A free gait controller designed for a heavy load hexapod robot

Zha, Fusheng; Chen, Chen; Guo, Wei; Zheng, Penglong; Shi, Jianhui

doi:10.1177/1687814019838369

Cited by 19 publications

(18 citation statements)

References 31 publications

(28 reference statements)

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“…Therefore, to get a swing leg trajectory which can adapt to the terrain, the slope attitude angles are needed to be obtained first. Similar to the coordinates defined in the authors' previous work [35] (see Figure 3 in reference [35]), a slope coordinate S and the global coordinate W are defined to obtain the slope attitude angles, as shown in Figure 7. The origin of the slope coordinate S is located at the origin of the body coordinate C. The z-axis of coordinate S, namely the Z S -axis shown in Figure 7, is defined to be perpendicular to the slope surface.…”

Section: Swing Leg Trajectory Generationmentioning

confidence: 99%

Section: Swing Leg Trajectory Generationmentioning

confidence: 99%

“…In the authors' previous work [35], a macro terrain recognition method is proposed to obtain the slope plane based on the foot positions of the support legs. Through the macro terrain recognition method, the unit normal vector of the slope W k S = W k Sx , W k Sy , W k Sz T can be calculated using Equation (4) shown in [35]. Employing the same W k S in reference [35], the relationship between W k W = [0, 0, 1] T which represents the unit normal vector of the global coordinate W, and W k S can be obtained, as shown in Equation 20:…”

Section: Swing Leg Trajectory Generationmentioning

confidence: 99%

“…Based on the gait parameters set by the robot users, the body moving velocities along different directions of the slope coordinate S can be obtained through the computation of Equation (35). Then, based on the actual attitude angles of the slope, the global moving velocity vector W V of the robot can be obtained, as shown in Equation (39):…”

Section: Support Leg Trajectory Generationmentioning

confidence: 99%

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Attitude Trajectory Optimization to Ensure Balance Hexapod Locomotion

Chen

Wei

Wang

et al. 2020

Sensors

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This paper proposes a simple attitude trajectory optimization method to enhance the walking balance of a large-size hexapod robot. To achieve balance motion control of a large-size hexapod robot on different outdoor terrains, we planned the balance attitude trajectories of the robot during walking and introduced how leg trajectories are generated based on the planned attitude trajectories. While planning the attitude trajectories, high order polynomial interpolation was employed with attitude fluctuation counteraction considered. Constraints that the planned attitude trajectories must satisfy during walking were well-considered. The trajectory of the swing leg was well designed with the terrain attitude considered to improve the environmental adaptability of the robot during the attitude adjustment process, and the trajectory of the support leg was automatically generated to satisfy the demand of the balance attitude trajectories planned. Comparative experiments of the real large-size hexapod robot walking on different terrains were carried out to validate the effectiveness and applicability of the attitude trajectory optimization method proposed, which demonstrated that, compared with the currently developed balance motion controllers, the attitude trajectory optimization method proposed can simplify the control system design and improve the walking balance of a hexapod robot.

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Section: Swing Leg Trajectory Generationmentioning

confidence: 99%

Section: Swing Leg Trajectory Generationmentioning

confidence: 99%

Section: Swing Leg Trajectory Generationmentioning

confidence: 99%

Section: Support Leg Trajectory Generationmentioning

confidence: 99%

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Attitude Trajectory Optimization to Ensure Balance Hexapod Locomotion

Chen

Wei

Wang

et al. 2020

Sensors

Self Cite

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“…To implement the above theory on a physical robot, the robot should have the ability to sense its landing information firstly. In [20], Zha et al developed a new kind of free gait controller and applied it to a large-scale hexapod robot with heavy load, where sensory feedback signals of the foot position were employed in both the free gait planner and the gait regulator. In [21], Faigl andČížek presented a minimalistic approach for a hexapod robot's adaptive locomotion control, which enabled traversing rough terrains with a small and affordable hexapod walking robot, and servomotor position feedback was also used to reliably detect the ground contact point.…”

Section: Introductionmentioning

confidence: 99%

A New Foot Trajectory Planning Method for Legged Robots and Its Application in Hexapod Robots

Xia

Zhang

2021

Applied Sciences

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Compared with wheeled and tracked robots, legged robots have better movement ability and are more suitable for the exploration of unknown environments. In order to further improve the adaptability of legged robots to complex terrains such as slopes, obstacle environments, and so on, this paper makes a new design of the legged robot’s foot sensing structure that can successfully provide accurate feedback of the landing information. Based on this information, a new foot trajectory planning method named three-element trajectory determination method is proposed. For each leg in one movement period, the three elements are the start point in the support phase, the end point in the support phase, and the joint angle changes in the transfer phase where the first two elements are used to control the height, distance, and direction of the movement, and the third element is used make decisions during the lifting process of the leg. For the support phase, the trajectory is described in Cartesian space, and a spline of linear function with parabolic blends is used. For the transfer phase, the trajectory is described in joint-space, and the joint angle function is designed as the superposition of the joint angle reverse-chronological function and the interpolation function which is obtained based on joint angle changes. As an important legged robot, a hexapod robot that we designed by ourselves with triangle gait is chosen to test the proposed foot trajectory planning method. Experiments show that, while the foot’s landing information can be read and based on the three-element trajectory planning method, the hexapod robot can achieve stable movement even in very complex scenes. Although the experiments are performed on a hexapod robot, our method is applicable to all forms of legged robots.

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