Robots will transform our daily lives in the near future by moving from controlled industrial lines to unstructured and uncertain environments such as home, offices, or outdoors with various applications from healthcare, service, to defense. Nevertheless, two fundamental problems remain unsolved for robots to work in such environments. On one hand, how to design robots, especially meso-scale ones with sizes of a few centimeters, with multiple locomotion abilities to travel in the unstructured environment is still a daunting task. On the other hand, how to control such robots to dynamically interact with the uncertain environment for agile and robust locomotion also requires tremendous efforts. This dissertation tries to tackle these two problems in the framework of biologically inspired robotics.On the design aspect, it will be shown how biologically principles found in nature can be used to build efficient meso-scale robots with various locomotion abilities such as jumping, wheeling, and aerial maneuvering. Specifically, a robot (MSU Jumper) with continuous jumping ability will be presented. The robot can achieve the following three performances simultaneously. First, it can perform continuous steerable jumping based on the self-righting and the steering capabilities. Second, the robot only requires a single actuator to perform all the functions. Third, the robot has a light weight (23.5 g) to reduce the damage from landing impacts. Based on the MSU Jumper, a robot (MSU Tailbot) with multiple locomotion abilities is discussed. This robot can not only wheel on the ground but also jump up to overcome obstacles. Once leaping into the air, it can also control its body angle using an active tail to dynamically maneuver in mid-air for safe landings.On the control aspect, a novel non-vector space control method that formulates the problem in the space of sets is presented. This method can be easily applied to vision based control by considering images as sets. The advantage of such a method is that there is no need to extract and track features during the control process, which is required by traditional methods. Based on the non-vector space approach, the compressive feedback is proposed to increase the feedback rate and reduce the computation time. This method is ideal for the control of meso-scale robots with limited sensing and computation ability.The bio-inspired design illustrated by the MSU Jumper and MSU Tailbot in this dissertation can be applied to other robot designs. Meanwhile, the non-vector space control with compressive feedbacks lays the foundation for the control of high dynamic meso-scale robots. Together, the biologically inspired method for the design and control of meso-scale robots will pave the way for next generation bio-inspired, low cost, and agile robots.
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