As macroscopic rough terrains are time varying and full of local topographic mutations, stable locomotions of legged robots moving through such terrains in a fixed gait form can be hardly obtained. This problem becomes more severe as the size and weight of the robot increase. An ideal pre-planned gait changing method can also be hardly designed due to the same limitations. Aiming to solve the problem, a new kind of free gait controller applied to a large-scale hexapod robot with heavy load is developed. The controller consists of two parts, a free gait planner and a gait regulator. Based on the observed macro terrain changes, the free gait planner adopts the macro terrain recognition method and the status searching method for selecting the best leg support status automatically. The gait regulator is adopted for the correction of the selected status to cope with local topographic mutations. Detailed simulation experiments are presented to demonstrate that, with the designed controller, the adopted hexapod robot can change moving gaits automatically in terms of the terrain conditions and obtain stable locomotions through rough terrains.

The friction and stiffness properties of the terrain are very important pieces of information for mobile robots in motion control, dynamics parameter adjustment, trajectory planning, etc. Inferring the friction and stiffness properties in advance can improve the safety, adaptability and reliability, and reduce the energy consumption of the robot. This paper proposes a vision-based two-stage framework for pre-estimating physical properties of the terrain. We established a field terrain image dataset with weak annotations. A semantic segmentation network that can segment terrains at the pixel level was designed. Given that the same terrain also has different physical properties, we designed two kinds of image features, and we use a decision-making model to realize the mapping from terrain to physical properties. We trained and tested the network comprehensively, and experimented with the complete framework for estimating physical properties. The experimental results show that our framework has good performance.

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.