Intelligent System Synthesis for Dynamic Locomotion Behavior in Multi-legged Robots

Robot technology has been implemented in many fields of our life, such as entertainment, security, rescue, rehabilitation, social life, the military, and etc. Multi-legged robot always exist in many fields, therefore it is important to be developed. Motion capabilities of the robot will be a main focus to be developed. Current development or conventional model of motion capabilities have several issues in saturation of development. There are some limitation in dynamic factors such as, locomotion generator, flexibility of motion planning, and smoothness of movement. Therefore, in this research, natural based computation are implemented as the basic model. There are three subsystems to be developed and integrated, (1) locomotion behavior model, (2) stability behavior model, and (3) motion planning model. Since individual people has different walking behavior in each walking direction and walking speed, locomotion behavior learning model of omni-directional bio-inspired locomotion which is generating different walking behavior in different walking provision are required to be developed. Step length in sagital and coronal direction, and degree of turning are considered parameters in walking provision. In proposed omni-directional walking model, interconnection structures composed by 16 neurons where 1 leg is represented by 4 joints and 1 joint is represented by 2 motor neurons. In order to acquire walking behavior in certain walking provision, the interconnection structure is optimized by multi-objectives evolutionary algorithm. For acquiring the diversity of references, several optimized interconnection structures are generated in optimization processes in different walking provisions. Learning models are proposed for solving non-linearity of relationship between walking input and walking output representing the synaptic weight of interconnection structure, where one learning model representing one walking parameter. Furthermore, by using optimized model, walking behavior can be generated with unsealed walking provision. Smooth walking transition with low error of desired walking provision was proved based on several numerical experiments in physical computer simulation. In stability behavior model, neuro-based push recovery controller is applied in multi-legged robot in order to keep the stability with minimum energy required. There are three motion patterns in individual people behavior when it gets external perturbation, those are ankle behavior, hip behavior, and step behavior. We propose a new model of Modular Recurrent Neural Network (MRNN) for performing online learning system in each motion behavior. MRNN consists of several recurrent neural networks (RNNs) working alternatively depending on the condition. MRNN performs online learning process of each motion behavior controller independently. The aim of push recovery controller is to manage the motion behavior controller by minimizing