Abstract
This paper presents a reconfigurable biomimetic robot which is able to crawl and roll. The robot mimics the morphology of a huntsman spider that can transform between crawling and rolling by reconfiguring its legs. Terrain perception for reconfigurable biomimetic robots has not been studied in literature. This work tends to perceive and segment the terrain when the robot is crawling or in the steady state between rollings. A remote control system is designed with a server-client mechanism which can perform real-time image processing with GPGPU coding and develop a probabilistic framework for terrain perception. For validation, we test the system in both an indoor lab environment and a more uncontrolled outdoor environment. The results suggest that the system provides a trustable performance.
Highlights
Reconfigurable robots capable of performing multi-state locomotion offer enormous potential with their versatility, fault tolerance, and efficiency for a variety of rugged missions in real world
Real-world deployments of these reconfigurable robots often require some intelligent capabilities, such as terrain perception and understanding, that go beyond purely mechanism design
Terrain perception we describe the image perception/segmentation based on the mean shift algorithm
Summary
Reconfigurable robots capable of performing multi-state locomotion offer enormous potential with their versatility, fault tolerance, and efficiency for a variety of rugged missions in real world. There have been a lot of works on reconfigurable robotics [1,2,3,4,5]. The design of reconfigurable robots can be inspired by nature. Some attempts tried to develop reconfigurable biomimetic robotic platforms with rolling and crawling capabilities in the cases of BiLBIQ [6]. These efforts had been focused completely on mechanism design with almost no effort associated with perception or autonomous features. Real-world deployments of these reconfigurable robots often require some intelligent capabilities, such as terrain perception and understanding, that go beyond purely mechanism design. Integrating complex reconfigurable design mechanisms with perception introduces a lot of new research challenges
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