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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Kongkiat Rothomphiwat Prakarn Jaroonsorn Pakpoom Kriengkomol Poramate Manoonpong Bio-Inspired Robotics and Neural Engineering Laboratory School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong Thailand AI and Robotics Ventures Company Limited (ARV) Bangkok Thailand Bio-Inspired Robotics and Neural Engineering Laboratory School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology (VISTEC) Rayong Thailand Embodied Artificial Intelligence and Neurorobotics Laboratory SDU Biorobotics the Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense Denmark Odense Denmark

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this work, we address the challenges of robust precision landing maneuvers for a quadrotor on both stationary and moving ground targets in the presence of disturbances that can cause the quadrotor to deviate from its desired trajectory, leading to maneuver failure. To overcome this, we propose a novel online adaptive trajectory planning approach based on the online temporal scaling adaptation of dynamic movement primitives (DMPs). This adaptation enables the desired trajectory to be dynamically adjusted in response to tracking errors and the goal’s state. Consequently, our proposed approach enhances accuracy, precision, and safety during landing maneuvers. The effectiveness of the approach is evaluated through comprehensive experiments conducted in both physical simulations and real-world environments, covering various disturbance scenarios.

关键词： Adaptation models Accuracy Trajectory planning Tracking Trajectory Safety Intelligent robots Quadrotors

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Multimodel Sensor Fusion for Learning Rich Models for Interacting Soft Robots

arXiv

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arXiv 2022年

作者： Thuruthel, Thomas George Iida, Fumiya The Bio-Inspired Robotics Lab Department of Engineering University of Cambridge United Kingdom

Soft robots are typically approximated as low-dimensional systems, especially when learning-based methods are used. This leads to models that are limited in their capability to predict the large number of deformation modes and interactions that a soft robot can have. In this work, we present a deep-learning methodology to learn high-dimensional visual models of a soft robot combining multimodal sensorimotor information. The models are learned in an end-to-end fashion, thereby requiring no intermediate sensor processing or grounding of data. The capabilities and advantages of such a modelling approach are shown on a soft anthropomorphic finger with embedded soft sensors. We also show that how such an approach can be extended to develop higher level cognitive functions like identification of the self and the external environment and acquiring object manipulation skills. This work is a step towards the integration of soft robotics and developmental robotics architectures to create the next generation of intelligent soft robots. © 2022, CC BY.

关键词： Deep learning

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neural Control and Learning with a Turning Strategy of a Gecko-inspired Robot with a Bendable Body

Neural Control and Learning with a Turning Strategy of a Gec...

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IEEE International Conference on Control and robotics engineering (ICCRE)

作者： Worasuchad Haomachai Zhendong Dai Yang Li Poramate Manoonpong State Key Laboratory of Mechanics and Control for Aerospace Structures College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Bio-inspired Robotics & Neural Engineering Lab School of Information Science & Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand

ISBN: (数字)9798350372694

ISBN: (纸本)9798350372700

Sprawling animals leverage flexible trajectories during turning to navigate challenging and narrow environments effectively. Motivated by this natural behavior, a gecko-inspired robot with a bendable body is employed to investigate and implement an efficient turning strategy. A neural central pattern generator (CPG) with a radial basis function (RBF)-based premotor neuron network (CPG-RBF network) with black-box optimization (BBO) was used to control and train the robot for walking and consequently exploring the turning strategies based on the established walking policy to facilitate turning without the need for additional learning time. Through simulated robot experiments in circular and track environments, the robot with an undulated C-shaped body strategy achieved a lower radius of the turning curve and consequently gained faster locomotion speed by approximately 23% compared to a fixed C-shaped body strategy. This enhanced performance is attributed to the undulated body's effectiveness in facilitating turning maneuvers.

关键词： Legged locomotion Navigation Animals Neurons Closed box Turning Generators

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Multi-modal Sensor Fusion for Learning Rich Models for Interacting Soft Robots

Multi-modal Sensor Fusion for Learning Rich Models for Inter...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Thomas George Thuruthel Fumiya Iida Department of Engineering The Bio-Inspired Robotics Lab University of Cambridge UK Department of Computer Science University College London London UK

关键词： Visualization Shape control Soft sensors Tactile sensors Soft robotics Predictive models Robot sensing systems

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3D Printable Self-Sensing Magnetorheological Elastomer

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MACROMOLECULAR MATERIALS AND engineering 2024年第2期309卷 2470003-2470003页

作者： Costi, Leone Georgopoulou, Antonia Mondal, Somashree Iida, Fumiya Clemens, Frank The Bio-Inspired Robotics Lab Department of Engineering University of Cambridge Trumpington Street Cambridge CB2 1PZ UK Department of Functional Materials Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf 8600 Switzerland

Front cover: Self-sensing magnetorheological elastomers showcase resistive changes induced both by magnetic fields and mechanical loads, on top of magnetic-dependent stiffening. In article 2300294, Leone Costi and co-workers report the first case of 3D printable self-sensing magnetorheological elastomers. The proposed materials are characterized both as a function of a varying magnetic field and mechanical cyclic load, both in compression and tension.

