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In order to solve the problem of autonomous recognition of hexapod robot and realize the intelligent and humanized development of robot, OpenMV is taken as the main platform, hexapod robot is taken as the main machine carrier, Python is taken as the main development language, C language is taken as the auxiliary development language, and the reasonable application of image processing technology is added. A simple visual recognition system based on OpenMV is designed to realize the application of visual recognition.

With the advent of significant collaborations between researchers who study insect walking and robotics engineers interested in constructing adaptive legged robots, insect walking is once again poised to make a more significant scientific contribution than the numbers of participants in the field might suggest. This review outlines current knowledge of the physiological basis of insect walking with an emphasis on recent new developments in biomechanics and genetic dissection of behavior, and the impact this knowledge is having on robotics. Engineers have begun to team with neurobiologists to build walking robots whose physical design and functional control are based on insect biology. Such an approach may have benefits for engineering, by leading to the construction of better-performing robots, and for biology, by allowing real-time and real-world tests of critical hypotheses about how locomotor control is effected. It is argued that in order for the new field of biorobotics to have significant influence it must adopt criteria for performance and an experimental approach to the development of walking robots.

This paper presents a novel CPG-based gait generation of the curved-leg hexapod robot that can enable smooth gait transitions between multi-mode gaits. First, the locomotion of the curved leg and instability during the gait transitions are analyzed. Then, a modified Hopf oscillator is applied in the CPG control, which can realize multiple gaits by adjusting a simple parameter. In addition, a smooth gait switching method is also proposed via smooth gait transition functions and gait planning. Tripod gait, quadruped gait, and wave gait are planned for the hexapod robot to achieve quick and stable gait transitions smoothly and continuously. MATLAB and ADAMS simulations and corresponding practical experiments are conducted. The results show that the proposed method can achieve smooth and continuous mutual gait transitions, which proves the effectiveness of the proposed CPG-based hexapod robot control.

Micro-hexapods, well-suited for navigating tight or uneven spaces and suitable for mass production, hold promise for exploration by robot groups, particularly in disaster scenarios. However, research on simultaneous localization and mapping (SLAM) for micro-hexapods has been lacking. Previous studies have not adequately addressed the development of SLAM systems considering changes in the body axis, and there is a lack of comparative evaluation with other movement mechanisms. This study aims to assess the influence of walking on SLAM capabilities in hexapod robots. Experiments were conducted using the same SLAM system and LiDAR on both a hexapod robot and crawler robot. The study compares map accuracy and LiDAR point cloud data through pattern matching. The experimental results reveal significant fluctuations in LiDAR point cloud data in hexapod robots due to changes in the body axis, leading to a decrease in map accuracy. In the future, the development of SLAM systems considering body axis changes is expected to be crucial for multi-legged robots like micro-hexapods. Therefore, we propose the implementation of a system that incorporates body axis changes during locomotion using inertial measurement units and similar sensors.

Detecting transportation pipeline leakage points within chemical plants is difficult due to complex pathways, multi-dimensional survey points, and highly dynamic scenarios. However, hexapod robots’ maneuverability and adaptability make it an ideal candidate for conducting surveys across different planes. The path-planning problem of hexapod robots in multi-dimensional environments is a significant challenge, especially when identifying suitable transition points and planning shorter paths to reach survey points while traversing multi-level environments. This study proposes a Particle Swarm Optimization (PSO)-guided Double Deep Q-Network (DDQN) approach, namely, the PSO-guided DDQN (PG-DDQN) algorithm, for solving this problem. The proposed algorithm incorporates the PSO algorithm to supplant the traditional random selection strategy, and the data obtained from this guided approach are subsequently employed to train the DDQN neural network. The multi-dimensional random environment is abstracted into localized maps comprising current and next level planes. Comparative experiments were performed with PG-DDQN, standard DQN, and standard DDQN to evaluate the algorithm’s performance by using multiple randomly generated localized maps. After testing each iteration, each algorithm obtained the total reward values and completion times. The results demonstrate that PG-DDQN exhibited faster convergence under an equivalent iteration count. Compared with standard DQN and standard DDQN, reductions in path-planning time of at least 33.94% and 42.60%, respectively, were observed, significantly improving the robot’s mobility. Finally, the PG-DDQN algorithm was integrated with sensors onto a hexapod robot, and validation was performed through Gazebo simulations and Experiment. The results show that controlling hexapod robots by applying PG-DDQN provides valuable insights for path planning to reach transportation pipeline leakage points within chemical plants.

