Walking trajectory control for a biped robot

A not trivial problem in bipedal robot walking is the instability produced by the violent transition between the different dynamic walk phases. In this work an dynamic algorithm to control a biped robot is proposed. The algorithm is based on cubic polynomial interpolation of the initial conditions for the robot’s position, velocity and acceleration. This guarantee a constant velocity an a smooth transition in the control trajectories. The algorithm was successfully probed in the bipedal robot “Dany walker” designed at the Freie Universität Berlin, finally a briefly mechanical description of the robot structure is presented

АНАЛИТИЧЕСКИЙ МЕТОД ФОРМИРОВАНИЯ ТРАЕКТОРИИ ДВИЖЕНИЯ АНТРОПОМОРФНОГО ШАГАЮЩЕГО АППАРАТА

The planning trajectory of biped robot (generate joints references) or called generating motion trajectory is an important and complex problem facing researchers and developers of motion control methods,especially when we dealing with walking robot, where the stability during walking in very important. The main purpose of planning the trajectory is to generate a reference motion for robot control systems, at the same time, the goal of control is to enable the bipedal system (biped robot) to follow a predefined trajectory during walking (go straight, overcome obstacles, raise / lower legs). Similar to what happens when a person walks, the formation of the trajectory of a biped robot also implies the possibility of improvement through study, minimization of dissociated (inefficiently used) energy, finding the optimal trajectory. This is usually achieved by specifying a series of parameters that describe the desired trajectory.In planning trajectory, a time sequence of values obtained by means of an interpolation function of the target trajectory. In this paper, we describe a different method that used for generating a trajectory of biped robot. Inother words, the motion of the end effector in the working space is described by an interpolating function.Based on the study of literature, among these methods one can discussed as following:1. Analytical methods2. Methods based on the center of gravity (CoG)3. Methods based on the measurement of walking parameters of a human4. Methods based on Stability5. Methods based on optimality6. Methods based on computational intelligenceFurther in this paper, analytical methods are considered in more detail and discussed three point:1. Constraint based model2. Oscillation motion trajectory3. Interpolation based model Keywords: Biped Robot, Generation trajectory, Planning Trajectory, method of planning trajectory.Формирование расчетной (опорной) траектории движения является важной и сложной проблемой, стоящей перед исследователями и разработчиками методов управления движением, АША. Целью планирования траектории движения является формирование опорного движения для систем управления движением; при этом цель управления – дать возможность двуногой системе (роботу) следовать заданной траектории во время движения (идти прямо, преодолевать препятствия, поднимать / опускать ноги).Планирование заключается в генерировании временной последовательности значений, получаемых с помощью интерполирующей функции требуемой траектории. Подобно тому, что происходит, когда человек ходит, формирование траектории двуногого робота также подразумевает возможность улучшения посредством изучения, минимизации диссоциированной (неэффективно используемой) энергии, нахождения оптимальной траектории. Обычно это достигается путем указания ряда параметров, которые описывают желаемую траекторию.В данном работе описываются методы генерирования траектории движения (АША). Другимисловами, движение концевого эффектора в рабочем пространстве описывается интерполирующей функцией.Основываясь на изучении литературы, среди этих методов можно обсудить следующее:1. Аналитические методы2. Методы, базирующиеся на центре тяжести3. Методы, базирующиеся на данных измерения параметров ходьбы человека4. Методы, базирующиеся на устойчивости5. Методы, базирующиеся на оптимальности6. Методы, базирующиеся на вычислительном интеллектеДалее в этой статье аналитические методы рассматриваются более подробно и обсуждаются три момента:1. Модель на базе ограничений2. Траектория осцилляционного движения3. Модель на базе интерполяций Ключевые слова: двуногий робот, антропоморфные роботы, планирование траектории, методы генерирования траектории

Quasi-Static and Dynamic Mismatch for Door Opening and Stair Climbing With a Legged Robot

This paper contributes to quantifying the notion of robotic fitness by developing a set of necessary conditions that determine whether a small quadruped has the ability to open a class of doors or climb a class of stairs using only quasi-static maneuvers. After verifying that several such machines from the recent robotics literature are mismatched in this sense to the common human scale environment, we present empirical workarounds for the Minitaur quadrupedal platform that enable it to leap up, force the door handle and push through the door, as well as bound up the stairs, thereby accomplishing through dynamical maneuvers otherwise (i.e., quasi-statically) achievable tasks. For more information: Kod*la

Center of Pressure Feedback for Controlling the Walking Stability Bipedal Robots using Fuzzy Logic Controller

