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Path planning is a fundamental and significant issue in robotics research, especially for the legged robots, since it is the core technology for robots to complete complex tasks such as autonomous navigation and exploration. The purpose of this paper is to propose a path planning and tracking framework for the autonomous navigation of hexapod robots.

First, a hexapod robot called Hexapod-Mini is briefly introduced. Then a path planning algorithm based on improved A* is proposed, which introduces the artificial potential field (APF) factor into the evaluation function to generate a safe and collision-free initial path. Then we apply a turning point optimization based on the greedy algorithm, which optimizes the number of turns of the path. And a fast-turning trajectory for hexapod robot is proposed, which is applied to path smoothing. Besides, a model predictive control-based motion tracking controller is used for path tracking.

The simulation and experiment results show that the framework can generate a safe, fast, collision-free and smooth path, and the author’s Hexapod robot can effectively track the path that demonstrates the performance of the framework.

The work presented a framework for autonomous path planning and tracking of hexapod robots. This new approach overcomes the disadvantages of the traditional path planning approach, such as lack of security, insufficient smoothness and an excessive number of turns. And the proposed method has been successfully applied to an actual hexapod robot.

The aim of this paper is to introduce a hexapod walking robot specifically designed for applications in humanitarian demining, intended to operate autonomously for several hours. To this end, the paper presents an experimental study for the evaluation of its energy efficiency.

First, the interest of using a walking robot for detection and localization of anti-personnel landmines is described, followed by the description of the mechanical system and the control architecture of the hexapod robot. Second, the energy efficiency of the hexapod robot is assessed to demonstrate its autonomy for performing humanitarian demining tasks. To achieve this, the power consumed by the robot is measured and logged, with a number of different payloads placed on-board (always including the scanning manipulator arm assembled on the robot front end), during the execution of a discontinuous gait on flat terrain.

The hexapod walking robot has demonstrated low energy consumption when it is carrying out several locomotion cycles with different loads on it, which is fundamental to have a desired autonomy. It should be considered that the robot has a mass of about 250 kg and that it has been loaded with additional masses of up to 170 kg during the experiments, with a consumption of mean power of 72 W, approximately.

This work provides insight on the use of a walking robot for humanitarian demining tasks, which has high stability and an autonomy of about 3 hours for a robot with high mass and high payload. In addition, the robot can be supervised and controlled remotely, which is an added value when it is working in the field.

The purpose of this paper is specifically to provide a more intelligent locomotion planning method for a hexapod robot based on trajectory optimization, which could reduce the complexity of locomotion design, shorten time of design and generate efficient and accurate motion.

The authors generated locomotion for the hexapod robot based on trajectory optimization method and it just need to specify the high-level motion requirements. Here the authors first transcribed the trajectory optimization problem to a nonlinear programming problem, in which the specified motion requirements and the dynamics with complementarity constraints were defined as the constraints, then a nonlinear solver was used to solve. The leg compliance was taken into consideration and the generated motions were deployed on the hexapod robot prototype to prove the utility of the method and, meanwhile, the influence of different environments was considered.

The generated motions were deployed on the hexapod robot and the movements were demonstrated very much in line with the planning. The new planning method does not require lots of parameter-tuning work and therefore significantly reduces the cycle for designing a new locomotion.

A locomotion generation method based on trajectory optimization was constructed for a 12-degree of freedom hexapod robot. The variable stiffness compliance of legs was considered to improve the accuracy of locomotion generation. And also, different from some simulation work before, the authors have designed the locomotion in three cases and constructed field tests to demonstrate its utility.

The purpose of this paper is to propose two simple tools for the kinematic characterization of hexapods. The paper also aims to share the experience of converting a popular commercial motion base (Stewart‐Gough platform, hexapod) to an industrial robot for use in heavy duty aerospace manufacturing processes.

The complete workspace of a hexapod is a six‐dimensional entity that is impossible to visualize. Thus, nearly all hexapod manufacturers simply state the extrema of each of the six dimensions, which is very misleading. As a compromise, a special 3D subset of the complete workspace is proposed, an approximation of which can be readily obtained using a computer‐aided design (CAD)/computer‐aided manufacturing (CAM) software suite, such as computer‐aided 3D interactive application (CATIA). While calibration techniques for serial robots are readily available, there is still no generally agreed procedure for calibrating hexapods. The paper proposes a simple calibration method that relies on the use of a laser tracker and requires no programming at all. Instead, the design parameters of the hexapod are directly and individually measured and the few computations involved are performed in a CAD/CAM software such as CATIA.

The conventional octahedral hexapod design has a very limited workspace, though free of singularities. There are important deviations between the actual and the specified kinematic model in a commercial motion base.

A commercial motion base can be used as a precision positioning device with its controller retrofitted with state‐of‐the‐art motion control technology with accurate workspace and geometric characteristics.

A novel geometric approach for obtaining meaningful measures of the workspace is proposed. A novel, systematic procedure for the calibration of a hexapod is outlined. Finally, experimental results are presented and discussed.

Biological systems such as insects have often been used as a source of inspiration when developing walking robots. Insects' ability to nimbly navigate uneven terrain, and their observed behavioral complexity have been a beacon for engineers who have used behavioral data and hypothesized control systems to develop some remarkably agile robots. The purpose of this paper is to show how it is possible to implement models of relatively recent discoveries of the stick insect's local control system (its thoracic ganglia) for hexapod robot controllers.

Walking control based on a model of the stick insect's thoracic ganglia, and not just observed insect behavior, has now been implemented in a complete hexapod able to walk, perform goal‐seeking behavior, and obstacle surmounting behavior, such as searching and elevator reflexes. Descending modulation of leg controllers is also incorporated via a head module that modifies leg controller parameters to accomplish turning in a role similar to the insect's brain and subesophageal ganglion.

While many of these features have been previously demonstrated in robotic subsystems, such as single‐ and two‐legged test platforms, this is the first time that the neurobiological methods of control have been implemented in a complete, autonomous walking hexapod.

The methods introduced here have minimal computation complexity and can be implemented on small robots with low‐capability microcontrollers. This paper discusses the implementation of the biologically grounded insect control methods and descending modulation of those methods, and demonstrates the performance of the robot for navigating obstacles and performing phototaxis.

This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact RGB-D sensors. This paper identifies problems related to in-motion data acquisition in a legged robot and evaluates the particular building blocks and concepts applied in contemporary SLAM systems against these problems. The SLAM systems are evaluated on two independent experimental set-ups, applying a well-established methodology and performance metrics.

Four feature-based SLAM architectures are evaluated with respect to their suitability for localization of multi-legged walking robots. The evaluation methodology is based on the computation of the absolute trajectory error (ATE) and relative pose error (RPE), which are performance metrics well-established in the robotics community. Four sequences of RGB-D frames acquired in two independent experiments using two different six-legged walking robots are used in the evaluation process.

The experiments revealed that the predominant problem characteristics of the legged robots as platforms for SLAM are the abrupt and unpredictable sensor motions, as well as oscillations and vibrations, which corrupt the images captured in-motion. The tested adaptive gait allowed the evaluated SLAM systems to reconstruct proper trajectories. The bundle adjustment-based SLAM systems produced best results, thanks to the use of a map, which enables to establish a large number of constraints for the estimated trajectory.

The evaluation was performed using indoor mockups of terrain. Experiments in more natural and challenging environments are envisioned as part of future research.

The lack of accurate self-localization methods is considered as one of the most important limitations of walking robots. Thus, the evaluation of the state-of-the-art SLAM methods on legged platforms may be useful for all researchers working on walking robots’ autonomy and their use in various applications, such as search, security, agriculture and mining.

The main contribution lies in the integration of the state-of-the-art SLAM methods on walking robots and their thorough experimental evaluation using a well-established methodology. Moreover, a SLAM system designed especially for RGB-D sensors and real-world applications is presented in details.

This paper aims to explain the design of a novel leg mechanism for SLEGS robot. SLEGS means both “S”-shaped for the legged robot and “O”-shaped for the wheeled robot. It is a reconfigurable/transformable mobile robot.

First, a novel robot leg is designed by inspiration from previous studies. Second, the SLEGS robot’s leg is modeled using 3D computer model, and kinematics analysis performed on the leg mechanism. Finally, the prototype of the novel leg was developed for the SLEGS robot.

The robot leg mechanism has both flexible and self-reconfigurable modular features. All legs automatically take the form of both a rotating wheel and a walking leg with a self-reconfigurable modular feature.

The modeled leg is original in terms of its novel locomotion mechanism in both the walking and wheeled configurations.

The purpose of this paper is to establish analytical and numerical solutions of a navigational law to estimate displacements of hyper-static multi-legged mobile robots, which combines: monocular vision (optical flow of regional invariants) and legs dynamics.

In this study the authors propose a Euler-Lagrange equation that control legs’ joints to control robot's displacements. Robot's rotation and translational velocities are feedback by motion features of visual invariant descriptors. A general analytical solution of a derivative navigation law is proposed for hyper-static robots. The feedback is formulated with the local speed rate obtained from optical flow of visual regional invariants. The proposed formulation includes a data association algorithm aimed to correlate visual invariant descriptors detected in sequential images through monocular vision. The navigation law is constrained by a set of three kinematic equilibrium conditions for navigational scenarios: constant acceleration, constant velocity, and instantaneous acceleration.

The proposed data association method concerns local motions of multiple invariants (enhanced MSER) by minimizing the norm of multidimensional optical flow feature vectors. Kinematic measurements are used as observable arguments in the general dynamic control equation; while the legs joints dynamics model is used to formulate the controllable arguments.

The given analysis does not combine sensor data of any kind, but only monocular passive vision. The approach automatically detects environmental invariant descriptors with an enhanced version of the MSER method. Only optical flow vectors and robot's multi-leg dynamics are used to formulate descriptive rotational and translational motions for self-positioning.

This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics; learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino – Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. Throughout the course, participants were introduced to programming languages (including Python and C++), advanced programming concepts such as ROS, basic API development, system concepts such as I2C and UART serial interfaces, PWM motor control and sensor fusion to improve robotic navigation and localization. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions.

This paper aims to use the Matsuoka’s neural oscillators as the basic units of central pattern generator (CPG), and to offer a new CPG architecture consisting of a dual neural CPG of circular three links responsible for oscillator phase adjustment, to which an external neural oscillator is added, which is responsible for oscillator amplitude adjustment, to control foot depth to balance itself when treading on an obstacle.

It is equipped with a triaxial accelerometer and a triaxial gyroscope to obtain a real-time robot attitude, and to disintegrate the foot tilt in each direction as feedback signals to CPG to restore the robot’ horizontal attitude on an uneven terrain. The CPG controller is a distributed control method, with each foot controller consisting of a group of reciprocally coupling neural oscillators and sensors to generate different locomotion by different coupling patterns.

The experiment results indicated that the gait design method succeeded in enabling a steady hexapod walking on a rugged terrain, the mode of response is such that adjustments can only be made when the tilt occurs.

The overall control mechanism uses individual foot tilts as the feedback signal input to the neural oscillators to change the amplitude and compare against the reference oscillators of fixed amplitude to generate the foot height reference signals that can balance the body, and then convert the control signals, through a trajectory generator, to foot trajectories from which the actual rotation angle of servo motors can be obtained through inverse kinematics to achieve the effect of restoring the balance when traveling.

The controller design based on the bionic CPG model has the ability to restore its balance when its body tilts. In addition to the model’s ability to control locomotion, from the response waveforms of this experiment, it can also be noticed that it can control the foot depth to balance itself when treading on an obstacle, and it can adapt to a changing environment. When the obstacle is removed, the robot can quickly regain its balance.