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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Yi Dong Jinjun Duan Yangjun Liu Zhendong Dai Poramate Manoonpong Institute of Bio-inspired Structure and Surface Engineering 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

Packaging is usually a multi-object manipulation task involving objects of different softness and dimensions. The flexibility of paper and the variety of shoe types and states are major challenges for packaging tasks. In this study, we propose a complete solution for a shoe packaging task involving paper, which makes full use of extrinsic resources to improve its efficiency. Firstly, according to the different characteristics of the manipulated objects and task requirements, visual perception is developed to obtain the grasp poses of different objects. Furthermore, paper grasping strategies by exploiting environmental constraints and shoe reorientation method with gravity are proposed, which are necessary for a shoe packaging task. Finally, the complete pipeline for a shoe packaging task is introduced and qualitative and quantitative experiments verify its feasibility and robustness.

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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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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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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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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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Online Adaptive Impedance Control with Gravity Compensation for an Interactive Lower-Limb Exoskeleton

Online Adaptive Impedance Control with Gravity Compensation ...

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

作者： Run Janna Kanut Tarapongnivat Natchaya Sricom Chaicharn Akkawutvanich Xiaofeng Xiong Poramate Manoonpong 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 (SDU) Odense Denmark

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

While lower-limb exoskeletons have been increasingly used for gait assistance and rehabilitation, most of them continue to function as assistive devices in the exoskeleton-user relationship as a leader and follower. This limits the user’s ability to interactively contribute to gait control. Therefore, this study proposes an interactive user-exoskeleton control strategy to translate the exoskeletons into interactive compliant companion devices with the exoskeleton-user relationship as the collaborator. This strategy is implemented through online adaptive impedance control with gravity compensation (OAIC-GC). It relies solely on internal pose feedback (joint position) rather than external sensors such as electromyography, torque, or force, as utilized in other assist-as-needed (AAN) control methods. The OAIC-GC can automatically capture the mechanical impedance dynamics of the user’s lower limbs during walking and thus facilitate adaptive, versatile, and personalized gait assistance. It is evaluated using a real lower-limb exoskeleton system with six degrees of freedom (DOFs) across different users engaging in various activities. These activities include symmetrical and asymmetrical walking on a split-belt treadmill at different speeds, as well as walking up stairs. The results indicate a significant improvement in the exoskeleton’s performance in terms of adaptability and movement smoothness under all activities when compared to traditional control. The proposed control reduces joint assistance torque across all exoskeleton joints, enhancing user interaction and comfort. This enables users to actively control their gait patterns, enabling the exoskeleton to operate in an interactive assist-as-needed (IAAN) mode.

关键词： Legged locomotion Mechanical sensors Limbs Torque Exoskeletons Stairs Electromyography Impedance Intelligent robots Gravity

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A Study of Fitness Gains in Evolving Finite State Machines

arXiv

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arXiv 2023年

作者： Zoltai, Gábor Xie, Yue Neumann, Frank Optimisation and Logistics School of Computer and Mathematical Sciences University of Adelaide Australia Bio-Inspired Robotics Laboratory Department of Engineering University of Cambridge United Kingdom

Among the wide variety of evolutionary computing models, Finite State Machines (FSMs) have several attractions for fundamental research. They are easy to understand in concept and can be visualised clearly in simple cases. They have a ready fitness criterion through their relationship with Regular Languages. They have also been shown to be tractably evolvable, even up to exhibiting evidence of open-ended evolution in specific scenarios. In addition to theoretical attraction, they also have industrial applications, as a paradigm of both automated and user-initiated control. Improving the understanding of the factors affecting FSM evolution has relevance to both computer science and practical optimisation of control. We investigate an evolutionary scenario of FSMs adapting to recognise one of a family of Regular Languages by categorising positive and negative samples, while also being under a counteracting selection pressure that favours fewer states. The results appear to indicate that longer strings provided as samples reduce the speed of fitness gain, when fitness is measured against a fixed number of sample strings. We draw the inference that additional information from longer strings is not sufficient to compensate for sparser coverage of the combinatorial space of positive and negative sample strings. © 2023, CC BY-NC-ND.

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A small soft-bodied crawling robot with electromagnetic legs and neural control for locomotion on various metal terrains

A small soft-bodied crawling robot with electromagnetic legs...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Naris Asawalertsak Worameth Nantareekurn Poramate Manoonpong The Bio-inspired Robotics and Neural Engineering Laboratory School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong Thailand the Embodied Artificial Intelligence and Neurorobotics Laboratory SDU Biorobotics The Mrersk Mc-Kinney Møller Institute University of Southern Denmark Odense M Denmark

Soft-bodied crawling animals (like caterpillars and inchworms) exploit their active soft bodies with passive adapt-ability to achieve efficient locomotion and move on multiple terrains. While several research studies have used this principle for robot development, most existing caterpillar/inchworm-inspired soft robots can still crawl on specific terrain (flat, inner, or outer pipes). To advance state-of-the-art soft robotic technology, we propose here a small soft-bodied crawling robot with electromagnetic legs and passive body adaptation. The robot is driven by neural central pattern generator (CPG)-based control. Due to the combination of its actively con-tractable/extendable body, passively adaptable interconnected body joints, and electromagnetic legs, the robot can successfully crawl on a variety of metal terrains, including a flat surface, step, slope, confined space, and an inner (concave surface) and outer (convex surface) pipe in both horizontal and vertical directions. Additionally, it can be steered to navigate through a cluttered environment with obstacles. Using the CPG-based control method, the robot's locomotion speed can be simply regulated by changing a single CPG-frequency control param-eter. This small soft robot has the potential to be employed as a robotic system for inner and outer pipe inspection and confined space exploration in the oil and gas industry.

关键词： Legged locomotion Service robots Navigation Oils Metals Soft robotics Inspection

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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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n-LIPO: Framework for Diverse Cooperative Agent Generation using Policy Compatibility

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Charakorn, Rujikorn Manoonpong, Poramate Dilokthanakul, Nat Vidyasirimedhi Institute of Science and Technology Bio-Inspired Robotics & Neural Engineering Laboratory School of Information Science and Technology Rayong Thailand Mærsk Mc-Kinney Møller Institute University of Southern Denmark Embodied AI and Neurorobotics Laboratory SDU Biorobotics Odense Denmark King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

Diverse training partners in multi-agent tasks are crucial for training a robust and adaptable cooperative agent. Prior methods often rely on state-action information to diversify partners’ behaviors, but this can lead to minor variations instead of diverse behaviors and solutions. We address this limitation by introducing a novel training objective based on "policy compatibility." Our method learns diverse behaviors by encouraging agents within a team to be compatible with each other while being incompatible with agents from other teams. We theoretically prove that incompatible policies are inherently dissimilar, allowing us to use policy compatibility as a proxy for diversity. We call this method Learning Incompatible Policies for n-Player Cooperative Games (n-LIPO). We propose to further diversify individual policies by incorporating a mutual information objective using state-action information. We empirically demonstrate that n-LIPO effectively generates diverse joint policies in various two-player and multi-player cooperative environments. In a complex cooperative task, two-player multi-recipe Overcooked, we find that n-LIPO generates a population of behaviorally diverse partners. These populations are then used to train robust generalist agents that can generalize better than using baseline populations. Finally, we demonstrate that n-LIPO can be applied to a high-dimensional StarCraft Multi-Agent Challenge (SMAC) multi-player cooperative environment to discover diverse winning strategies when only a single goal exists. © 2020 IEEE.

关键词： Reinforcement learning

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