The multi-locomotion robot (MLR), including bionic insect microrobot, bionic animal robot and so on, should choose different locomotion modes according to the obstacles it faces. However, under different locomotion modes, the power consumption, moving speed, and falling risk of MLR are different, and in most cases, they are mutually exclusive. This paper proposes a path planning algorithm for MLR based on a multi-objective genetic algorithm with elitist strategy (MLRMOEGA), which has four optimization objectives: power consumption, time consumption, path falling risk, and path smoothness. We propose two operators: a map analysis operator and a population diversity expansion operator, to improve the global search ability of the algorithm and solve the problem so that it is easy to fall into the local optimal solution. We conduct simulations on MATLAB, and the results show that the proposed algorithm can effectively optimize the objective function value compared with the traditional genetic algorithm under the equal weight of the four optimization objectives, and, under alternative weights, the proposed algorithm can effectively generate the corresponding path of the decision maker’s intention under the weight of preference. Compared with the traditional genetic algorithm, the global search ability is improved effectively.

This article mainly introduces a dynamically stabilized and balanced point-footed bipedal robot, which we named MPLBR (Multi-locomotion Parallel-Legged Bipedal Robot). The robot structure is designed with a symmetric architecture, including six motors and a five-link double crank mechanism for extending the legs. Based on the inverted pendulum model, a robot control process framework is proposed using the virtual model control (VMC) algorithm and the decoupling balance control algorithm, combined with robot gait planning and state detection, to achieve stable walking and hopping control of the robot. Through simulation experiments and physical prototype experiments to verify the algorithm, the robot can achieve stable walking control and bounce motion modes.

To control dual arm robot made by ourselves using voice based on ROS, voice recognition is realized by node includes the bag named speech command recognition made by iFLYTEK. The robot entity model is built in SolidWorks and written to URDF file as STL format. The robot motion control file is configured through Moveit!. The communication between ROS and STM32 which used to control solid robot is completed by using serial port and the joint motion angle of the command is sent to STM32 from Moveit through Arbotix simulation driver. The feasibility of the voice control system is verified by voice experiment.

In order to achieve the normal walking of hemiplegic patients, this paper proposes a single-legged exoskeleton robot according to the bionics principle, and presents an adaptive adjustment strategy for walking characteristics. The least square regression analysis is used to fit the angle data of healthy leg joints by cubic polynomials, and then the parametric design of the fitted curve is carried out to obtain the influence of the user’s stride frequency and stride length on the joint angle, so that the gait of the exoskeleton can be adjusted in real time according to the stride length and stride frequency of the healthy leg to realize normal walking. In order to verify the effectiveness of the adaptive adjustment strategy proposed in this paper, the angle of leg joints under normal gait is collected in advance. In addition, an adult male is chosen as the subject to walk on the horizontal ground wearing the single-legged exoskeleton as the experiment. The experimental results show that the designed exoskeleton is reasonable, and the adaptive adjustment strategy proposed in this paper can make the exoskeleton adapt well and follow the gait of healthy legs to achieve a more natural walking state.

This paper presents the design and control of a lightweight three degrees of freedom robotic arm based on a load-adaptive hoisting mechanism. The proposed design integrates a spring-loaded rope and a variable radius reel into the gripper, enabling efficient load adaptability with minimal structural complexity. By leveraging this mechanism, the robotic arm achieves significant weight reduction while maintaining robust performance under variable payloads. The study includes a comprehensive analysis of the system’s kinematics and dynamics, focusing on the interaction between the adaptive gripper and the arm structure. A prototype of the robotic arm was developed and experimentally tested to validate its functionality.