Recently, lightweight and flexible soft actuators have attracted interest from robotics researchers. We focused on pneumatic rubber artificial muscle (PAM) as a high-output soft actuator. The high compliance of PAM allows a robot to adapt flexibly to the environment without many external sensors. Although PAM has these characteristics, it is difficult to control because of the nonlinearity between the input and output and the delay of air response. This limits the accuracy of artificial muscles and complicates motion planning. Therefore, we considered that PAM can be driven by simplified control laws, so that the entire system shows emergent motion guided by metaheuristics. We developed a legged robot with two joints driven by PAMs. Each PAM was controlled with a cyclic signal, and the genetic algorithm was applied to optimize these signals. We tested to check whether the behavior of the PAMs is changed by the genetic algorithm using three simple performance indexes. We found out that although the genetic algorithm adjusted the local cyclic inputs appropriately according to each performance index, the time-varying characteristic of PAMs disturbed the monotonic increment of the evaluation values. We also discovered that by only adjusting the input timing, the leg develops a limitation in robustness.
With robots becoming closer to humans in recent years, human-friendly robots made of soft materials provide a new line of research interests. We designed and developed a soft robot that can move via self-deformation toward the practical application of monitoring children and the elderly on a daily basis. The robot’s structure was built out of flexible frames, which are bending-type pneumatic artificial muscles (BPAMs). We first provide a description and discussion on the nature of BPAM, followed by static characteristics experiment. Although the BPAM theoretical model shares a similar tendency with the experimental results, the actual BPAMs moved along the depth direction. We then proposed and demonstrated an effective locomotion method for the robot and calculated its locomotion speed by measuring its drive time and movement distance. Our results confirmed the reasonability of the robot’s speed for monitoring children and the elderly. Nevertheless, during the demonstration, some BPAMs were bent sharply by other activated BPAMs as the robot was driving, leaving a little damage on these BPAMs. This will be addressed in our future work.
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