The Design and Realization of a Sensitive Walking Platform

Legged locomotion provides robots with the capability of adapting to different terrain conditions. General complex terrain traversal methodologies solely rely on proprioception which readily leads to instability under dynamical situations. Biological legged locomotion utilizes somatosensory feedback to sense the real-time interaction of the feet with ground to enhance stability. Nevertheless, limited attention has been given to sensing the feet-terrain interaction in robotics. This project introduces a paradigm shift in robotic walking called sensitive walking realized through the development of a compliant bipedal platform. Sensitive walking extends upon the success of sensitive manipulation which utilizes tactile feedback to localize an object to grasp, determine an appropriate manipulation configuration, and constantly adapts to maintain grasp stability. Based on the same concepts of sensitive manipulation, sensitive walking utilizes podotactile feedback to enhance real-time walking stability by effectively adapting to variations in the terrain. Adapting legged robotic platforms to sensitive walking is not as simple as attaching any tactile sensor to the feet of a robot. The sensors and the limbs need to have specific characteristics that support the implementation of the algorithms and allow the biped to safely come in contact with the terrain and detect the interaction forces. The challenges in handling the synergy of hardware and sensor design, and fabrication in a podotactile-based sensitive walking robot are addressed. The bipedal platform provides contact compliance through 12 series elastic actuators and contains 190 highly flexible tactile sensors capable of sensing forces at any incident angle. Sensitive walking algorithms are provided to handle multi-legged locomotion challenges including stairs and irregular terrain

A Robot Operating System (ROS) based humanoid robot control

This thesis presents adapting techniques required to enhance the capability of a commercially available robot, namely, Robotis Bioloid Premium Humanoid Robot (BPHR). BeagleBone Black (BBB), the decision-making and implementing (intelligence providing) component, with multifunctional capabilities is used in this research. Robot operating System (ROS) and its libraries, as well as Python Script and its libraries have been developed and incorporated into the BBB. This fortified BBB intelligence providing component is then transplanted into the structure of the Robotis Bioloid humanoid robot, after removing the latter’s original decision-making and implementing component (controller). Thus, this study revitalizes the Bioloid humanoid robot by converting it into a humanoid robot with multiple features that can be inherited using ROS. This is a first of its kind approach wherein ROS is used as the development framework in conjunction with the main BBB controller and the software impregnated with Python libraries is used to integrate robotic functions. A full ROS computation is developed and a high level Application Programming Interface (API) usable by software utilizing ROS services is also developed. In this revised two-legged-humanoid robot, USB2Dynamixel connector is used to operate the Dynamixel AX-12A actuators through the Wi-Fi interface of the fortified BBB. An accelerometer sensor supports balancing of the robot, and updates data to the BBB periodically. An Infrared (IR) sensor is used to detect obstacles. This dynamic model is used to actuate the motors mounted on the robot leg thereby resulting in a swing-stance period of the legs for a stable forward movement of the robot. The maximum walking speed of the robot is 0.5 feet/second, beyond this limit the robot becomes unstable. The angle at which the robot leans is governed by the feedback from the accelerometer sensor, which is 20 degrees. If the robot tilts beyond a specific degree, then it would come back to its standstill position and stop further movement. When the robot moves forward, the IR sensors sense obstacles in front of the robot. If an obstacle is detected within 35 cm, then the robot stops moving further. Implementation of ROS on top of the BBB (by replacing CM530 controller with the BBB) and using feedback controls from the accelerometer and IR sensor to control the two-legged robotic movement are the novelties of this work

A Novel 3D printed leg design for a Biped Robot

This paper proposes a novel leg design for a humanoid robot that can be 3D printed. More explicitly, the efforts of this paper are to bring some of the more complex leg designs seen in large scale bipedal robot into the realm of smaller bipeds while still allowing for it to be easily reproducible or modified. In order to accomplish this 3D printing technology was utilized, as well as an iterative design process. An ankle and knee powered by linear actuators were first constructed to test the conceptual design of the leg. This was followed by a complete leg design with improved ankle and knee, along with the rest of the leg

Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks

Realistic behaviour simulation of a humanoid robot

This paper describes a humanoid robot simulator with realistic dynamics. As simulation is a powerful tool for speeding up the control software development, the proposed accurate simulator allows to fulfil this goal. The simulator is based on the Open Dynamics Engine and GLScene graphics library, providing instant visual feedback. User is able to test any control strategy without bringing damage to the real robot in the early stages of the development. The proposed simulator also captures some characteristics of the environment that are important and allows to test controllers without access to the real hardware. Experimental and simulator results are presented in order to validate the proposed simulator

Humanoid robot simulator: a realistic dynamics approach

This paper describes a humanoid robot simulator with realistic dynamics. As simulation is a powerful tool for speeding up the control software development, the suggested accurate simulator allows to accomplish this goal. The simulator, based on the Open Dynamics Engine and GLScene graphics library, provides instant visual feedback and allows the user to test any control strategy without damaging the real robot in the early stages of the development. The proposed simulator also captures some characteristics of the environment that are important and allows to test controllers without access to the real hardware. Experimental results are shown that validate this approach

A literature review on the optimization of legged robots

Over the last two decades the research and development of legged locomotion robots has grown steadily. Legged systems present major advantages when compared with ‘traditional’ vehicles, because they allow locomotion in inaccessible terrain to vehicles with wheels and tracks. However, the robustness of legged robots, and especially their energy consumption, among other aspects, still lag behind mechanisms that use wheels and tracks. Therefore, in the present state of development, there are several aspects that need to be improved and optimized. Keeping these ideas in mind, this paper presents the review of the literature of different methods adopted for the optimization of the structure and locomotion gaits of walking robots. Among the distinct possible strategies often used for these tasks are referred approaches such as the mimicking of biological animals, the use of evolutionary schemes to find the optimal parameters and structures, the adoption of sound mechanical design rules, and the optimization of power-based indexes