YIP A soft intelligent robot for self-digging, multi-modal sensing, and in situ marine sediment analysis
Abstract
Project Abstract (Approved for Public Release)Research Problem and Objectives: Marine sediments are a large storage of ocean pollutants, including heavy metals, persistent organic pollutants, and pharmaceuticals, especially in regions with intense industrial activities, urbanization, and shipping. Such pollutants, once accumulated, pose significant threats to marine ecosystems and organisms, and ultimately human health. Current sediment analysis usually entails lengthy and complicated procedures of sediment sampling and sample preparation/separation, followed by ex-situ sediment analysis using bulky and expensive instruments. Also, sediment analysis largely exists near shores, with very limited information available in the deep ocean. A comprehensive understanding of sediment chemistry and dynamics is significantly limited by 1) the need for ocean sediment sampling and bulky ex-situ analysis instruments, 2) measurement inaccuracy caused by sediment sample degradation and contamination, and 3) limited information about the overall sediment environment. To address these limitations, we will develop a soft intelligent robot that is capable of 1) digging into the marine sediment, 2) conducting in situ analysis of sediment characteristics, especially heavy metals, and 3) performing real-time monitoring of the sediment environment (pH, temperature, pressure, and conductivity), to achieve a comprehensive understanding of the deep ocean sediment chemistry and dynamics.Technical Approach and Tasks: The project will consist of three major tasks as follows.1) Design and develop a soft #octopus# robot for digging into marine sediments. The robot will be fabricated from stimuli-responsive, shape-morphing liquid crystal elastomers. As part of the robot, self-digging #octopus# arms will be fabricated in a coil shape that, upon thermal stimulation from embedded liquid metals, will induce a linear or radial contraction to generate sufficient thrust force and torque to effectively dig into the sediment. We will perform experimental and theoretical analysis to optimize essential material and geometry parameters of the #octopus# arm to achieve desired digging depth and speed.2) Develop microelectrodes for in situ analysis of heavy metals in ocean sediments. A flexible microsensor array composed of gold and bismuth thin film microelectrodes will be developed for in situ measurements of heavy metals including Cu, Pb, Hg, Cd, and Zn via electrochemical square wave anodic stripping voltammetry. In addition, to provide a comprehensive understanding of the seabed environments, we will develop soft, multimodal sensors including pH, temperature, pressure, and salinity sensors, and integrate them with the soft robot for real-time monitoring of the seabed environment.3) System integration and testing. The multimodal sensors will be integrated with the self-digging #octopus# robot into an intelligent system for autonomous, comprehensive sediment analysis in situ. A flexible printed circuit board will be designed and fabricated to house all the electronics and control the thermal actuation of the self-digging arm as well as data transmissionfrom the multimodal sensors. We will perform thorough lab and field testing to evaluate the functionality and efficiency of the developed intelligent system.Anticipated Outcome & Impact: If successful, the proposed work will yield a versatile soft robot with the capabilities of digging into the marine sediment and performing in situ sediment analysis and real-time sediment environment monitoring. The development of such an intelligent system represents a critical step in the overall technology map of the Navy#s growing push for the blue economy by providing the previously inaccessible characteristics of ocean sediments and informing theoretical modelsfor a comprehensive understanding of seafloor stability and dynamic processes. This breakthrough will create new avenues for next-generation ocean exploration.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 08, 2024
- Source ID
- N000142412561
Entities
People
- Xueju Wang
Organizations
- Office of Naval Research
- United States Navy
- University of Connecticut