Intelligent Approaches in Locomotion - A Review <s> Fuzzy logic systems <s> Bipedal locomotion is an important hallmark of human evolution. Despite of complex control systems, human locomotion is characterized by smooth, regular, and repeating movements. Therefore, there is the potential for applying human locomotion strategies and any knowledge available to the biped control. In order to make the most use of the information available, a linguistic-numerical integration-based biped control method is proposed in this paper. The numerical data from biped measuring instruments, and the linguistic rules obtained from intuitive walking knowledge and biomechanics study have been classified into four categories: direct rules, direct data, indirect rules, and indirect data. Based on inverse learning and data fusion theory, two simple and intuitive integration schemes are proposed to integrate linguistic and numerical information with various forms, such as direct and indirect. One is neurofuzzy-based integration, and another is fuzzy rules extraction-based integration. The simulation results show that the biped gait and joint control performance can be significantly improved by the prescribed synergy method-based neurofuzzy gait synthesis and fuzzy rules extraction-based joint control strategies using linguistic and numerical integrated information. <s> BIB001 </s> Intelligent Approaches in Locomotion - A Review <s> Fuzzy logic systems <s> 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. <s> BIB002 </s> Intelligent Approaches in Locomotion - A Review <s> Fuzzy logic systems <s> Abstract How to learn from both expert knowledge and measurement-based information for a robot to acquire perception and motor skills is a challenging research topic in the field of autonomous robotic systems. For this reason, a general GA (genetic algorithm)-based fuzzy reinforcement learning (GAFRL) agent is proposed in this paper. We first characterize the robot learning problem and point out some major issues that need to be addressed in conjunction with reinforcement learning. Based on a neural fuzzy network architecture of the GAFRL agent, we then discuss how different kinds of expert knowledge and measurement-based information can be incorporated in the GAFRL agent so as to accelerate its learning. By making use of the global optimization capability of GAs, the GAFRL can solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can evaluate the candidate solutions regularly even during the periods without external feedback from the environment. This can guide GAs to perform a more effective global search. Finally, different types of GAFRL agents are constructed and verified using the simulation model of a physical biped robot. <s> BIB003 </s> Intelligent Approaches in Locomotion - A Review <s> Fuzzy logic systems <s> Trunk motions are typically used in biped robots to stabilize the locomotion. However, they can be very large for some leg trajectories unless they are carefully designed. This paper proposes a fuzzy-logic zero-moment-point (ZMP) trajectory generator that would eventually reduce the swing motion of the trunk significantly even though the leg trajectory is casually designed, for example, simply to avoid obstacles. The fuzzy-logic ZMP trajectory generator uses the leg trajectory as an input. The resulting ZMP trajectory is similar to that of a human one and continuously moves forward in the direction of the locomotion. The trajectory of the trunk to stabilize the locomotion is determined by solving a differential equation with the ZMP trajectory and the leg trajectory known. The proposed scheme is simulated on a 7-DOF biped robot in the sagittal plane. The simulation results show that the ZMP trajectory generated by the proposed fuzzy-logic generator increases the stability of the locomotion and thus reduces the motion range of the trunk significantly. <s> BIB004 </s> Intelligent Approaches in Locomotion - A Review <s> Fuzzy logic systems <s> This paper presents a general fuzzy reinforcement learning (FRL) method for biped dynamic balance control. Based on a neuro fuzzy network architecture, different kinds of expert knowledge and measurement-based information can be incorporated into the FRL agent to initialise its action network, critic network and/or evaluation feedback module so as to aecelerate its learning. The proposed FRL agent is constructed and verified using the simulation model of a physical biped robot. The sinmtation analysis shows that by incorporation of the human intuitive balancing knowledge and walking evaluation knowledge, the FRL agent's learning rate for side-to-side and front-to-back balance of the simulated biped can be improved. We also demonstrate that it is possible for a biped robot to start its walking with a priori knowledge and then learn to improve its behaviour with the FRL agents. <s> BIB005 </s> Intelligent Approaches in Locomotion - A Review <s> Fuzzy logic systems <s> Abstract Gait generation for legged vehicles has since long been considered as an area of keen interest by the researchers. Soft computing is an emerging technique, whose utility is more stressed, when the problems are ill-defined, difficult to model and exhibit large scale solution spaces. Gait generation for legged vehicles is a complex task. Therefore, soft computing can be applied to solve it. In this work, gait generation problem of a two-legged robot is modeled using a fuzzy logic controller (FLC), whose rule base is optimized offline, using a genetic algorithm (GA). Two different GA-based approaches (to improve the performance of FLC) are developed and their performances are compared to that of manually constructed FLC. Once optimized, the FLCs will be able to generate dynamically stable gait of the biped. As the CPU-time of the algorithm is found to be only 0.002 s in a P-III PC, the algorithm is suitable for on-line (real-time) implementations. <s> BIB006 </s> Intelligent Approaches in Locomotion - A Review <s> Fuzzy logic systems <s> This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot. <s> BIB007
A zero moment point controlled biped was developed in BIB004 , where in order to improve the gait, a fuzzy logic controller was used to vary the ZMP position in the support rectangle of the foot. This was done to match the shifting in real human gaits from the rear to the front of the foot. After simulations, this technique resulted in a more desirable gait with less trunk swing. In BIB002 fuzzy logic controllers were used for path planning, ditch crossing, and turning for a hexapod. The controllers processed ditch distance and angle information, and outputted actions for each leg. Initially, the rules were specified by the author, but it was assumed that a subset could be more effective. To achieve this, GAs were used to prune the rules, using a fitness function developed to minimize travel time and maximize walking efficiency. Through simulation it was found that, whatever the composition of the original rule set, the GA always improved the fitness. The approach was extended in BIB006 to control a biped walking up stairs. Two fuzzy logic controllers were used, one to analyse the current system state, and one to prescribe actions based on this state. A ZMP based criterion was employed to monitor the success in creating an effective climbing gait. An author specified rule set and GA pruned subset were compared as in BIB002 , plus a fully GA determined set. The fully GA specified set was found to be superior to the GA pruned set, which in turn was superior to the author set. A biped ditch-crossing fuzzy logic algorithm was developed in BIB007 (also mentioned in sections 2 and 4), where the gait was largely constructed analytically, but two fuzzy logic controllers were used for four free parameters. The rule sets were specified by GA and the best solution was found to be more stable and efficient than a fully analytical approach, but slightly worse than a NN based one. Linguistic rules and data based rules were fused in BIB001 to produce bipedal control, where an expert based fuzzy rules system was combined with a data based neuro-fuzzy network to produce a final control value. In BIB005 , BIB003 an actor-critic system was used for on-line learning. The actor and critic were constructed using neuro-fuzzy networks, and had layers with neurons corresponding to system states. The architecture allowed learning algorithms to be applied to adjust the parameters of the fuzzy system. The classification of the fuzzy system was author specified and therefore the behaviour was interpretable. An external error reinforcement signal with fuzzy encoding was also used, again allowing expert knowledge to be incorporated and this way reducing the training time considerably. In BIB006 comparisons were made for the GA fuzzy technique against others. The authors cited the ability to optimize the system as preferable over more restrictive analytical techniques. Compared to NNs, the authors valued the ease of interpretation of the rule based system and argued their implementation (using a GA) had better search of the solution space than the one in BIB005 . However, it should be noted that a GA version of BIB005 was presented in BIB003 .