In this work we address the flexible physical docking-and-release as well as recharging needs for a marsupial system comprising an autonomous tiltrotor hybrid Micro Aerial Vehicle and a high-end legged locomotion robot. Within persistent monitoring and emergency response situations, such aerial / ground robot teams can offer rapid situational awareness by taking off from the mobile ground robot and scouting a wide area from the sky. For this type of operational profile to retain its long-term effectiveness, regrouping via landing and docking of the aerial robot onboard the ground one is a key requirement. Moreover, onboard recharging is a necessity in order to perform systematic missions. We present a framework comprising: a novel landing mechanism with recharging capabilities embedded into its design, an external battery-based recharging extension for our previously developed power-harvesting Micro Aerial Vehicle module, as well as a strategy for the reliable landing and the docking-and-release between the two robots. We specifically address the need for this system to be ferried by a quadruped ground system while remaining reliable during aggressive legged locomotion when traversing harsh terrain. We present conclusive experimental validation studies by deploying our solution on a marsupial system comprising the MiniHawk micro tiltrotor and the Boston Dynamics Spot legged robot.
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Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information
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. Modern 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 and the Berkeley Emergent Space Tensegrities Laboratory have developed hardware and a framework for rapidly deploying sensor robots for integrated ground-aerial disaster response. The semi-autonomous delivery of sensors using tensegrity (tension-integrity) robotics uses structures that are flexible, lightweight, and have high stiffness-to-weight ratios, making them ideal candidates for robust high-altitude deployments. Squishy Robotics has developed a tensegrity robot for commercial use in Hazardous Materials (HazMat) scenarios that is capable of being deployed from commercial drones or other aircraft. Squishy Robots have been successfully deployed with a delicate sensing and communication payload of up to 1,000 ft.
This paper describes the framework for optimizing the deployment of emergency sensors spatially over time. AI techniques (e.g., Long Short-Term Memory neural networks) 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. Decisions on mitigation include shutting down the plant or evacuating the local community. The 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. A case study using data from the Tennessee Eastman process dataset of a chemical plant displayed in OSI Soft is provided.
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- Award ID(s):
- 1927010
- NSF-PAR ID:
- 10317188
- Date Published:
- Journal Name:
- ASME 2021 International Mechanical Congress Exposition (IMECE 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/IMECE2021-70759
