Hexapod Walking Research Articles

Gait planning and adjustment for a hexapod walking robot with one leg failure

ABSTRACT Gait adjusting is an effective way to solve leg failure problems and is also the foundation of intelligent self-adjustment. This paper takes a hexagonal type of robot as a prototype and proposes a ‘2-2-1’ gait grouping and gait sequence for the one leg failure case based on the ‘4 + 2’ gait. The longitudinal stability margin is used to evaluate the FLF-A and FLF-B gaits with left front leg failure and FLM-A and FLM-B gait with left middle leg failure. The foothold positions are adjusted by changing the median angle of the stance phase of the body-coxa joint in each leg in order to make sure the centre of gravity is in a stable support polygon. The experiments of the robot walking show that the FLF-A gait and the FLM-B gait are more stable than the FLF-B and FLM-A after adjustment. The pitch and roll angles are maintained within − 3° to 3°, which verify the effectiveness of the stance phase leg median position adjustment strategy. This study could be used to do forward pre-adjustment in the case of one leg failure.

Multiple local controls integrated by RMPs for FCP-based hexapod walking

Multiple local controls integrated by RMPs for FCP-based hexapod walking

Autonomous gait switching method and experiments of a hexapod walking robot for Mars environment with multiple terrains

Autonomous gait switching method and experiments of a hexapod walking robot for Mars environment with multiple terrains

A Soft Actor-Critic Approach for a Blind Walking Hexapod Robot with Obstacle Avoidance

This paper investigates a path planning approach for the walking and obstacle avoidance of a blind hexapod robot in various field conditions. Hexapod robots often perform field tasks in unstructured environments, and their external sensors are affected by weather and light. This paper proposes the use of internal sensors to sense the terrain and a slightly modified soft actor-critic algorithm to train the motion strategy. A hexapod robot is capable of walking smoothly on rough ground only using internal sensors that are not affected by weather factors, and the soft actor-critic approach is superior for overcoming high-dimensional issues for multi-degree-freedom robot motion in unstructured environments.The experiments showed that the hexapod robot not only traversed rugged terrain at a fixed speed but also possessed obstacle avoidance capabilities.

Energy Efficiency of a Wheeled Bio-Inspired Hexapod Walking Robot in Sloping Terrain

Multi-legged robots, such as hexapods, have great potential to navigate challenging terrain. However, their design and control are usually much more complex and energy-demanding compared to wheeled robots. This paper presents a wheeled six-legged robot with five degrees of freedom, that is able to move on a flat surface using wheels and switch to gait in rugged terrain, which reduces energy consumption. The novel joint configuration mimics the structure of insect limbs and allows our robot to overcome difficult terrain. The wheels reduce energy consumption when moving on flat terrain and the trochanter joint reduces energy consumption when moving on slopes, extending the operating time and range of the robot. The results of experiments on sloping terrain are presented. It was confirmed that the use of the trochanter joint can lead to a reduction in energy consumption when moving in sloping terrain.

NeuroWalknet, a controller for hexapod walking allowing for context dependent behavior.

Decentralized control has been established as a key control principle in insect walking and has been successfully leveraged to account for a wide range of walking behaviors in the proposed neuroWalknet architecture. This controller allows for walking patterns at different velocities in both, forward and backward direction-quite similar to the behavior shown in stick insects-, for negotiation of curves, and for robustly dealing with various disturbances. While these simulations focus on the cooperation of different, decentrally controlled legs, here we consider a set of biological experiments not yet been tested by neuroWalknet, that focus on the function of the individual leg and are context dependent. These intraleg studies deal with four groups of interjoint reflexes. The reflexes are elicited by stimulation of the femoral chordotonal organ (fCO) or groups of campaniform sensilla (CS). Motor output signals are recorded from the alpha-joint, the beta-joint or the gamma-joint of the leg. Furthermore, the influence of these sensory inputs to artificially induced oscillations by application of pilocarpine has been studied. Although these biological data represent results obtained from different local reflexes in different contexts, they fit with and are embedded into the behavior shown by the global structure of neuroWalknet. In particular, a specific and intensively studied behavior, active reaction, has since long been assumed to represent a separate behavioral element, from which it is not clear why it occurs in some situations, but not in others. This question could now be explained as an emergent property of the holistic structure of neuroWalknet which has shown to be able to produce artificially elicited pilocarpine-driven oscillation that can be controlled by sensory input without the need of explicit innate CPG structures. As the simulation data result from a holistic system, further results were obtained that could be used as predictions to be tested in further biological experiments.

Autonomous robotic exploration with simultaneous environment and traversability models learning.

In this study, we address generalized autonomous mobile robot exploration of unknown environments where a robotic agent learns a traversability model and builds a spatial model of the environment. The agent can benefit from the model learned online in distinguishing what terrains are easy to traverse and which should be avoided. The proposed solution enables the learning of multiple traversability models, each associated with a particular locomotion gait, a walking pattern of a multi-legged walking robot. We propose to address the simultaneous learning of the environment and traversability models by a decoupled approach. Thus, navigation waypoints are generated using the current spatial and traversability models to gain the information necessary to improve the particular model during the robot’s motion in the environment. From the set of possible waypoints, the decision on where to navigate next is made based on the solution of the generalized traveling salesman problem that allows taking into account a planning horizon longer than a single myopic decision. The proposed approach has been verified in simulated scenarios and experimental deployments with a real hexapod walking robot with two locomotion gaits, suitable for different terrains. Based on the achieved results, the proposed method exploits the online learned traversability models and further supports the selection of the most appropriate locomotion gait for the particular terrain types.

Mechanical Design and a Novel Structural Optimization Approach for Hexapod Walking Robots

This paper presents a novel model-based structural optimization approach for the efficient electromechanical development of hexapod robots. First, a hexapod-design-related analysis of both optimization objectives and relevant parameters is conducted based on the derived dynamical model of the robot. A multi-objective optimization goal is proposed, which minimizes energy consumption, unwanted body motion and differences between joint torques. Then, an optimization framework is established, which utilizes a sophisticated strategy to handle the optimization problems characterized by a large set of parameters. As a result, a satisfactory result is efficiently obtained with fewer iterations. The research determines the optimal parameter set for hexapod robots, contributing to significant increases in a robot’s walking range, suppressed robot body vibrations, and both balanced and appropriate motor loads. The modular design of the proposed simulation model also offers flexibility, allowing for the optimization of other electromechanical properties of hexapod robots. The presented research focuses on the mechatronic design of the Szabad(ka)-III hexapod robot and is based on the previously validated Szabad(ka)-II hexapod robot model.

Modelling and Analysis of Hexapod walking Robot

Modelling and Analysis of Hexapod walking Robot

The Hybrid Position/Force Walking Robot Control Using Extenics Theory and Neutrosophic Logic Decision.

This paper presents a hybrid force/position control. We developed it for a hexapod walking robot that combines multiple bipedal robots to increase its load. The control method integrated Extenics theory with neutrosophic logic to obtain a two-stage decision-making algorithm. The first stage was an offline qualitative decision-applying Extenics theory, and the second was a real-time decision process using neutrosophic logic and DSmT theory. The two-stage algorithm separated the control phases into a kinematic control method that used a PID regulator and a dynamic control method developed with the help of sliding mode control (SMC). By integrating both control methods separated by a dynamic switching algorithm, we obtained a hybrid force/position control that took advantage of both kinematic and dynamic control properties to drive a mobile walking robot. The experimental and predicted results were in good agreement. They indicated that the proposed hybrid control is efficient in using the two-stage decision algorithm to drive the hexapod robot motors using kinematic and dynamic control methods. The experiment presents the robot’s foot positioning error while walking. The results show how the switching method alters the system precision during the pendulum phase compared to the weight support phase, which can better compensate for the robot’s dynamic parameters. The proposed switching algorithm directly influences the overall control precision, while we aimed to obtain a fast switch with a lower impact on the control parameters. The results show the error on all axes and break it down into walking stages to better understand the control behavior and precision.

From Adaptive Locomotion to Predictive Action Selection – Cognitive Control for a Six-Legged Walker

Locomotion in animals provides a model for adaptive behavior as it is able to deal with various kinds of perturbations. Work in insects suggests that this evolved flexibility results from a modular architecture, which can be characterized by a recurrent neural network allowing for various emerging attractor states. Whereas a lower control-level coordinates joint movements on a short timescale, a higher-level handles action selection on longer timescales. Implementation of such a control system on a walking hexapod robot was able to deal with various walking patterns including disturbances such as uneven terrain or loss of a leg. Here, we propose a cognitive expansion to the adaptive control system that allows dealing with novel challenging situations. This approach makes use of an internal simulation-based planner that is triggered when the model-free controller fails to recover from an unstable pose. Using a grounded internal body model, the planner then tries, in internal simulation, different solutions out of context, and thus, proposes a new plan to be executed on the real robot. We demonstrate the feasibility of this control approach for walking over terrain with uncertain footholds in three scenarios.

Development of Harvestman-like Hexapod Walking Robot ASURA-II

Development of Harvestman-like Hexapod Walking Robot ASURA-II

Erratum to “Comparative study on the reachable workspaces of regular and irregular axially symmetric hexapod robots”

Six-legged hexapod walking robots are well-known for their intrinsic stability during navigation and 6-DoF object manipulation. The robot must be operated under a stable workspace envelope to attain effective manipulation. The structural parameters of the robot dramatically influence the characteristics of the workspace. Most studies analyze the workspace only by varying the leg parameter of the robot, maintaining the base configuration constant. This study aims to investigate the effect of change in the base plate characteristics on the workspace for an axially symmetric hexapod robot with cyclic and elliptical configurations. Superimposing the lateral 2D workspace of each pair of legs evolved to obtain a 3D workspace. The quantitative data analysis of the workspace shows that the cyclic configuration has an average increase of 23.74 %, and the elliptical configuration has a reduction of 1.97 % reachable workspace volume compared with regular hexapods. Furthermore, regular, cyclic, and elliptical hexapods are fabricated and validated with the selective workspace analysis data to reinforce the results. This study shows that the change in the hexagonal base plate configuration has a significant influence on workspace characteristics.

Modeling, Simulation and GUI Control of a Hexapod Walking Robot

Modeling, Simulation and GUI Control of a Hexapod Walking Robot

Automatic selection of the Groebner Basis’ monomial order employed for the synthesis of the inverse kinematic model of non-redundant open-chain robotic systems

The methods most commonly used to synthesize the Inverse Kinematic Model (IKM) of open-chain robotic systems strongly depend on the robot’s geometry, which make them difficult to systematize. In a previous work we presented a systematic procedure that relies on Groebner Bases to synthesize the IKM of non-redundant open-chain robots. This study expands the developed procedure with a methodology for the automatic selection of the basis’ monomial order. The procedure’s inputs are the robot’s Denavit-Hartenberg parameters and the movement range of its actuators, while the output is the synthesized IKM, ready to be used in the robot’s control system or in a simulation of its behavior. This procedure can synthesize the IKM of a wide range of open-chain robotic systems, such as Cartesian robots, SCARA, non-redundant multi-legged robots, and all non-redundant manipulators that satisfy the in-line wrist condition. The procedure’s performance is assessed through two study cases of open-chain robots: a walking hexapod and a PUMA manipulator. The optimal monomial order is successfully identified for all cases. Also the output errors of the synthesized IKMs are negligible when evaluated in their corresponding workspaces, while their computation times are comparable to those required by the kinematic models calculated by traditional methods.

Self-Learning Event Mistiming Detector Based on Central Pattern Generator.

A repetitive movement pattern of many animals, a gait, is controlled by the Central Pattern Generator (CPG), providing rhythmic control synchronous to the sensed environment. As a rhythmic signal generator, the CPG can control the motion phase of biomimetic legged robots without feedback. The CPG can also act in sensory synchronization, where it can be utilized as a sensory phase estimator. Direct use of the CPG as the estimator is not common, and there is little research done on its utilization in the phase estimation. Generally, the sensory estimation augments the sensory feedback information, and motion irregularities can reveal from comparing measurements with the estimation. In this work, we study the CPG in the context of phase irregularity detection, where the timing of sensory events is disturbed. We propose a novel self-supervised method for learning mistiming detection, where the neural detector is trained by dynamic Hebbian-like rules during the robot walking. The proposed detector is composed of three neural components: (i) the CPG providing phase estimation, (ii) Radial Basis Function neuron anticipating the sensory event, and (iii) Leaky Integrate-and-Fire neuron detecting the sensory mistiming. The detector is integrated with the CPG-based gait controller. The mistiming detection triggers two reflexes: the elevator reflex, which avoids an obstacle, and the search reflex, which grasps a missing foothold. The proposed controller is deployed and trained on a hexapod walking robot to demonstrate the mistiming detection in real locomotion. The trained system has been examined in the controlled laboratory experiment and real field deployment in the Bull Rock cave system, where the robot utilized mistiming detection to negotiate the unstructured and slippery subterranean environment.

Design, Construction, and Rough-Terrain Locomotion Control of Novel Hexapod Walking Robot With Four Degrees of Freedom Per Leg

Multi-legged walking robots are suitable platforms for unstructured and rough terrains because of their immense locomotion capabilities. These are, however, redeemed by more sophisticated control and energy-demanding motion in comparison to wheeled robots. Particularly, electrically actuated multi-legged walking robots suffer from the adverse ratio between the robot body weight and payload capacity. Moreover, the locomotion speed and endurance ratio is far from what can be achieved with wheeled robots. In this paper, we focus on six-legged walking robots with statically-stable gait. Based on the analysis of existing solutions, we propose a novel construction of the affordable electrically actuated robot with substantial improvements in its motion capabilities, locomotion speed, reliability, and endurance. The proposed design is implemented in a Hexapod Ant Robot (HAntR) that is accompanied by the developed locomotion control approach to improve its rough terrains negotiation capabilities by the active distribution of the robot weight to the legs in the stance phase. Properties of the robot have been experimentally verified in extensive deployments, and based on the experimental benchmarking of the built prototype, HAntR is capable of locomotion for over an hour with the payload of 85% of its weight, and its maximum crawled distance per one second is 87% of its nominal length. HAntR represents significant improvements not only regarding the robots with identical actuators but also in comparison to other existing platforms. Therefore, we consider HAntR represents a step further towards a wide range of future applications and deployments of six-legged walking robots.

A hybrid tactile sensor-based obstacle overcoming method for hexapod walking robots

Walking robots are considered as a promising solution for locomotion across irregular or rough terrain. While wheeled or tracked robots require flat surface like roads or driveways, walking robots can adapt to almost any terrain type. However, overcoming diverse terrain obstacles still remains a challenging task even for multi-legged robots with a high number of degrees of freedom. Here, we present a novel method for obstacle overcoming for walking robots based on the use of tactile sensors and generative recurrent neural network for positional error prediction. By using tactile sensors positioned on the front side of the legs, we demonstrate that a robot is able to successfully overcome obstacles close to robots height in the terrains of different complexity. The proposed method can be used by any type of a legged machine and can be considered as a step toward more advanced walking robot locomotion in unstructured terrain and uncertain environment.

Q-VAE for Disentangled Representation Learning and Latent Dynamical Systems

A variational autoencoder (VAE) derived from Tsallis statistics called q-VAE is proposed. In the proposed method, a standard VAE is employed to statistically extract latent space hidden in sampled data, and this latent space helps make robots controllable in feasible computational time and cost. To improve the usefulness of the latent space, this paper focuses on disentangled representation learning, e.g., $\beta$-VAE, which is the baseline for it. Starting from a Tsallis statistics perspective, a new lower bound for the proposed q-VAE is derived to maximize the likelihood of the sampled data, which can be considered an adaptive $\beta$-VAE with deformed Kullback-Leibler divergence. To verify the benefits of the proposed q-VAE, a benchmark task to extract the latent space from the MNIST dataset was performed. The results demonstrate that the proposed q-VAE improved disentangled representation while maintaining the reconstruction accuracy of the data. In addition, it relaxes the independency condition between data, which is demonstrated by learning the latent dynamics of nonlinear dynamical systems. By combining disentangled representation, the proposed q-VAE achieves stable and accurate long-term state prediction from the initial state and the action sequence. The dataset for hexapod walking is available on IEEE Dataport, doi: https://dx.doi.org/10.21227/99af-jw71.

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.