the energy required for responding to the external perturbation. This controller selects the appropriate motion behavior and adjusts the gain that represent the influence of the motion behavior to certain push disturbance based on behavior graphs which is generated by adaptive regression spline. We applied the proposed controller to the humanoid robot that has small footprint in open dynamics engine. Experimental result shows the effectiveness of the push controller stabilizing the external perturbation with minimum energy required. Proposed motion planning model presents a natural mechanism of the human brain for generating a dynamic path planning in 3-D rough terrain. The proposed model not only emphasizes the inner state process of the neuron but also the development process of the neurons in the brain. There are two information transmission processes in this proposed model, the forward transmission activity for constructing the neuron connections to find the possible way and the synaptic pruning activity with backward neuron transmission for finding the best pathway from current position to target position and reducing inefficient neuron with its synaptic connections. In order to respond and avoid the unpredictable obstacle, dynamic path planning is also considered in this proposed model. An integrated system for applying the proposed model in the actual experiments is also presented. In order to confirm the effectiveness of the proposed model, we applied the integrated system in the pathway of a four-legged robot on rough terrain in computer simulation. For analyzing and proving the flexibility of proposed model, unpredictable collision is also performed in those experiments. The model can find the best pathway and facilitate the safe movement of the robot. When the robot found an unpredictable collision, the path planner dynamically changed the pathway. The proposed path planning model is capable to be applied in further advance implementation. In order to implement the motion capabilities in real cases, all subsystem should be integrated into one interconnected motion capabilities model. We applied small quadruped robot equipped with IMU, touch sensor, and dual ultrasonic sensor for performing motion planning in real terrain from starting point to goal point. Before implemented, topological map is generated by Kinect camera. In this implementation, all subsystem were analyzed and performed well and the robot able to stop in the goal point. These implementation proved the effectiveness of the system integration, the motion planning model is able to generate safe path planning, the locomotion model is able to generate flexible movement depending on the walking provision from motion planning model, and the stability model can stabilize the robot on rough terrain. Generally, the proposed model can be expected to bring a great contribution to the motion capabilities development and can be used as alternative model for acquiring the dynamism and efficient model in