provide. Squishy Robotics provides an integrated aerial/ground solution for inspection, diagnostics and prognostics.
The Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants. Yet there will be gaps in coverage due to broken sensors or sparce density in very large plants, such as in the petrochemical industry. Critical decisions depend on these sensors, but what should be done if there are anomalous readings or an indication of a hazardous situation that might require shutting down the plant or evacuating the local community? Should they put people in danger to take more accurate or localized sensor readings?
Squishy Robotics optimizes the deployment of emergency sensors spatially over time. AI techniques (e.g., Long Short-Term Memory neural networks, Random forests) identify regions where sensors would be most valued without requiring humans to enter the potentially dangerous area. The cost function for optimization considers costs of false-positive and false-negative errors. Expected Value of Information (EVI) is used to identify the most valuable type and location of physical sensors to be deployed to increase the decision-analytic value of a sensor network. This case study using data from the Tennessee Eastman process dataset of a chemical plant displayed in OSI Soft or Blueforce interfaces.
HazMat Training with the LACoFD
Squishy Robotics engineers joined the Los Angeles County Fire Department during a hazardous materials (HazMat) training scenario at the Del Valle Regional Training Center “HazMat City.” Deployed via drone airdrops, our sensor robots provided visual and 4-gas sensor data to the incident command center during a simulated tanker truck leak.