关键词： 3D printing magnetoresistive sensors magnetorheological elastomers piezoresistive sensors

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Morphological Adaptation for Speed Control of Pipeline Inspection Gauges MC-PIG

Morphological Adaptation for Speed Control of Pipeline Inspe...

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2021 Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021

作者： Phodapol, Sujet Suthisomboon, Tachadol Kosanunt, Pong Vongasemjit, Ravipas Janbanjong, Petch Manoonpong, Poramate Bio-inspired Robotics and Neural Engineering BRAIN Laboratory School of Information Science and Technology IST Vidyasirimedhi Institute of Science and Technology VISTEC AI and Robotics Ventures Co. Ltd. PTT Exploration and Production PLC

ISBN: (纸本)9781613998342

Passive and active hybrid pipeline inspection gauges (PIGs) have been used for in-pipe inspection. While a passive PIG cannot control its speed, the hybrid version can achieve this by using an integrated valve specifically designed and embedded in the PIG. This study proposes a generic new method for speed adaptation in PIGs (called MC-PIG) by introducing a generic, modular, controllable, external valve unit add-on for attaching to existing conventional (passive) PIGs with minimal change. The MC-PIG method is based on the principle of morphological computation with closed-loop control. It is achieved by regulating/computing the PIG's morphology (i.e., a modular rotary valve unit add-on) to control bypass flow. Adjustment of the valve angle can affect the flow rate passing through the PIG, resulting in speed regulation ability. We use numerical simulation with computational fluid dynamics (CFD) to investigate and analyze the speed of a simulated PIG with the valve unit adjusted by proportional-integral (PI) control under various in-pipe pressure conditions. Our simulation experiments are performed under different operating conditions in three pipe sizes (16″, 18″, and 22″ in diameter) to manifest the speed adaptation of the PIG with the modular valve unit add-on and PI control. Our results show that the PIG can effectively perform real-time adaptation (i.e., adjusting its valve angle) to maintain the desired speed. The valve design can be adjusted from 5 degrees (closed valve, resulting in high moving speed) to a maximum of 45 degrees (fully open valve, resulting in low moving speed). The speed of the PIG can be regulated from 0.59 m/s to 3.88 m/s in a 16″ pipe at 4.38 m/s (in-pipe fluid velocity), 2500 kPa (operating pressure), and 62 °C (operating temperature). Finally, the MC-PIG method is validated using a 3D-printed prototype in a 6″ pipe. Through the investigation, we observed that two factors influence speed adaptation;the pressure drop coefficient and frictio

关键词： Speed

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S-DOT: Soft Dot-Structured Surface Pattern for Enhanced Grip and Comfort in Exoskeleton Straps

S-DOT: Soft Dot-Structured Surface Pattern for Enhanced Grip...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Suksakaow Mahuttanatan Kanut Tarapongnivat Naris Asawalertsak Chaicharn Akkawutvanich Poramate Manoonpong Bio-Inspired Robotics and Neural Engineering Laboratory School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong Thailand Embodied Artificial Intelligence and Neurorobotics Laboratory SDU Biorobotics the Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense Denmark

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

In exoskeleton leg strap applications, maintaining a reliable and enhanced grip, particularly under varying conditions, remains an ongoing challenge. Conventional ethylenevinyl acetate (EVA) foam solutions, while offering some degree of comfort, often fail to provide consistent grip stability, especially when exposed to moisture or oil. To address this limitation, this study investigates an alternative solution using a combination of soft materials, such as silicone and surface pattern design. These patterns are meticulously crafted to optimize load distribution and minimize high-pressure points without compromising grip efficiency. Through comprehensive experiments and analysis under dry, moist, and oily conditions, we compare the performance of the proposed silicone-patterned straps against traditional EVA foam straps. The results reveal that the soft dot-structured surface pattern (S-DOT)—silicone with a dot pattern—significantly outperforms EVA foam, exhibiting the lowest slip distance (<10 mm) and showcasing its potential as a simple but effective anti-slip solution for enhanced exoskeleton wearability. The findings of this study suggest a promising future for patterned silicone-based techniques in improving force dispersion and enhancing exoskeleton wearability in real-world scenarios.

