Paper Reading AI Learner

Soft robotics towards sustainable development goals and climate actions

2023-03-21 15:33:08
Goffredo Giordano, Saravana Prashanth Murali Babu, Barbara Mazzolai

Abstract

Soft robotics technology can aid in achieving United Nations Sustainable Development Goals (SDGs) and the Paris Climate Agreement through development of autonomous, environmentally responsible machines powered by renewable energy. By utilizing soft robotics, we can mitigate the detrimental effects of climate change on human society and the natural world through fostering adaptation, restoration, and remediation. Moreover, the implementation of soft robotics can lead to groundbreaking discoveries in material science, biology, control systems, energy efficiency, and sustainable manufacturing processes. However, to achieve these goals, we need further improvements in understanding biological principles at the basis of embodied and physical intelligence, environment-friendly materials, and energy-saving strategies to design and manufacture self-piloting and field-ready soft robots. This paper provides insights on how soft robotics can address the pressing issue of environmental sustainability. Sustainable manufacturing of soft robots at a large scale, exploring the potential of biodegradable and bioinspired materials, and integrating onboard renewable energy sources to promote autonomy and intelligence are some of the urgent challenges of this field that we discuss in this paper. Specifically, we will present field-ready soft robots that address targeted productive applications in urban farming, healthcare, land and ocean preservation, disaster remediation, and clean and affordable energy, thus supporting some of the SDGs. By embracing soft robotics as a solution, we can concretely support economic growth and sustainable industry, drive solutions for environment protection and clean energy, and improve overall health and well-being.

Abstract (translated)

软机器人技术可以通过开发自主、环境负责任的机器,使用可再生能源驱动,来协助实现联合国可持续发展目标(SDGs)和巴黎气候协定。利用软机器人技术,我们可以通过促进适应、恢复和修复,减缓气候变化对人类和社会自然世界带来的有害影响。此外,实施软机器人技术还可以导致在材料科学、生物学、控制系统、能源效率和可持续制造进程中的开创性发现。但是,要实现这些目标,我们需要进一步改善理解生物体内性和身体智能、环境友好材料以及节省能源的战略,以设计和制造自主运行并field-ready的软机器人。本文提供了关于软机器人如何解决环境问题的重要见解。大规模生产软机器人的可持续性制造、探索可生物降解和生物启发材料的潜力,以及集成船上可再生能源,以促进自主和智能是该领域紧迫挑战之一,我们在此本文中讨论了这些问题。具体而言,我们将提供针对城市农业、医疗保健、土地和海洋保护、灾难恢复和清洁且价格合理的能源的目标生产性应用field-ready的软机器人,从而支持一些SDGs。通过拥抱软机器人作为解决方案,我们可以具体支持经济增长和可持续工业,推动环境保护和清洁能源的解决方案,并改善整体健康和福利。

URL

https://arxiv.org/abs/2303.11931

PDF

https://arxiv.org/pdf/2303.11931.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot