Sm all animals and robots must often rapidly traverse large bump - like obstacles when moving through complex 3 - D terrains, during which, in addition to leg - ground contact, their body inevitably come s into physical contact with the obstacl es. However, we know little about the performance limits of large bump traversal and how body - terrain interaction affects traversal . To address these, we challenged the discoid cockroach and a n open - loop six - legged robot to dynamically run into a large bump of varying height t o discover the maximal traversal performance, and studied how locomotor modes and traversal performance are affected by body - terrain interaction . Remarkably, d uring rapid running, both t he animal and the robot were cap able of dynamically traversing a bump much higher than its hip height ( up to 4 times the hip height for the animal and 3 times for the robot, respectively) at traversal speeds typical of running, with decreasing traversal probability with increasing bump height. A stability analysis using a novel locomotion energy landscape model explained why traversal was more likely when the animal or robot approach ed the bump with a low initial body yaw and a high initial body pitch, and why deflection was more likely otherwise . Inspired by these principl es, we demonstrated a novel control strategy of active body pitch ing that increase d the robot's maximal traversable bump height by 75%. Our study is a major step in Bioinspiration & Biomimetics (2018), 13, 02600 5; htt ps://li.me.jhu.edu 2 establishing the framework of locomotion energy landscapes to understand locomotion in complex 3 - D terrains .

The artificial lateral line (ALL) is a bioinspired flow sensing system for underwater robots, comprising of distributed flow sensors. The ALL has been successfully applied to detect the undulatory flow fields generated by body undulation and tail-flapping of bioinspired robotic fish. However, its feasibility and performance in sensing the undulatory flow fields produced by human leg kicks during swimming has not been systematically tested and studied. This paper presents a novel sensing framework to investigate the undulatory flow field generated by swimmer's leg kicks, leveraging bioinspired ALL sensing. To evaluate the feasibility of using the ALL system for sensing the undulatory flow fields generated by swimmer leg kicks, this paper designs an experimental platform integrating an ALL system and a lab-fabricated human leg model. To enhance the accuracy of flow sensing, this paper proposes a feature extraction method that dynamically fuses time-domain and time-frequency characteristics. Specifically, time-domain features are extracted using one-dimensional convolutional neural networks and bidirectional long short-term memory networks (1DCNN-BiLSTM), while time-frequency features are extracted using short-term Fourier transform and two-dimensional convolutional neural networks (STFT-2DCNN). These features are then dynamically fused based on attention mechanisms to achieve accurate sensing of the undulatory flow field. Furthermore, extensive experiments are conducted to test various scenarios inspired by human swimming, such as leg kick pattern recognition and kicking leg localization, achieving satisfactory results.

An artificial lateral line (ALL) is a bioinspired flow sensing system of an underwater robot that consists of distributed flow sensors. The ALL has achieved great success in sensing the motion states of bioinspired underwater robots, e.g., robotic fish, that are driven by body undulation and/or tail flapping. However, the ALL has not been systematically tested and studied in the sensing of underwater robots driven by rotating propellers due to the highly dynamic and complex flow field therein. This paper makes a bold hypothesis that the distributed flow measurements sampled from the propeller wake flow, although infeasible to represent the entire flow dynamics, provides sufficient information for estimating the lateral motion states of the leader underwater robot. An experimental testbed is constructed to investigate the feasibility of such a state estimator which comprises a cylindrical ALL sensory system, a rotating leader propeller, and a water tank with a planar sliding guide. Specifically, a hybrid network that consists of a one-dimensional convolution network (1DCNN) and a bidirectional long short-term memory network (BiLSTM) is designed to extract the spatiotemporal features of the time series of distributed pressure measurements. A multi-output deep learning network is adopted to estimate the lateral motion states of the leader propeller. In addition, the state estimator is optimized using the whale optimization algorithm (WOA) considering the comprehensive estimation performance. Extensive experiments are conducted the results of which validate the proposed data-driven algorithm in estimating the motion states of the leader underwater robot by propeller wake sensing.

Legged robots constructed from soft materials are commonly claimed to demonstrate safer, more robust environmental interactions than their rigid counterparts. However, this motivating feature of soft robots requires more rigorous development for comparison to rigid locomotion. This article presents a soft legged robot platform, Horton, and a feedback control system with safety guarantees on some aspects of its operation. The robot is constructed using a series of soft limbs, actuated by thermal shape memory alloy (SMA) wire muscles, with sensors for its position and its actuator temperatures. A supervisory control scheme maintains safe actuator states during the operation of a separate controller for the robot's pose. Experiments demonstrate that Horton can lift its leg and maintain a balancing stance, a precursor to locomotion. The supervisor is verified in hardware via a human interaction test during balancing, keeping all SMA muscles below a temperature threshold. This work represents the first demonstration of a safety-verified feedback system on any soft legged robot.

Among programmers, there's a principle called DRY, which stands for "Don't repeat yourself." It's an attempt to avoid writing code that duplicates the function of other code. DRY embodies the same resistance to needless repetition as the more common idiom, "Don't reinvent the wheel." Among those making robots, a group that includes software and hardware engineers attempts to adhere to these principles, as can be seen in designs that borrow from nature, from the evolved forms of life on Earth. Biomimicry and bioinspired design provide a way to avoid reinventing the wheel.