This paper proposes the control system for 3-D locomotion of a humanoid biped robot based on a biological approach. The muscular system in the human body and the neural oscillator for generating locomotion signals are adapted in this paper. We extend the neuro-locomotion system for modeling a multiple neuron system, where motoric neurons represent the muscular system and sensoric neurons represent the sensor system inside the human body. The output signals from coupled neurons representing the angle joint level are controlled by gain neurons that represent the energy burst for driving the joint in each motor. The direction and the length of step in robot locomotion can be adjusted by command neurons. In order to form the locomotion pattern, we apply multiobjective evolutionary computation to solve the multiobjective problem when optimizing synapse weights between the motoric neurons. We use recurrent neural network (RNN) for the stabilization system required for supporting locomotion. RNN generates a dynamic weight synapse value between the sensoric neuron and the motoric neuron. The effectiveness of our system is demonstrated in open dynamic engine computer simulation and in a real robot application that has 12 degrees of freedom (DoFs) in legs and four DoFs in hands.

There are currently many quadruped robots suited to a wide range of applications, but traversing some terrains, such as vertical ladders, remains an open challenge. There is still a need to develop adaptive robots that can walk and climb efficiently. This paper presents an adaptive quadruped robot that, by mimicking feline structure, supports several novel capabilities. We design a novel paw structure and several point-cloud-based sensory structures incorporating a quad-composite time-of-flight sensor and a dual-laser range finder. The proposed robot is equipped with physical and cognitive capabilities which include: 1) a dynamic-density topological map building with attention model, 2) affordance perception using the topological map, and 3) a neural-based locomotion model. The novel capabilities show strong integration between locomotion and internal–external sensory information, enabling short-term adaptations in response to environmental changes. The robot performed well in several situations: walking on natural terrain, walking with a leg malfunction, avoiding a sudden obstacle, climbing a vertical ladder. Further, we consider current problems and future development.

This article examines the importance of integrating locomotion and cognitive information for achieving dynamic locomotion from a viewpoint combining biology and ecological psychology. We present a mammalian neuromusculoskeletal model from external sensory information processing to muscle activation, which includes: 1) a visual-attention control mechanism for controlling attention to external inputs; 2) object recognition representing the primary motor cortex; 3) a motor control model that determines motor commands traveling down the corticospinal and reticulospinal tracts; 4) a central pattern generation model representing pattern generation in the spinal cord; and 5) a muscle reflex model representing the muscle model and its reflex mechanism. The proposed model is able to generate the locomotion of a quadruped robot in flat and natural terrain. The experiment also shows the importance of a postural reflex mechanism when experiencing a sudden obstacle. We show the reflex mechanism when a sudden obstacle is separately detected from both external (retina) and internal (touching afferent) sensory information. We present the biological rationale for supporting the proposed model. Finally, we discuss future contributions, trends, and the importance of the proposed research.

This paper presents a natural mechanism of the human brain for generating a dynamic path planning in 3-D rough terrain. The proposed paper not only emphasizes the inner state process of the neuron but also the development process of the neurons in the brain. There are two algorithm processes in this proposed model, the forward transmission activity for constructing the neuron connections to find the possible way and the synaptic pruning activity with backward neuron transmission for finding the best pathway from current position to target position and reducing inefficient neuron with its synaptic connections. In order to respond and avoid the unpredictable obstacle, dynamic path planning is also considered in this proposed model. An integrated system for applying the proposed model in the real cases is also presented. In order to prove the effectiveness of the proposed model, we applied it in the pathway of a four-legged robot on rough terrain in both computer simulation and real cases. Unpredictable collision is also performed in those experiments. The model can find the best pathway and facilitate the safe movement of the robot. When the robot found an unpredictable collision, the path planner dynamically changed the pathway. The proposed path planning model is capable to be applied in further advance implementation.

Cyber-physical-social systems (CPSS) are expected to support telemedicine and will be the future of independent rehabilitation. However, a connection between the physical and cognitive as one of the social aspects has not been adequately supported. This paper proposes a multiscopic CPSS framework to investigate this issue by developing hand–object interaction (HOI) recognition with visual attention for the block-design test (BDT). We use three vision systems to extract hand features from block interaction. First, a hand-tracking vision is used to collect the hand-skeletal data and finger joint angle feature. We estimate the physical handling postures at the microscopic level. Second, an egocentric vision with an eye tracker is used to get hand and eye movement at the mesoscopic level. We analyze hand-eye coordination through hand gestures and the focus of visual attention when the hand interacts with the block. Third, an evaluation vision is used to classify the color features in each block at the macroscopic level. The system can recognize whether the design matches the task given. The eight-block design test with two scenarios demonstrates that the system can successfully measure human behavior from physical to cognitive during BDT. We investigate the relationship between physical and cognitive evaluation through the experiment in eight healthy persons. The regression and correlation analyses between the dominant and non-dominant hands show that evaluation indices – skewness-kurtosis of handling posture, attention to pattern, and attention to blocks – could indicate improvement during BDT. We believe this study will benefit clinicians and researchers by providing information unavailable in clinical settings. Code and datasets are available online at https://github.com/anom-tmu/bdt-multiscopic.

We present an evolving neural oscillator based bio-inspired biped robot locomotion for minimizing the constraints during the locomotion process. Sensory-motor coordination model is represented by the interconnection between motor neurons and sensory neurons. An evolutionary computation technique is applied for reconstructing the number of joints and the number of neurons in each joint depending on the environmental condition. In this system, either the number of joints, or the number of neurons, or the interconnection structure are dynamically changed depending on the conditions acquired from the sensors that equipped in the robot. Bacterial programming is inspired by the evolutionary process of bacteria including bacterial mutation and gene transfer. This system is applied in computer simulation for realizing the optimization process and the optimized structure is applied in a small humanoid robot. In experiments, we run the robot in several different environmental conditions. Different neuron structures are resulted depending on the environmental conditions. The proposed tree structure based optimization strategy can simplify the sensory-motor interconnection structure.