the future instead of conventional model usage. In the future, the proposed model can be applied into any legged robot as navigation, supporter, or rescue robot in unstable environmental condition. In addition, we will realize a cognitive locomotion that generates multiple gaits depending on the 3 aspects, embodiment, locomotion generator, and cognition model. A dynamic neuro-locomotion integrated with internal and external sensory information for correlating with the environmental condition will be designed.ロボット技術は、エンターテイメント、セキュリティ、救助、リハビリ、社会生活、軍事などの様々な生活分野に実現さている。多脚ロポットは常に多くの分野に存在するため開発することが重要である。ロボットの運動能力が開発の主要となっている。現状の開発されている動作能力は,飽和状態にある。いくつかの動的な要因により、歩行生成器、動作計画の柔軟性、および動作の滑らかさ等に制限がある。そこで、本研究では、基本的なモデルとして自然計算に基づく方法論を実装する、また、本研究では、歩行動作モデル、安定動作モデル、や運動計画モデルからなる3つのサブシステムを開発し統合する。人間は歩行方向と速度に応じて歩行動作が異なるため、異なる歩行軸では異なる歩行動作を生成するという全方位生物的な運動の歩行動作学習モデルが開発には要求される。球欠および制御方向のステップ長や旋回の度合いは,歩行軸のパラメータとして考慮される。提案した全方位歩行モデルでは,1肢につき16個のニューロンによって構成される相互接続構造を4つの関節によって表現する。また、1つの関節は,2個のモータニューロンによって表現する。一定の歩行軸での歩行動作を獲得するために,本研究では,多目的進化アルゴリズムによって最適化を行う。提案手法では、参照点の多様性を獲得するために,異なる歩行軸においていくつかの最適な相互接続構造が生成される。相互接続構造のシナプス重みを表現している歩行入力と出力間の非線形な関係を解くための学習モデルを構築する。本手法では,1つの学習モデルが1つの歩行パラメータで表現され、最適化されたモデルを用いることにより,歩行動作は,スケーリングされていない歩行軸を生成することが可能となる,物理演算シミュレーションを用いた実験により,誤差の少ない歩行軸の滑らかな歩行遷移を本実験では示している。安定動作モデルでは、必要最小限のエネルギーで安定性を維持するため多足歩行ロボットにニューロベースプッシュリカバリ制御器を適用した。外力をを受けたとき,人間の行動には足首の動作・股関節の動作・踏み動作の3つの動作パターンが存在する。本研究では,各運動動作におけるオンライン学習システムを実現するために、モジュラーリカレントニューラルネットワーク(MRNN)を用いた新たな学習モデルを提案する。MRNNは状況に応じて選択される複数のリカレントニューラルネットワーク(RNN)によって構成される。MRNNは各運動動作コントローラのオンライン学習プロセスを独立して実行する。プッシュリカバリ制御器の目的は、外乱に応じてエネルギー最小化を行うことによって運動動作制御器を管理することである。この制御器は適切な運動動作を選択し,適応回帰スプラインにより生成された動作グラフに基づき押し動作に対して最も影響を及ぼす運動動作のゲインの調整を行う。提案した制御器をOpen Dynamics Engine(ODE)上で小さな足の長さを持つヒューマノイドロボットに適用し,必要最小限のエネルギーで外力に対して安定させるプッシュリカバリ制御器の有効性を示している。3次元の不整地における動的な経路計画を生成するために,人間の自然な脳機能に基づいた動作計画手法を提案する。本モデルは、ニューロンの内部状態過程だけでなく、脳内のニューロンの発達過程も重視している。本モデルは二つのアルゴリズムに構成される。1つは、通過可能な道を見つけるために構築される接続的なニューロン活動である順方向伝達活動であり,もう1つは、現在位置から最適経路を見つけるために、シナプス結合を用いて非効率的なニューロンを減少させる逆方向にニューロン伝達を行うシナプスプルーニング活動である。また,予測不可能な衝突を回避するために,動的な経路計画も実行される。さらに、実環境において提案されたモデルを実現するための統合システムも提示される。提案モデルの有効性を検証するために,コンピュータシミュレーション上で、不整地環境の4足歩行ロボットに関するシミュレーション環境を実装した。これらの実験では,予測不能な衝突に関する実験も行った。本モデルは、最適経路を見つけ出しロボットの安全な移動を実現できた。さらに、ロボットが予測できない衝突を検出した場合,経路計画アルゴリズムが経路を動的に変更可能であることを示している。これらのことから、提案された経路計画モデルはさらなる先進的な展開が実現可能であると考えられる。実環境における運動能力を実装するためには、すべてのサブシステムを1つの運動能力モデルに統合する必要がある。そこで本研究では、IMU、タッチセンサ、2つの超音波センサを搭載した小型の4足歩行ロポットを用いた実環境において出発地点から目的地点までの運動計画を行った、本実装では、3次元距離計測センサであるKinecを用い3次元空間の位相構造を生成する。また、本実装では、すべてのサブシステムが分析され、ロボットは目的地点で停止することができた。さらに、安全な経路計画を生成することができたことからシステム統合の有効性が確認できた。また、歩行モデルにより歩行軸に応じた柔軟な動きが生成されることで、この安定性モデルは不整地環撹でもロボットの歩行を安定させることができた。これらのことから、本提案モデルは運動能力への多大な貢献が期待され、ダイナミクスを獲得するための代替モデルとして使用することができ,現在よく使用されているモデルに代わる効率的なモデルとなることが考えられる。今後の課題としては,不安定な環境下におけるナビゲーション・支援・レスキューロボットといった任意の肢の数を持つ多足歩行ロボットへの本提案モデルの適用があげられる。さらに,身体性,歩行生成,認知モデルの3つの観点から複数の歩容を生成する認知的歩行を実現することを考えている。環境と相互作用するためのモデルとして、内界センサと外界センサ情報を統合した動的ニューロ歩行を実現する予定である。首都大学東京, 2018-03-25, 修士（工学）首都大学東