This paper presents a sensor-based stability walk for bipedal robots by using force sensitive resistor (FSR) sensor. To perform walk stability on uneven terrain conditions, FSR sensor is used as feedbacks to evaluate the stability of bipedal robot instead of the center of pressure (CoP). In this work, CoP that was generated from four FSR sensors placed on each foot-pad is used to evaluate the walking stability. The robot CoP position provided an indication of walk stability. The CoP position information was further evaluated with a fuzzy logic controller (FLC) to generate appropriate offset angles to be applied to meet a stable situation. Moreover, in this paper designed a FLC through CoP region's stability and stable compliance control are introduced. Finally, the performances of the proposed methods were verified with 18-degrees of freedom (DOF) kid-size bipedal robot

Designing an algorithm for bioloid humanoid navigating in its indoor environment

Gait analyses are the preliminary requirements to establish a navigation system of a humanoid robot. Designing a suitable indoor environment and its mapping are also important for the android localization, selection of a goal to achieve it and to perform the assigned tasks in its surroundings. This paper delineates the various gaits like walking, turning, obstacle overcoming and step up-down stairs for a humanoid system. The writing also explicates the design of the indoor test environment with the stationary obstacles placed on the navigation routes. The development of an efficient algorithm is also excogitated based on the various analyses of gaits and the predefined map of the test environment. As the navigation map is predetermined, the designed algorithm animates the humanoid to navigate by selecting an optimal route, depending on some external commands, to reach at the goal position. Finally the performance of the system is analysed based on the elapsed time of the navigation action with the validation of optimal navigation strategy where the designed algorithm demonstrates the robustness of its implementation and execution

Design Development and Analysis of Humanoid Robot

Humanoid robots are those resembling their motion and functioning similar to human beings, having capabilities of doing day to day activities similar to man and replace him in every possible way. These activities vary from daily activities such as walking, standing, and bowing, to staircase climbing, running, and kneeling. The current research integrates multiple technologies and methodologies within a system such as 3D printing, Inverse Kinematic programming, Power electronics, Control system, Learning algorithms, Mechanical Design, Human-computer interaction, software tools for collaborative projects. A detailed mechanical design procedure has been carried out in CAD along with its structural analysis in FEA. Followed by Kinematic and Dynamic analysis of the system considering suitable physical properties in V-re

Guided Curriculum Learning for Walking Over Complex Terrain

Reliable bipedal walking over complex terrain is a challenging problem, using a curriculum can help learning. Curriculum learning is the idea of starting with an achievable version of a task and increasing the difficulty as a success criteria is met. We propose a 3-stage curriculum to train Deep Reinforcement Learning policies for bipedal walking over various challenging terrains. In the first stage, the agent starts on an easy terrain and the terrain difficulty is gradually increased, while forces derived from a target policy are applied to the robot joints and the base. In the second stage, the guiding forces are gradually reduced to zero. Finally, in the third stage, random perturbations with increasing magnitude are applied to the robot base, so the robustness of the policies are improved. In simulation experiments, we show that our approach is effective in learning walking policies, separate from each other, for five terrain types: flat, hurdles, gaps, stairs, and steps. Moreover, we demonstrate that in the absence of human demonstrations, a simple hand designed walking trajectory is a sufficient prior to learn to traverse complex terrain types. In ablation studies, we show that taking out any one of the three stages of the curriculum degrades the learning performance.Comment: Submitted to Australasian Conference on Robotics and Automation (ACRA) 202

Learning When to Switch: Composing Controllers to Traverse a Sequence of Terrain Artifacts

Legged robots often use separate control policiesthat are highly engineered for traversing difficult terrain suchas stairs, gaps, and steps, where switching between policies isonly possible when the robot is in a region that is commonto adjacent controllers. Deep Reinforcement Learning (DRL)is a promising alternative to hand-crafted control design,though typically requires the full set of test conditions to beknown before training. DRL policies can result in complex(often unrealistic) behaviours that have few or no overlappingregions between adjacent policies, making it difficult to switchbehaviours. In this work we develop multiple DRL policieswith Curriculum Learning (CL), each that can traverse asingle respective terrain condition, while ensuring an overlapbetween policies. We then train a network for each destinationpolicy that estimates the likelihood of successfully switchingfrom any other policy. We evaluate our switching methodon a previously unseen combination of terrain artifacts andshow that it performs better than heuristic methods. Whileour method is trained on individual terrain types, it performscomparably to a Deep Q Network trained on the full set ofterrain conditions. This approach allows the development ofseparate policies in constrained conditions with embedded priorknowledge about each behaviour, that is scalable to any numberof behaviours, and prepares DRL methods for applications inthe real worl