Method for Six-Legged Robot Stepping on Obstacles by Indirect Force Estimation

Adaptive gaits for legged robots often requires force sensors installed on foot-tips,however impact,temperature or humidity can affect or even damage those sensors.Efforts have been made to realize indirect force estimation on the legged robots using leg structures based on planar mechanisms.Robot Octopus III is a six-legged robot using spatial parallel mechanism（UP-2UPS） legs.This paper proposed a novel method to realize indirect force estimation on walking robot based on a spatial parallel mechanism.The direct kinematics model and the inverse kinematics model are established.The force Jacobian matrix is derived based on the kinematics model.Thus,the indirect force estimation model is established.Then,the relation between the output torques of the three motors installed on one leg to the external force exerted on the foot tip is described.Furthermore,an adaptive tripod static gait is designed.The robot alters its leg trajectory to step on obstacles by using the proposed adaptive gait.Both the indirect force estimation model and the adaptive gait are implemented and optimized in a real time control system.An experiment is carried out to validate the indirect force estimation model.The adaptive gait is tested in another experiment.Experiment results show that the robot can successfully step on a 0.2 m-high obstacle.This paper proposes a novel method to overcome obstacles for the six-legged robot using spatial parallel mechanism legs and to avoid installing the electric force sensors in harsh environment of the robot＇s foot tips.

Obstacle avoidance technology of bionic quadruped robot based on multi-sensor information fusion

In order to improve the ability of a bionic quadruped robot to percept the location of obstacles in a complex and dynamic environment, the information fusion between an ultrasonic sensor and a binocular sensor was studied under the condition that the robot moves in the Walk gait on a structured road. Firstly, the distance information of obstacles from these two sensors was separately processed by the Kalman filter algorithm, which largely reduced the noise interference. After that, we obtained two groups of estimated distance values from the robot to the obstacle and a variance of the estimation value. Additionally, a fusion of the estimation values and the variances was achieved based on the STF fusion algorithm. Finally, a simulation was performed to show that the curve of a real value was tracked well by that of the estimation value, which attributes to the effectiveness of the Kalman filter algorithm. In contrast to statistics before fusion, the fusion variance of the estimation value was sharply decreased. The precision of the position information is 4. 6 cm, which meets the application requirements of the robot.

Visual Avoidance of Collision with Randomly Moving Obstacles through Approximate Reinforcement Learning

In this research work,a hierarchical controller has been designed for an autonomous navigation robot to avoid unexpected moving obstacles where the state and action spaces are continuous.The proposed scheme consists of two parts:1)a controller with a high-level approximate reinforcement learning(ARL)technique for choosing an optimal trajectory in autonomous navigation;and 2)a low-level,appearance-based visual servoing(ABVS)controller which controls and execute the motion of the robot.A novel approach for path planning and visual servoing has been proposed by the combined system framework.The characteristics of the on-board camera which is equipped on the robot is naturally suitable for conducting the reinforcement learning algorithm.Regarding the ARL controller,the computational overhead is quite low thanks to the fact that a knowledge of obstacle motion is not necessary.The developed scheme has been implemented and validated in a simulation system of obstacle avoidance.It is noted that findings of the proposed method are successfully verified by obtaining an optimal robotic plan motion strategy.