Learning control of bipedal dynamic walking robots with neural networks

Thesis (Elec.E.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 90-94).Stability and robustness are two important performance requirements for a dynamic walking robot. Learning and adaptation can improve stability and robustness. This thesis explores such an adaptation capability through the use of neural networks. Three neural network models (BP, CMAC and RBF networks) are studied. The RBF network is chosen as best, despite its weakness at covering high dimensional input spaces. To overcome this problem, a self-organizing scheme of data clustering is explored. This system is applied successfully in a biped walking robot system with a supervised learning mode. Generalized Virtual Model Control (GVMC) is also proposed in this thesis, which is inspired by a bio-mechanical model of locomotion, and is an extension of ordinary Virtual Model Control. Instead of adding virtual impedance components to the biped skeletal system in virtual Cartesian space, GVMC uses adaptation to approximately reconstruct the dynamics of the biped. The effectiveness of these approaches is proved both theoretically and experimentally (in simulation).by Jianjuen Hu.Elec.E

Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir

Over the past years, many robots have been devised to facilitate agricultural activities (that are labor-intensive in nature) so that they can carry out tasks such as crop care or selective harvesting with minimum human supervision. It is commonly observed that rapid change in terrain conditions can jeopardize the performance and efficiency of a robot when performing agricultural activity. For instance, a terrain covered with gravel produces high vibration to robot when traversing on the surface. In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). The aim is to evaluate the effectiveness of the Support Vector Machine in recognizing different terrain conditions in an agriculture field. A test bed equipped with a tracked-driven robot and three types o f terrain i.e. sand, gravel and vegetation has been developed. A small and low power MEMS accelerometer is integrated into the robot for measuring the vertical acceleration. In this experiment, the vibration signals resulted from the interaction between the robot and the different type of terrain were collected. An extensive experimental study was conducted to evaluate the effectiveness of SVM. The results in terms of accuracy of two machine learning techniques based on terrain classification are analyzed and compared. The results show that the robot that is equipped with an SVM can recognize different terrain conditions effectively. Such capability enables the robot to traverse across changing terrain conditions without being trapped in the field. Hence, this research work contributes to develop a self-adaptive agricultural robot in coping with different terrain conditions with minimum human supervision

심층 강화학습을 이용한 사람의 모션을 통한 이형적 캐릭터 제어기 개발

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 서진욱.사람의 모션을 이용한 로봇 컨트롤 인터페이스는 사용자의 직관과 로봇의 모터 능력을 합하여 위험한 환경에서 로봇의 유연한 작동을 만들어낸다. 하지만, 휴머노이드 외의 사족보행 로봇이나 육족보행 로봇을 위한 모션 인터페이스를 디자인 하는 것은 쉬운일이 아니다. 이것은 사람과 로봇 사이의 형태 차이로 오는 다이나믹스 차이와 제어 전략이 크게 차이나기 때문이다. 우리는 사람 사용자가 움직임을 통하여 사족보행 로봇에서 부드럽게 여러 과제를 수행할 수 있게끔 하는 새로운 모션 제어 시스템을 제안한다. 우리는 우선 캡쳐한 사람의 모션을 상응하는 로봇의 모션으로 리타겟 시킨다. 이때 상응하는 로봇의 모션은 유저가 의도한 의미를 내포하게 되며, 우리는 이를 지도학습 방법과 후처리 기술을 이용하여 가능케 하였다. 그 뒤 우리는 모션을 모사하는 학습을 커리큘럼 학습과 병행하여 주어진 리타겟된 참조 모션을 따라가는 제어 정책을 생성하였다. 우리는 "전문가 집단"을 학습함으로 모션 리타게팅 모듈과 모션 모사 모듈의 성능을 크게 증가시켰다. 결과에서 볼 수 있듯, 우리의 시스템을 이용하여 사용자가 사족보행 로봇의 서있기, 앉기, 기울이기, 팔 뻗기, 걷기, 돌기와 같은 다양한 모터 과제들을 시뮬레이션 환경과 현실에서 둘 다 수행할 수 있었다. 우리는 연구의 성능을 평가하기 위하여 다양한 분석을 하였으며, 특히 우리 시스템의 각각의 요소들의 중요성을 보여줄 수 있는 실험들을 진행하였다.A human motion-based interface fuses operator intuitions with the motor capabilities of robots, enabling adaptable robot operations in dangerous environments. However, the challenge of designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is emerged from the different morphology and dynamics of a human controller, leading to an ambiguity of control strategy. We propose a novel control framework that allows human operators to execute various motor skills on a quadrupedal robot by their motion. Our system first retargets the captured human motion into the corresponding robot motion with the operator's intended semantics. The supervised learning and post-processing techniques allow this retargeting skill which is ambiguity-free and suitable for control policy training. To enable a robot to track a given retargeted motion, we then obtain the control policy from reinforcement learning that imitates the given reference motion with designed curriculums. We additionally enhance the system's performance by introducing a set of experts. Finally, we randomize the domain parameters to adapt the physically simulated motor skills to real-world tasks. We demonstrate that a human operator can perform various motor tasks using our system including standing, tilting, manipulating, sitting, walking, and steering on both physically simulated and real quadruped robots. We also analyze the performance of each system component ablation study.1 Introduction 1 2 Related Work 5 2.1 Legged Robot Control 5 2.2 Motion Imitation 6 2.3 Motion-based Control 7 3 Overview 9 4 Motion Retargeting Module 11 4.1 Motion Retargeting Network 12 4.2 Post-processing for Consistency 14 4.3 A Set of Experts for Multi-task Support 15 5 Motion Imitation Module 17 5.1 Background: Reinforcement Learning 18 5.2 Formulation of Motion Imitation 18 5.3 Curriculum Learning over Tasks and Difficulties 21 5.4 Hierarchical Control with States 21 5.5 Domain Randomization 22 6 Results and Analysis 23 6.1 Experimental Setup 23 6.2 Motion Performance 24 6.3 Analysis 28 6.4 Comparison to Other Methods 31 7 Conclusion And Future Work 32 Bibliography 34 Abstract (In Korean) 44 감사의 글 45석

Coupling Vision and Proprioception for Navigation of Legged Robots

We exploit the complementary strengths of vision and proprioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully utilize this capability, we need a high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy in varying environments. We achieve this by using proprioceptive feedback to ensure the safety of the planned path by sensing unexpected obstacles like glass walls, terrain properties like slipperiness or softness of the ground and robot properties like extra payload that are likely missed by vision. The navigation system uses onboard cameras to generate an occupancy map and a corresponding cost map to reach the goal. A fast marching planner then generates a target path. A velocity command generator takes this as input to generate the desired velocity for the walking policy. A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator. We show superior performance compared to wheeled robot baselines, and ablation studies which have disjoint high-level planning and low-level control. We also show the real-world deployment of VP-Nav on a quadruped robot with onboard sensors and computation. Videos at https://navigation-locomotion.github.ioComment: CVPR 2022 final version. Website at https://navigation-locomotion.github.i

Utilizing Compliance To Address Modern Challenges in Robotics

Mechanical compliance will be an essential component for agile robots as they begin to leave the laboratory settings and join our world. The most crucial finding of this dissertation is showing how lessons learned from soft robotics can be adapted into traditional robotics to introduce compliance. Therefore, it presents practical knowledge on how to build soft bodied sensor and actuation modules: first example being soft-bodied curvature sensors. These sensors contain both standard electronic components soldered on flexible PCBs and hyperelastic materials that cover the electronics. They are built by curing multi-material composites inside hyper elastic materials. Then it shows, via precise sensing by using magnets and Hall-effect sensors, how closed-loop control of soft actuation modules can be achieved via proprioceptive feedback. Once curvature sensing idea is verified, the dissertation describes how the same sensing methodology, along with the same multi-material manufacturing technique can be utilized to construct soft bodied tri-axial force sensors. It shows experimentally that these sensors can be used by traditional robotic grippers to increase grasping quality. At this point, I observe that compliance is an important property that robots may utilize for different types of motions. One example being Raibert\u27s 2D hopper mechanism. It uses its leg-spring to store energy while on the ground and release this energy before jumping. I observe that via soft material design, it would be possible to embed compliance directly into the linkage design itself. So I go over the design details of an extremely lightweight compliant five-bar mechanism design that can store energy when compressed via soft ligaments embedded in its joints. I experimentally show that the compliant leg design offers increased efficiency compared to a rigid counterpart. I also utilize the previously mentioned soft bodied force sensors for rapid contact detection (~5-10 Hz) in the hopper test platform. In the end, this thesis connects soft robotics with the traditional body of robotic knowledge in two aspects: a) I show that manufacturing techniques we use for soft bodied sensor/actuator designs can be utilized for creating soft ligaments that add strength and compliance to robot joints; and b) I demonstrate that soft bodied force sensing techniques can be used reliably for robotic contact detection

The evolution of modular artificial neural networks.

