Multi-sensor fusion based SLAM and obstacle avoidance mobile chassis

Abstract

The invention provides a multi-sensor fusion based SLAM and obstacle avoidance mobile chassis capable of improving the mapping accuracy and achieving accurate obstacle avoidance, and belongs to the technical field of robots. The mobile chassis comprises: an environment sensing module configured to acquire environmental data around a chassis body through a laser sensor and a visual sensor; a bottomsensing module configured to acquire the rotation directional angular displacement of the chassis body and surrounding obstacle information; a control module configured to: process the environmentaldata to construct a map, determine a position of an obstacle according to the obstacle information, acquire a motion control instruction for the chassis body, transmit the command to a servo drive module to realize obstacle avoidance, and configured to receive a linear displacement of the chassis body returned by the servo drive module, and acquire a current pose of the chassis body according tothe rotation directional angular displacement; and the servo drive module configured to drive the chassis body according to the motion control command so as to achieve speed control, and acquire the linear displacement information of the chassis body.

Description

technical field [0001] The invention relates to a mobile chassis equipped with a mobile dual-arm robot, in particular to a mobile chassis based on multi-sensor fusion SLAM and obstacle avoidance, and belongs to the field of robot technology. Background technique [0002] In the family or factory workshop, due to the complex environment, narrow and crowded passages, and irregular placement of items, the mobile chassis is required to have good flexibility in these environments. In the common chassis drive mode, there are differential drive and mecanum wheel omnidirectional drive. The former can realize zero turning radius, and the latter can realize flexible walking in three degrees of freedom in the plane, but the shock-absorbing performance of the latter and The load-bearing capacity is poor, and it is not suitable for this mobile chassis equipped with a robotic arm. At the same time, considering its tasks such as grasping and handling, it is necessary for the chassis to hav...

AI Technical Summary

Problems solved by technology

However, LiDAR can only obtain two-dimensional plane data to construct a two-dimensional map. It will ignore the height information of obstacles in the map, will not be able to obtain environmental mutations, and will cause the failure of navigation and obstacle avoidance. When deterministic, the real-time performance of local path planning will not reach the required level

The depth camera sensor can detect the environmental information in the field of view, and can obtain raw data characterized by dense point images. Using it alone as a SLAM (simultaneous localization and mapping, real-time positioning and map construction) sensor will greatly affect the performance of the algorithm. Real-time, increasing the burden on the sensor

At the same time, only the data of the encoder is used as the pose estimation of the mobile chassis, which will be affected by the accumulated drift of the encoder for a long time, resulting in poor accuracy of large-scale mapping

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