Optimization method for human-robot command combinations of hexapod robot based on multi-objective constraints

Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting drivers in generating human-robot command combinations. Furthermore, human-robot state constraints are quantified as geometric constraints on robot motion and driver fatigue constraints. By optimizing and filtering the feasible set of human-robot commands based on human-robot state constraints, instruction combinations are formed and recommended to the driver in real-time, thereby enhancing the efficiency and safety of human-machine coordination. To validate the effectiveness of the proposed method, a remote human-robot collaborative driving control system based on wearable devices is designed and implemented. Experimental results demonstrate that drivers utilizing the human-robot command recommendation system exhibit significantly improved robot walking stability and reduced collision rates compared to individual driving.

Improved Double Deep Q-Network Algorithm Applied to Multi-Dimensional Environment Path Planning of Hexapod Robots

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.

The Influence of Micro-Hexapod Walking-Induced Pose Changes on LiDAR-SLAM Mapping Performance

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.

Dynamic turning and running of a hexapod robot using a separated and laterally arranged two-leg model

We report on the development of separated and laterally arranged two-leg (SLTL) models with/without differentiated leg properties and their use as the dynamic running and turning templates for a hexapod robot. The laterally arranged two-leg morphology enables differential driving for turning. The differentiable leg settings, such as stiffness, enables the model to adopt unbalanced leg arrangements of empirical legged gaits, such as a tripod gait, into consideration. The fixed-point motion of the model was utilized as the main methodology to plan dynamic running and turning, in which the plot of one-step distance versus period was constructed for the legs' operation point selection and matching. The proposed methodology was experimentally validated using four indices: turning curvature, flight phase, motion stability, and energy efficiency. The experimental results show that the running robot using the SLTL model with differentiated leg stiffness has better energy efficiency than one without by 4%, while the latter model has identical performance to the original spring-loaded inverted pendulum model with rolling contact. As for turning, the robot using the SLTL models with/without differentiated leg stiffness can preserve dynamic turning in all experiments with turning curvatures up to0.28m-1and0.30m-1, respectively,33%and43%more than the robot using the original model-less phase-shift turning strategy (0.21 m-1). Using the proposed model-based strategy, the flight phase of the robot turning in all curvatures (including straight running) maintains around 20%, the root-mean-squared (RMS) values of pitch and roll remains less than3 deg, and the specific resistance (SR) is bounded between0.64 and 0.73. By contrast, the robot using the phase-shifting turning strategy can maintain dynamic motion up to a turning curvature of0.21 m-1. A further increase in phase shifting not only does not increase the turning curvature but also changes the robot motion from running to walking. In this case, no flight phase exists, theSRjumps up significantly, and RMS values of pitch and roll also increase dramatically. In short, the experimental validation confirms the effectiveness of the proposed methodology for initiating the dynamic running and turning of the robot.

Design, Analysis and Experiments of Hexapod Robot with Six-Link Legs for High Dynamic Locomotion

An important feature of a legged robot is its dynamic motion performance. Traditional methods often improve the dynamic motion performance by reducing the moment of inertia of robot legs or by adopting quasi-direct drive actuators. This paper proposes a method to enhance the dynamic performance of a legged robot by transmission mechanism. Specifically, we present a unique six-link leg mechanism that can implement a large output motion using a small drive motion. This unique feature can enhance the robots' dynamic motion capability. Experiments with a hexapod robot verified the effectiveness of the mechanism. The experimental results showed that, when the steering gear of the robot rotates 1°, the toe can lift 7 mm (5% of body height), and the maximum running speed of the robot can reach 390 mm/s (130% of the moveable body length per second).

An assessment of contamination pickup on ground robotic vehicles for nuclear surveying application

Ground robotic vehicles are often deployed to inspect areas where radioactive floor contamination is a prominent risk. However, the accuracy of detection could be adversely affected by enhanced radiation signal through self-contamination of the robot occurring over the course of the inspection. In this work, it was hypothesised that a six-legged robot could offer advantages over the more conventional ground robotic devices such as wheeled and tracked rovers. To investigate this, experimental contamination testing and computational Monte Carlo simulation techniques (GEANT4) were employed to understand how radioactive contamination pick-up on three different robotic vehicles would affect their detection accuracy. Two robotic vehicles were selected for comparison with the hexapod robot based on their type of locomotion; a wheeled rover and a tracked rover. With the aid of a non-toxic fluorescent tracer dust, the contamination received by the all three vehicles when traversing a contaminated area was initially compared through physical inspection using high definition cameras. The parametric results from these tests where used in the computational study carried out in GEANT4. A cadmium zinc telluride detector was simulated at heights ranging from 10 to 50 cm above each contaminated vehicle, as if it were mounted on a plinth. Assuming a uniform activity of 60 Bq cm^-2on all contaminated surfaces, the results suggested that due to the hexapod's small ground-contacting surface area and geometry, radiation detection rates using an uncollimated detector are likely to be overestimated by between only 0.07%-0.12%, compared with 3.95%-8.43% and 1.75%-14.53% for the wheeled and tracked robot alternatives, respectively.

