Optimal path and gait generations simultaneously of a six-legged robot using a GA-Fuzzy approach

This paper describes a new method for generating optimal path and gait simultaneously of a six-legged robot using a combined GA-fuzzy approach. The problem of combined path and gait generations involves three steps, namely determination of vehicle's trajectory, foothold selection and design of a sequence of leg movements. It is a complicated task and no single traditional approach is found to be successful in handling this problem. Moreover, the traditional approaches do not consider optimization issues, yet they are computationally expensive. Thus, the generated path and gaits may not be optimal in any sense. To solve such problems optimally, there is still a need for the development of an efficient and computationally faster algorithm. In the proposed genetic-fuzzy approach, optimal path and gaits are generated by using fuzzy logic controllers (FLCs) and genetic algorithms (GAs) are used to find optimized FLCs. The optimization is done off-line on a number of training scenarios and optimal FLCs are found. The hexapod can then use these GA-tuned FLCs to navigate in test-case scenarios

Data-Driven Methods to Build Robust Legged Robots

For robots to ever achieve signicant autonomy, they need to be able to mitigate performance loss due to uncertainty, typically from a novel environment or morphological variation of their bodies. Legged robots, with their complex dynamics, are particularly challenging to control with principled theory. Hybrid events, uncertainty, and high dimension are all confounding factors for direct analysis of models. On the other hand, direct data-driven methods have proven to be equally dicult to employ. The high dimension and mechanical complexity of legged robots have proven challenging for hardware-in-the-loop strategies to exploit without signicant eort by human operators. We advocate that we can exploit both perspectives by capitalizing on qualitative features of mathematical models applicable to legged robots, and use that knowledge to strongly inform data-driven methods. We show that the existence of these simple structures can greatly facilitate robust design of legged robots from a data-driven perspective. We begin by demonstrating that the factorial complexity of hybrid models can be elegantly resolved with computationally tractable algorithms, and establish that a novel form of distributed control is predicted. We then continue by demonstrating that a relaxed version of the famous templates and anchors hypothesis can be used to encode performance objectives in a highly redundant way, allowing robots that have suffered damage to autonomously compensate. We conclude with a deadbeat stabilization result that is quite general, and can be determined without equations of motion.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155053/1/gcouncil_1.pd

Assessment and understanding of unilateral trans-tibial amputee gait using principal component analysis and discriminant function analysis

The general aim of this thesis was to develop analytical techniques for the assessment and understanding of lower-limb amputee (LLA) gait. The number of individuals with lower limb amputation (LLA) worldwide is growing and being able to optimise rehabilitation and prosthetic prescriptions are becoming more important. Gait analysis may be able to inform these processes, in particular at the individual level. In study one, a machine learning algorithm was developed and optimised using Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA) to distinguish between barefoot and shod running. An iterative process was used to optimise the algorithm, exploring all possible iterations of ten individuals out of twenty, finding the combination of people with the greatest generic features and thus the lowest error rate for classification. The outcome showed 93.5% classification accuracy between barefoot and shod running. This study demonstrated that an iteration procedure could optimise a machine learning algorithm to overcome the issues of overfitting, which is particularly useful when working with a small sample size as is common in gait analysis. In study two, PCA and DFA were used to identify differences between the gait of individuals with unilateral trans-tibial amputation (UTTA) and able-bodied individuals. Different approaches were explored, establishing that PCA conducted on normalised temporal-waveforms yielded the best outcome. Results revealed that UTTA and able-bodied gait differed with regards to certain biomechanical variables, providing a better understanding of LLA function. Although differences between individuals with LLA and able-bodied individuals have previously been investigated, this study demonstrates that using multivariate statistical analyses a vast number of variables can be investigated simultaneously, identifying the hierarchy of variables and thus which need to be targeted during treatment. Clinical diagnosis is based on individual patients, thus in study three PCA was used to determine whether one individual with a UTTA displayed unique gait characteristics when compared to a group of able-bodied individuals. Both covariance and correlation matrices were used during PCA, providing information about variation and magnitude of the data, respectively. Results revealed that each individual with UTTA has subject-specific gait characteristics, which highlights that this method can be used to identify variables which can be targeted during treatment. In the fourth and final study, PCA was used to understand the effects of attempted symmetry on dynamic stability of individuals with UTTA. Although in rehabilitation symmetrical gait is often sought for since asymmetrical gait is said to cause long term adverse effects, results revealed that asymmetry might be playing a functional role and in fact aids better stability in UTTA gait. This outcome may suggest that after a certain symmetry has been reached, the target of rehabilitation may need to be reconsidered to aim for better stability. In conclusion, multivariate statistical analysis could be used to assess and understand LLA function. In a clinical setting, the ability to identify important variables during a task, particularly at patient-specific level has the potential to improve the development of treatment recommendations. Prosthetic prescription and rehabilitation processes can be tailored and in turn the outcome may be more successful which could increase the likelihood of independent living of patients and therefore improve the quality of life of individuals with LLA