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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.

This paper aims to present a new method to analyze the robot’s obstacle negotiation based on the terramechanics, where the terrain physical parameters, the sinkage and the slippage of the robot are taken into account, to enhance the robot’s trafficability.

In this paper, terramechanics is used in motion planning for all-terrain obstacle negotiation. First, wheel/track-terrain interaction models are established and used to analyze traction performances in different locomotion modes of the reconfigurable robot. Next, several key steps of obstacle-climbing are reanalyzed and the sinkage, the slippage and the drawbar pull are obtained by the models in these steps. In addition, an obstacle negotiation analysis method on loose soil is proposed. Finally, experiments in different locomotion modes are conducted and the results demonstrate that the model is more suitable for practical applications than the center of gravity (CoG) kinematic model.

Using the traction performance experimental platform, the relationships between the drawbar pull and the slippage in different locomotion modes are obtained, and then the traction performances are obtained. The experimental results show that the relationships obtained by the models are in good agreement with the measured. The obstacle-climbing experiments are carried out to confirm the availability of the method, and the experimental results demonstrate that the model is more suitable for practical applications than the CoG kinematic model.

Comparing with the results without considering Terramechanics, obstacle-negotiation analysis based on the proposed track-terrain interaction model considering Terramechanics is much more accurate than without considering Terramechanics.

The purpose of this paper is to introduce a tetrahedral mobile robot with only revolute joints (TMRR). By using rotation actuators, the mechanism of the robot gains favorable working space and eliminates the engineering difficulties caused by the multilevel extension compared with liner actuators. Furthermore, the rolling locomotion is improved to reduce displacement error based on dynamics analysis.

The main body of deforming mechanism with a tetrahedral exterior shape is composed of four vertexes and six RRR chains. The mobile robot can achieve the rolling locomotion and reach any position on the ground by orderly driving the rotation actuators. The global kinematics of the mobile modes are analyzed. Dynamics analysis of the robot falling process is carried out during the rolling locomotion, and the rolling locomotion is improved by reducing the collision impulse along with the moving direction.

Based on global kinematics analysis of TMRR, the robot can realize the continuous mobility based on rolling gait planning. The main cause of robot displacement error and the corresponding improvement locomotion are gained through dynamic analysis. The results of the theoretical analysis are verified by experiments on a physical prototype.

The work introduced in this paper is a novel exploration of applying the mechanism with only revolute joints to the field of tetrahedral rolling robots. It is also an attempt to use the improved rolling locomotion making this kind of mobile robot more practical. Meanwhile, the reasonable engineering structure of the robot provides feasibility for load carrying.

The purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD).

Based on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground.

The assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis.

The work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.

In this research, the authors established a hierarchical motion planner for quadruped locomotion, which enables a parallel wheel-quadruped robot, the “BIT-NAZA” robot, to traverse rough three-dimensional (3-D) terrain.

Presented is a novel wheel-quadruped mobile robot with parallel driving mechanisms and based on the Stewart six degrees of freedom (6-DOF) platform. The task for traversing rough terrain is decomposed into two prospects: one is the configuration selection in terms of a local foothold cost map, in which the kinematic feasibility of parallel mechanism and terrain features are satisfied in heuristic search planning, and the other one is a whole-body controller to complete smooth and continuous motion transitions.

A fan-shaped foot search region focuses on footholds with a strong possibility of becoming foot placement, simplifying computation complexity. A receding horizon avoids kinematic deadlock during the search process and improves robot adaptation.

Both simulation and experimental results validated the proposed scenario available and appropriate for quadruped locomotion to traverse challenging 3-D terrains.

This paper analyzes kinematic workspace for a parallel robot with 6-DOF Stewart mechanism on both body and foot. A fan-shaped foot search region enhances computation efficiency. Receding horizon broadens the preview search to decrease the possibility of deadlock minima resulting from terrain variation.

The purpose of this paper is to introduce the robotic quadrupedal platform ALoF that is designed to aid research on perception in legged locomotion.

A well‐balanced size and complexity of the robot results in a robust platform that is easy to handle, yet able to perform complex maneuvers as well as to carry sophisticated 3D sensors. A very large range of motion allows the robot to actively explore its surroundings through haptic interaction, and to choose between a wide range of planning options.

This robot was employed and tested in the lunar robotics challenge organized by the European Space Agency, for which the authors also developed a novel crawling gait, in which the weight of the robot is alternately supported by scaled plates under the main body and the four shank segments. This allowed for stable locomotion in steep terrain with very loose soil.

The paper describes how a very large range of motion allows the robot to actively explore its surroundings through haptic interaction, and to choose between a wide range of planning options. The paper describes how the authors developed a novel crawling gait, in which the weight of the robot is alternately supported by scaled plates under the main body and the four shank segments.

The aim of the research is the development of a small-scale ground mobile robot for surveillance and inspection; the main design goals are mobility in indoor environments with step climbing ability, pivoting around a vertical axis and without oscillations for stable vision, mobility in unstructured environments, low mechanical and control complexity.

The proposed hybrid leg-wheel robot is characterized by a main body equipped with two actuated wheels and two praying Mantis rotating legs; a rear frame with two idle wheels is connected to the main body by a vertical revolute joint for steering; a second revolute joint allows the rear axle to roll. The geometrical synthesis of the robot has been performed using a nondimensional approach for generality's sake.

The experimental campaign on the first prototype confirms the fulfilment of the design objectives; the robot can efficiently walk in unstructured environments realizing a mixed wheeled-legged locomotion.

Thanks to the operative flexibility of Mantis in indoor and outdoor environments, the range of potential applications is wide: surveillance, inspection, monitoring of dangerous locations, intervention in case of terroristic attacks, military tasks.

Different from other robots of similar size, Mantis combines high speed and energetic efficiency, stable vision, capability of climbing over high steps, obstacles and unevenness.

Humanoid robots have complicated dynamics, and they lack dynamic stability. Despite having similarities in kinematic structure, developing a humanoid robot with robust walking is quite difficult. In this paper, an attempt to produce a robust and expected walking gait is made by using an ALO (ant lion optimization) tuned linear inverted pendulum model plus flywheel (LIPM plus flywheel).

The LIPM plus flywheel provides the stabilized dynamic walking, which is further optimized by ALO during interaction with obstacles. It gives an ultimate turning angle, which makes the robot come closer to the obstacle and provide a turning angle that optimizes the travel length. This enhancement releases the constraint on the height of the COM (center of mass) and provides a larger stride. The framework of a sequential locomotion planer has been discussed to get the expected gait. The proposed method has been successfully tested on a simulated model and validated on the real NAO humanoid robot.

The convergence curve defends the selection of the proposed controller, and the deviation under 5% between simulation and experimental results in regards to travel length and travel time proves its robustness and efficacy. The trajectory of various joints obtained using the proposed controller is compared with the joint trajectory obtained using the default controller. The comparison shows the stable walking behavior generated by the proposed controller.

Humanoid robots are preferred over mobile robots because they can easily imitate the behaviors of humans and can result in higher output with higher efficiency for repetitive tasks. A controller has been developed using tuning the parameters of LIPM plus flywheel by the ALO approach and implementing it in a humanoid robot. Simulations and experiments have been performed, and joint angles for various joints are calculated and compared with the default controller. The tuned controller can be implemented in various other humanoid robots

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.