Adaptive Locomotion Control of a Hexapod Robot via Bio-Inspired Learning

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.

Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

Neural signals for locomotion are influenced both by the neural network architecture and sensory inputs coordinating and adapting the gait to the environment. Adaptation relies on the ability to change amplitude, frequency, and phase of the signals within the sensorimotor loop in response to external stimuli. However, in order to experiment with closed-loop control, we first need a better understanding of the dynamics of the system and how adaptation works. Based on insights from biology, we developed a spiking neural network capable of continuously changing amplitude, frequency, and phase online. The resulting network is deployed on a hexapod robot in order to observe the walking behavior. The morphology and parameters of the network results in a tripod gait, demonstrating that a design without afferent feedback is sufficient to maintain a stable gait. This is comparable to results from biology showing that deafferented samples exhibit a tripod-like gait and adds to the evidence for a meaningful role of network topology in locomotion. Further, this work enables research into the role of sensory feedback and high-level control signals in the adaptation of gait types. A better understanding of the neural control of locomotion relates back to biology where it can provide evidence for theories that are currently not testable on live insects.

Research on the Stationarity of Hexapod Robot Posture Adjustment

This paper proposes a smooth adjustment method for the instability problem that occurs during the start and stop of a multi-footed robot during attitude change. First, kinematics analysis is used to establish the mapping relationship between the joint angles of the robot support legs and the body posture. The leg joint angle is a known quantity that can be measured accurately and in real time. Therefore, when the position of the foot end of the support leg is unchanged, a unique set of joint angles can be obtained with the change of body posture at a certain moment. Based on the designed mapping model, the smooth adjustment of the posture can be achieved by the smooth adjustment of the support legs. Second, a constraint index that satisfies the requirements of the robot's steady adjustment of the robot is given. The S-curve acceleration/deceleration method is used to plan the body's attitude angle transformation curve, and then the mapping control relationship is used to obtain the control trajectory requirements of the joint to achieve smooth adjustment. In addition, this paper also gives a simple choice and motion control method for the redundancy problem caused by the number of support legs of a multi-footed robot when the attitude is changed. The simulation and prototype experiments verify and analyze the proposed method. The results of comparative experiments show that the posture adjustment method proposed in this paper has continuous acceleration without breakpoints, the speed changes gently during the start and stop phases of the attitude transformation, and there is no sudden change in the entire process, which improves the consistency of the actual values of the attitude planning curve with the target values. The physical prototype experiment shows that the maximum deviation between the actual value of the attitude angular velocity and the target value changes from 62.5% to 5.5%, and the degree of fit increases by 57.0%. Therefore, this study solves the problem of the instability of the fuselage when the robot changes its attitude, and it provides an important reference for the multi-footed robot to improve the terrain adaptability.

CPG-Based Gait Generation of the Curved-Leg Hexapod Robot with Smooth Gait Transition

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.

Turning and Radius Deviation Correction for a Hexapod Walking Robot Based on an Ant-Inspired Sensory Strategy

In order to find a common approach to plan the turning of a bio-inspired hexapod robot, a locomotion strategy for turning and deviation correction of a hexapod walking robot based on the biological behavior and sensory strategy of ants. A series of experiments using ants were carried out where the gait and the movement form of ants was studied. Taking the results of the ant experiments as inspiration by imitating the behavior of ants during turning, an extended turning algorithm based on arbitrary gait was proposed. Furthermore, after the observation of the radius adjustment of ants during turning, a radius correction algorithm based on the arbitrary gait of the hexapod robot was raised. The radius correction surface function was generated by fitting the correction data, which made it possible for the robot to move in an outdoor environment without the positioning system and environment model. The proposed algorithm was verified on the hexapod robot experimental platform. The turning and radius correction experiment of the robot with several gaits were carried out. The results indicated that the robot could follow the ideal radius and maintain stability, and the proposed ant-inspired turning strategy could easily make free turns with an arbitrary gait.