Kinematic Design Analysis and Optimization of Mobility System Using MATLAB

209-212In this research work, several sophisticated types of equipment and automation have been studied, points taken and considered to realise the locomotion of modern territory of all uneven environments. One of the most main mission and structure of this study is the preferred simplicity of the bipedal walking locomotion system. The study included from simple to complicated legs as like single-legged, like humanoid and up to sixteen legs like a caterpillar. Most of the bipedal walking robots are with research study and we concentrated to emphasizes the significance of robotic legged motion stability in the compact. These bipedal walking robots can walk on rough surfaces, turn efficiently and climb staircase if needed. In particular, a suitable bipedal walking model having an upper link and a lower link will make the system to the desired motions, which has been experimentally exposed to provide a stable walking system. The MATLAB software tool is used to optimize the mechanical constraints and to compare, analyse and investigate the influence of motion stability. The simulation results show a possible performance of projected leg bipedal walking mechanism

Design and Trajectory Planning of Bipedal Walking Robot with Minimum Sufficient Actuation System

This paper presents a new type of mechanism and trajectory planning strategy for bipedal walking robot. The newly designed mechanism is able to improve the performance of bipedal walking robot in terms of energy efficiency and weight reduction by utilizing minimum number of actuators. The usage of parallelogram mechanism eliminates the needs of having an extra actuator at the knee joint. This mechanism works together with the joint space trajectory planning in order to realize straight legged walking which cannot be achieved by conventional inverse kinematics trajectory planning due to the singularity. The effectiveness of the proposed strategy is confirmed by computer simulation results

Kinematic Design Analysis and Optimization of Mobility System Using MATLAB

In this research work, several sophisticated types of equipment and automation have been studied, points taken and considered to realise the locomotion of modern territory of all uneven environments. One of the most main mission and structure of this study is the preferred simplicity of the bipedal walking locomotion system. The study included from simple to complicated legs as like single-legged, like humanoid and up to sixteen legs like a caterpillar. Most of the bipedal walking robots are with research study and we concentrated to emphasizes the significance of robotic legged motion stability in the compact. These bipedal walking robots can walk on rough surfaces, turn efficiently and climb staircase if needed. In particular, a suitable bipedal walking model having an upper link and a lower link will make the system to the desired motions, which has been experimentally exposed to provide a stable walking system. The MATLAB software tool is used to optimize the mechanical constraints and to compare, analyse and investigate the influence of motion stability. The simulation results show a possible performance of projected leg bipedal walking mechanism

Bipedal walking trajectory energy minimization through a learned hip height trajectory

This thesis describes methods used to optimize energy consumption of an offine bipedal walking trajectories through hip height control. The experiments were carried out on a miniature humanoid robot within the simulation environment Webots. Zero Moment Point (ZMP) preview control methods were implemented in Matlab to produce a stable walking trajectory for the robot with a fixed hip height. The hip height trajectory was then developed using an observation based Q-learning method that consider both stability and energy consumption. Through the Q-learning methods there was approximately a 9% decrease in the average energy consumption. Additionally, an increase in stability was observed.M.S., Mechanical Engineering and Mechanics -- Drexel University, 201