This thesis describes a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming and Genetic Programming; however, these often fail in the evolution of complex systems, particularly when such systems involve multi-domain sensory information which interacts in complex ways with system outputs. The aim in this work is to produce an evolutionary method that allows the structure of the network to evolve from simple to complex as it interacts with a dynamic environment. This new algorithm is therefore based on Incremental Evolution. A simulated model of a legged robot was used as a test-bed for the approach. The algorithm starts with a simple robotic body plan. This then grows incrementally in complexity along with its controlling neural network and the environment it reacts with. The network grows by adding modules to its structure - so the technique may also be termed a Growth Algorithm. Experiments are presented showing the successful evolution of multi-legged gaits and a simple vision system. These are then integrated together to form a complete robotic system. The possibility of the evolution of complex systems is one advantage of the algorithm and it is argued that it represents a possible path towards more advanced artificial intelligence. Applications in Electronics, Computer Science, Mechanical Engineering and Aerospace are also discussed

The Flemish Sports Compass: from sports orientation to elite performance prediction

The road from beginner to sports champion is a long and unpredictable one. Therefore, choosing a sport that fits their individual characteristics is essential for children to keep them involved in sports. The Flemish Sports Compass is a generic test battery designed to advise children in their sports choice. The test battery includes anthropometric, physical and motor performance measurements and it has the special quality that, in addition to talent detection and talent orientation, it also enhances various derivative test batteries for talent identification. The Flemish Sports Compass consists of field tests appliable in both elementary schools children and in Flemish elite sport schools. On the one hand it is possible to discriminate between different performance levels and on the other this test battery has te ability to detect sport-specific characteristics of an individual. First part of this doctoral dissertation consists of two introductory chapters. The first chapter provides an overview of definitions, theoretical talent models and practical talent systems. The second chapter explains the rationale and the design of the Flemish Sports Compass and displays the preliminary studies for designing the Flemish Sports Compass. In the second part of this dissertation, six original studies are reported. The first study highlights the potential of the Flemish Sports Compass for primary school children. In this study the differences between the sport specific profiles are less pronounced than in the second and the third study, which measured respectively the students of the Flemish elite sport schools and promising athletes of different sports federations. The first three studies indicated that the generic test battery can be deployed on beginners (talent detection) as well as elite athletes (talent identification). The fourth study, with increased specificity, indicates that the generic tests of the Flemish Sports Compass also are able to distinguish between medallists in international competitions and subelite volleyball players. The talent characteristics measured by the Flemish Sports Compass are not only good at predicting and identifying elite level, they also predict attrition in sport. In the fifth study, survival analysis was applied. Parallel to the methods used in medical science where examining the outcome of medication on the participants life expectations is the main goal, survival chances of athletes were calculated in our fifth study. The last study indicated the importance of predictive analytics of a generic test battery. It was shown that artificial neural networks reduce the risk of missing gifted athletes, when selecting the high potential athletes and how the cost of talent development can be reduced without losing talents. In the third part of this dissertation results are discussed and critical reflections and recommendations are given. The different studies provide opportunities to develop a specific talent system for a small country. Flanders’ disadvantage is, that it is hard to compete with giant nations such as China, Russia and the United States. However, the disadvantage of being small is an advantage at the same time. Smallness reduces the risk of missing one single talent. A coordinated approach is necessary, because implementing different talent programs in every single sports federation leads to fragmentation of the scarce resources. First steps have been made by starting up the Flemish sports compass project. Cooperation is the key for small countries. Talent detection in primary schools is the first step to be taken. The advantages are various and children learn to make choices, which is beneficial for their autonomy and competence. Children have different reasons for practicing sports. Some are interested in competition some are not and a few believe in their chances to win medals for their country. Whatever the underlying motivation, we assume that children choose their appropriate sport, although it is obvious that also the sport chooses the child, because the sport demands specific characteristics. This doctoral dissertation intends to to formulate a scientifically based proposal for the implementation of the Flemish Sports Compass. Undermentioned you find the detailed report