关键词： Legged locomotion Limbs Oils Friction Exoskeletons Force Moisture Soft robotics Stability analysis Reliability

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S-BUN: Soft Bifunctional Utility Module for Robot Sensing and Signaling

S-BUN: Soft Bifunctional Utility Module for Robot Sensing an...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Suksakaow Mahuttanatan Naris Asawalertsak Jinjuta Paripurana Kanut Tarapongnivat Thirawat Chuthong Poramate Manoonpong Bio-Inspired Robotics and Neural Engineering Laboratory School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong Thailand Embodied Artificial Intelligence and Neurorobotics Laboratory SDU Biorobotics Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense Denmark

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Conventional approaches in robotics for perceiving the environment and signaling the robot’s state or intention for human-robot interaction involve the use of separate sensing and signaling systems. This can sometimes result in high costs and complex system installations. In this study, we propose an alternative approach, integrating both robot sensing and signaling mechanisms into a single utility module (called S-BUN, Soft Bifunctional Utility module for robot sensing aNd signaling). Soft material (Ecoflex 00-10 silicone) is used to form its bun-like structure with a central cavity filled with a NaCl solution. inspired by honeycombs, the module’s surface incorporates a hexagonal pattern to enhance structural robustness. The design of S-BUN enables it to function as a sensor for both non-contact proximity and touch sensing, utilizing the NaCl solution. Additionally, it serves as a signaling mechanism to indicate the robot’s state through the active inflation and deflation dynamics of the module, simulating lifelike breathing patterns. Through our experiments, we present S-BUN’s capabilities in proximity and touch sensing, including its ability to discern various touch intensities. Finally, we demonstrate the application of S-BUN in the context of reactive behavioral control for a crawling robot and human-robot interaction scenarios.

关键词： Costs Communication system signaling Human-robot interaction Robot sensing systems Robustness Sensors Complex systems Robots Intelligent robots

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A Deep Learning Approach with Uncertainty Estimation to Assess Aboveground biomass Mapping of Tropical Rainforest in Thailand

A Deep Learning Approach with Uncertainty Estimation to Asse...

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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： Nattawach Sataudom Songphon Reangsang Suwit Navakam Poramate Manoonpong Anuphao Aobpaet Pasuta Sunthornhao Natnicha Yooyen Mattana Pongsopon Natthasiree Chulinrak Sasawat Soontaros Panunya Charoensawadpong Soravis Supavetch Varuna Company Limited Bangkok Thailand Reforestation and Ecology Institute PTT Public Company Limited Bangkok Thailand Bio-Inspired Robotics and Neural Engineering Vidyasirimedhi Institute of Science and Technology Rayong Thailand Department of Civil Engineering Faculty of Engineering Kasetsart University Bangkok Thailand Department of Forest Management Faculty of Forestry Kasetsart University Bangkok Thailand

ISBN: (数字)9798350358582

ISBN: (纸本)9798350358599

This study proposes an aboveground biomass (AGB) density model to overcome the limitation of sparse data coverage in Thailand and scale at national level. The model is based on a U-Net convolutional neural network and fuses spaceborne remote sensing data including Sentinel-1, Sentinel-2, and GEDI. Five models were developed using a deep ensemble technique to provide uncertainty estimation, where accuracy was validated using three types of forest inventory data. Tropical rainforests and other forested areas such as perennial crop regions with similar geographical characteristics were evaluated. Results show high uncertainty in the out-of-distribution data areas, with %RRMSE for Pará rubber, mixed deciduous forest, and dry evergreen forest of 17.05, 24.47, and 14.0, respectively.

关键词： Rainforests Uncertainty Fuses biological system modeling Estimation Forestry Data models

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Editorial: The roles of self-organization and sensory adaptation for locomotion in animals and robots

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Frontiers in Neurorobotics 2024年 18卷 1372772页

作者： Sándor, Bulcsú Gros, Claudius Manoonpong, Poramate Department of Physics Babes-Bolyai University Cluj-Napoca Romania Institute for Theoretical Physics Goethe University Frankfurt Frankfurt am Main Germany Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong Thailand Embodied Artificial Intelligence and Neurorobotics Lab SDU Biorobotics The Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense Denmark

Editorial on the Research Topic The roles of self-organization and sensory adaptation for locomotion in animals and robots 1 Introduction Self-organization is the emergence of a global spatial or dynamical pattern in a system with interacting components governed by simple local rules (Gros, 2015). The investigated leg and body coordination strategies include (i) the local leg and body coordination at the segment level (LCS) in a manner similar to millipedes, simultaneous leg amplitude reduction in response to different turning directions (SAR, like insects), and the global phase reversal of legs inside of turning curve (GPR, typical engineering approach). While Wang et al. mostly focus on variants of particle swarm optimization algorithms, various bio-inspired heuristic optimization methods are also discussed together with the active challenges of robot olfaction research. 3 Concluding remarks The present Research Topic brings together studies focusing on different aspects of self-organized locomotion starting from the role of load feedback signals, through bio-inspired control strategies for intra- and inter-leg coordination, biologically realistic neural networks for obstacle avoidance and maze navigation tasks, to odor source localization by swarms. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

关键词： robotic locomotion motion patterns swarms of robots control and learning algorithms sensory feedback loop

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