# State of the Art: Bipedal Robots for Lower Limb Rehabilitation
## 2 Bipedal Locomotion
### Motion Planning  
Like humans, the bipedal robot walks by alternating legs. To achieve a stable walk, a reasonable gait must be planned. The quality of the gait will directly affect the stability of walking, the size of the driving torque, and aesthetics. Similarly, in the application of rehabilitation robots, motion planning is also needed for coordination with the movement of people. At present, five kinds of gait planning methods exist. * Gait Planning Based on Bionic Kinematics  
The purpose of bipedal locomotion is to imitate human walking characteristics, so gait planning can learn from the human bionic gait. Human motion capture data (HMCD) [57; 58] can plan complex and diverse actions. As the reference action is generated by the human body, the planning method becomes simpler. Honda's research team generated ASIMO's gait based on the analysis of the mutual restraint and coordination between the joints of human lower limbs. However, the feasibility of the method entirely depends on the walking data, and accurately and completely measuring or recording the characteristics of human walking data is difficult with the existing instrument. So obtaining gait data for robots is challenging. * Gait Planning Based on Stability  
The study of human dynamic walking shows that the balance of human walking is not due to the relatively large soles of the feet, but the complex coordination of the body. To achieve a stable gait, a large number of research scholars have proposed stabilization criteria for different models, such as center of pressure criterion (CoP) [59], foot rotation indicator (FRI) [60], and zero moment point (ZMP) criterion. Among them, the ZMP stability criterion proposed by [PERSON] is one of the most widely used. Honda's ASIMO used the ZMP theory to achieve walking and balance of bipedal robots. * Model-Based Gait Planning  
The model-based gait planning method mainly includes the multi-link model (Figure 4a) and the inverted pendulum model (Figure 4b) [61; 62]. The gait planning of the multi-link model has been successfully applied to the Honda robot. This gait planning method is based on the ideal model while ignoring the radial and lateral motion of the coupling. Therefore, when the planned gait is applied to the actual prototype, an unavoidable error occurs. However, this is one of the most widely used methods. The adaptability of the data to the actual environment, and the robustness to the interference, need to be improved in the future. * Gait Planning Based on Energy Consumption Optimization  
People walk with the lowest energy consumption and remain stable as result of the evolutionary process. Therefore, planning a gait with the energy-optimized method can make bipedal locomotion more human. [PERSON] first analyzed the torque required to achieve the joint movements from the energy point of view [65]. [PERSON] generates the walking gait of the biped robot Bonten-Maru for the optimization target with the lowest energy and the minimum variation in joint torque [66]. The energy optimization method can fully develop the performance of bipedal robots and reduce the system requirements. However, the calculation of the optimal planning is complex and cannot be implemented in real time. * Gait Planning Based on Intelligent Algorithm  
Intelligent techniques, such as neural networks, fuzzy logic, and genetic algorithms, have powerful self-learning, adaptive, and fault-tolerant capabilities that have attracted many robotics researchers to apply them to gait planning. In 1992, each joint of robot SD-2 was represented by a joint neuron [67]. In the study, the neural network obtained the relationship between the foot force and the angle of the corresponding joint angle adjustment. In addition to the neural network, due to the advantages of fuzzy logic in terms of knowledge expression, many scholars apply it to the generation and control of the biped gait.