This paper proposes a novel continuous footholds optimization method for legged robots to expand their walking ability on complex terrains. The algorithm can efficiently run onboard and online by using terrain perception information to protect the robot against slipping or tripping on the edge of obstacles, and to improve its stability and safety when walking on complex terrain. By relying on the depth camera installed on the robot and obtaining the terrain heightmap, the algorithm converts the discrete grid heightmap into a continuous costmap. Then, it constructs an optimization function combined with the robot's state information to select the next footholds and generate the motion trajectory to control the robot's locomotion. Compared with most existing footholds selection algorithms that rely on discrete enumeration search, as far as we know, the proposed algorithm is the first to use a continuous optimization method. We successfully implemented the algorithm on a hexapod robot, and verified its feasibility in a walking experiment on a complex terrain.

Based on the principle of bionic hexapod, a 3D virtual prototype model of the bionic hexapod robot and the contact model between its feet and the ground are established by using MSC.ADAMS mechanical dynamics software to study the motion of the bionic hexapod robot on the horizontal ground. And then, the kinematics analysis of a single leg of the robot is made to realize the overall motion control of the robot. This paper analyzes the gait principle of the bionic hexapod robot and introduces the gait of the robot used. By simulating the straight motion of the robot, the angular velocity and angular acceleration in the legs of the virtual prototype model are obtained. The study is a theoretical foundation for the design of the physical model and motion planning of a bionic hexapod robot.

The study delivers a cohesive system that combines structural stress investigation, navigational planning evaluation, and adaptive joint control to optimize hexapod effectiveness on hills, stairs, and uneven surfaces. The robot was developed through the iterative drafting technique and designed by assigning in PLA material. Structural examination with Finite Element Analysis (FEA) under 10 N and 20 N forces demonstrated a positive stress allocation and a safety factor of 2.8, combining compact development with durability. In the ROS/Gazebo exploration investigations utilizing global planners like A*, Dijkstra, RRT, and Artificial Potential Field (APF) in combination with a PID-driven local planner, A* as well as Dijkstra developed nearly the best pathways with 100% accuracy. This cut down on route variation by about 17% in comparison to RRT. RRT established confident that the exploration was always the same, but it established paths that were more lengthy and less smooth. APF, on the contrary, made paths that were smooth but less reliable due to the local minima. Adaptive synchronization for joint control quantitatively provided an improvement in joint angle stability, reducing oscillatory deviations by 12% and displacement errors by 15% relative to baseline controllers. The core novelty within this approach is the integrative methodology that will inherently synergize finite element structural analysis, comparative path planning, and Adaptive joint synchronization: presenting a comprehensive optimization strategy, new to hexapod robotics. Together, these advances allow for robust and efficient real-world deployment of hexapods. Future work will extend to hybrid learning-based planning, and sensor-driven dynamic adaptation.

This paper provides an adaptive robust control strategy for foot trajectory following control of hexapod robot on basis of the Udwadia–Kalaba theory. In this paper, the foot trajectory following control problem of the hexapod robot is transformed into the problem of solving the system control constraint force on basis of the Udwadia–Kalaba theory. Compared with the traditional control strategy, linearization or approximations are not required by using the Udwadia–Kalaba theory for nonlinear system such as the hexapod robot. Due to modeling error, measurement error and the change of working state, the system may have non-ideal initial conditions, vibration interference and other uncertain factors during operation, which affect the control accuracy. An adaptive robust controller is designed for solving uncertainties. Meanwhile, the stability is analyzed by using the second method of Lyapunov function. Finally, the accuracy and stability of the control method proposed are verified by establishing the leg model of the hexapod robot and conducting simulation analysis. The simulation results show that the provided adaptive control process has faster error convergence speed and response speed compared with the sliding mode control method.

In this paper, an adaptive locomotion control approach for a hexapod robot is proposed. Inspired from biological neuro control systems, a 3D two-layer artificial center pattern generator (CPG) network is adopted to generate the locomotion of the robot. The first layer of the CPG is responsible for generating several basic locomotion patterns and the functional configuration of this layer is determined through kinematics analysis. The second layer of the CPG controls the limb behavior of the robot to adapt to environment change in a specific locomotion pattern. To enable the adaptability of the limb behavior controller, a reinforcement learning (RL)-based approach is employed to tune the CPG parameters. Owing to symmetrical structure of the robot, only two parameters need to be learned iteratively. Thus, the proposed approach can be used in practice. Finally, both simulations and experiments are conducted to verify the effectiveness of the proposed control approach.