Bioinspired dual soft arm mobile robot with humanoid tactile fingertip sensing and bubble artificial muscles for adaptive obstacle avoidance and object manipulation

With the rapid advancements in automation and soft robotics, the exploration of mobile robots for applications in complex environments is increasingly deepening. This paper presents a novel dual soft arm mobile robot (DSAMR), whose design integrates advanced soft robotics technologies with biomimetic design inspired by human arms, aiming to achieve efficient obstacle avoidance and object manipulation. The robot employs Bubble Artificial Muscle Arms (BAMAs) for locomotion, enabling flexible movements such as forward, backward, and turning motions; it also integrates TacTip (tactile fingertip), a biomimetic sensor that mimics the tactile structure of human fingertips, to achieve real-time perception. BAMAs and TacTip collaborate to achieve the integration of perception and operation like a human hand, enabling the system to accurately detect obstacles and manipulate objects, including typical delicate items such as a paper towel roll and a pen, with the maximum capacity to grasp objects weighing up to 148.8 g. Experiments have demonstrated that a single inflation-deflation cycle of the BAMAs enables the DSAMR to turn right by 35.5° and left by 28.3°, and successfully allows the DSAMR to recognize obstacles and turn to avoid them. The experimental results indicate that the DSAMR can operate effectively in dynamic environments, with excellent stability and obstacle avoidance capabilities. This paper discusses the design details of BAMA actuators, steering engines, and TacTip, as well as their integration into the robot's motion and sensing systems. The findings emphasize the DSAMR's potential applications in industrial automation, particularly in the context of Industry 4.0. Finally, the study summarizes optimization strategies and future improvement directions to enhance the robot's operational efficiency, including onboard power integration and advanced obstacle recognition technologies.