Rich and Robust Bio-Inspired Locomotion Control for Humanoid Robots

Bipedal locomotion is a challenging task in the sense that it requires to maintain dynamic balance while steering the gait in potentially complex environments. Yet, humans usually manage to move without any apparent difficulty, even on rough terrains. This requires a complex control scheme which is far from being understood. In this thesis, we take inspiration from the impressive human walking capabilities to design neuromuscular controllers for humanoid robots. More precisely, we control the robot motors to reproduce the action of virtual muscles commanded by stimulations (i.e. neural signals), similarly to what is done during human locomotion. Because the human neural circuitry commanding these muscles is not completely known, we make hypotheses about this control scheme to simplify it and progressively refine the corresponding rules. This thesis thus aims at developing new walking algorithms for humanoid robots in order to obtain fast, human-like and energetically efficient gaits. In particular, gait robustness and richness are two key aspects of this work. In other words, the gaits developed in the thesis can be steered by an external operator, while being resistant to external perturbations. This is mainly tested during blind walking experiments on COMAN, a 95 cm tall humanoid robot. Yet, the proposed controllers can be adapted to other humanoid robots. In the beginning of this thesis, we adapt and port an existing reflex-based neuromuscular model to the real COMAN platform. When tested in a 2D simulation environment, this model was capable of reproducing stable human-like locomotion. By porting it to real hardware, we show that these neuromuscular controllers are viable solutions to develop new controllers for robotics locomotion. Starting from this reflex-based model, we progressively iterate and transform the stimulation rules to add new features. In particular, gait modulation is obtained with the inclusion of a central pattern generator (CPG), a neural circuit capable of producing rhythmic patterns of neural activity without receiving rhythmic inputs. Using this CPG, the 2D walker controllers are incremented to generate gaits across a range of forward speeds close to the normal human one. By using a similar control method, we also obtain 2D running gaits whose speed can be controlled by a human operator. The walking controllers are later extended to 3D scenarios (i.e. no motion constraint) with the capability to adapt both the forward speed and the heading direction (including steering curvature). In parallel, we also develop a method to automatically learn stimulation networks for a given task and we study how flexible feet affect the gait in terms of robustness and energy efficiency. In sum, we develop neuromuscular controllers generating human-like gaits with steering capabilities. These controllers recruit three main components: (i) virtual muscles generating torque references at the joint level, (ii) neural signals commanding these muscles with reflexes and CPG signals, and (iii) higher level commands controlling speed and heading. Interestingly, these developments target humanoid robots locomotion but can also be used to better understand human locomotion. In particular, the recruitment of a CPG during human locomotion is still a matter open to debate. This question can thus benefit from the experiments performed in this thesis

Towards Robust Bipedal Locomotion:From Simple Models To Full-Body Compliance

Thanks to better actuator technologies and control algorithms, humanoid robots to date can perform a wide range of locomotion activities outside lab environments. These robots face various control challenges like high dimensionality, contact switches during locomotion and a floating-base nature which makes them fall all the time. A rich set of sensory inputs and a high-bandwidth actuation are often needed to ensure fast and effective reactions to unforeseen conditions, e.g., terrain variations, external pushes, slippages, unknown payloads, etc. State of the art technologies today seem to provide such valuable hardware components. However, regarding software, there is plenty of room for improvement. Locomotion planning and control problems are often treated separately in conventional humanoid control algorithms. The control challenges mentioned above are probably the main reason for such separation. Here, planning refers to the process of finding consistent open-loop trajectories, which may take arbitrarily long computations off-line. Control, on the other hand, should be done very fast online to ensure stability. In this thesis, we want to link planning and control problems again and enable for online trajectory modification in a meaningful way. First, we propose a new way of describing robot geometries like molecules which breaks the complexity of conventional models. We use this technique and derive a planning algorithm that is fast enough to be used online for multi-contact motion planning. Similarly, we derive 3LP, a simplified linear three-mass model for bipedal walking, which offers orders of magnitude faster computations than full mechanical models. Next, we focus more on walking and use the 3LP model to formulate online control algorithms based on the foot-stepping strategy. The method is based on model predictive control, however, we also propose a faster controller with time-projection that demonstrates a close performance without numerical optimizations. We also deploy an efficient implementation of inverse dynamics together with advanced sensor fusion and actuator control algorithms to ensure a precise and compliant tracking of the simplified 3LP trajectories. Extensive simulations and hardware experiments on COMAN robot demonstrate effectiveness and strengths of our method. This thesis goes beyond humanoid walking applications. We further use the developed modeling tools to analyze and understand principles of human locomotion. Our 3LP model can describe the exchange of energy between human limbs in walking to some extent. We use this property to propose a metabolic-cost model of human walking which successfully describes trends in various conditions. The intrinsic power of the 3LP model to generate walking gaits in all these conditions makes it a handy solution for walking control and gait analysis, despite being yet a simplified model. To fill the reality gap, finally, we propose a kinematic conversion method that takes 3LP trajectories as input and generates more human-like postures. Using this method, the 3LP model, and the time-projecting controller, we introduce a graphical user interface in the end to simulate periodic and transient human-like walking conditions. We hope to use this combination in future to produce faster and more human-like walking gaits, possibly with more capable humanoid robots