Robots capable of manipulating cohesive materials would be beneficial in a variety of complex construction tasks. To discover principles by which robotic systems can effectively manipulate entangled granular media, we develop a robophysical platform for interaction with media composed of u-shaped particles. This robotic platform uses environmental signals to autonomously coordinate excavation, transport, and deposition of material. We test the effect of material initial conditions by characterizing robot performance in two different material compaction states, and observe as much as a 75

Centipedes locomote through complex obstacle-rich environments by propagating waves of body bending and limb stepping. However, little is known about how collisions with obstacles influence locomotion. In terrestrial environments such as branches or leaf litter, obstacles can both cause drag and offer affordances for the animals to generate thrust. In laboratory experiments, we challenged Scolopendra polymorpha (∼9 cm long, ∼1 cm wide) to negotiate model heterogeneous terrains, hexagonal and square lattices composed of thin posts. The centipedes maintained rapid motion (∼0.2 body lengths per cycle, comparable to flat ground speed) across lattices of different spacings by altering their body and limb postures in response to collisions. Several behaviors minimized deleterious limb and head collisions: the first was "prolonged limb adduction," in which consecutive limbs fold to the body after a leading limb collides with a post, while other limbs maintained a stepping pattern. The second, occurring in narrower lattices, was "body twisting," in which the animal propagated local body twists to locomote on its side using the posts as footholds. In some cases, the animals used a peristaltic-like gait, previously undocumented for this species. We propose that the principles discovered here can improve morphologies and control schemes for elongate robots tasked with navigating similar terradynamic scenarios.

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that emulate natural evolution. Over the years, EAs have been successfully applied to numerous classification problems. This paper proposes an enhanced evolutionary algorithm for synthesizing classifiers in supervised data scenarios. The algorithm is rooted in Genetic Programming (GP), a form of optimization inspired by biological evolution. GP evolves computer programs that perform specific tasks, represented as sets of genes manipulated by a Genetic Algorithm (GA). Our approach generates a Directed Acyclic Graph (DAG) for each training class to align each test class with one of them. Additionally, the approach incorporates a fitness evaluation function with dual objectives: the cumulative evaluation values of test samples and the count of prediction errors in classes. We benchmark our approach against established machine learning methods such as Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Particle Swarm Optimization (PSO). The enhanced algorithm demonstrates competitive performance, achieving an accuracy of over 70% when evaluating evolving classifiers across twelve public UCI datasets.

Characterized by their elongate bodies and relatively simple legs, multi-legged robots have the potential to locomote through complex terrains for applications such as search-and-rescue and terrain inspection. Prior work has developed effective and reliable locomotion strategies for multi-legged robots by propagating the two waves of lateral body undulation and leg stepping, which we will refer to as the two-wave template. However, these robots have limited capability to climb over obstacles with sizes comparable to their heights. We hypothesize that such limitations stem from the two-wave template that we used to prescribe the multi-legged locomotion. Seeking effective alternative waves for obstacle-climbing, we designed a five-segment robot with static (non-actuated) legs, where each cable-driven joint has a rotational degree-of-freedom (DoF) in the sagittal plane (vertical wave) and a linear DoF (peristaltic wave). We tested robot locomotion performance on a flat terrain and a rugose terrain. While the benefit of peristalsis on flat-ground locomotion is marginal, the inclusion of a peristaltic wave substantially improves the locomotion performance in rugose terrains: it not only enables obstacle-climbing capabilities with obstacles having a similar height as the robot, but it also significantly improves the traversing capabilities of the robot in such terrains. Our results demonstrate an alternative actuation mechanism for multi-legged robots, paving the way towards all-terrain multi-legged robots.

Centipede-like robots offer an effective and robust solution to navigation over complex terrain with minimal sensing. However, when climbing over obstacles, such multi-legged robots often elevate their center-of-mass into unstable configurations, where even moderate terrain uncertainty can cause tipping. Robust mechanisms for such elongate multi-legged robots to self-right remain unstudied. Here, we use a comparative biological and robophysical approach to investigate self-righting strategies. We first released S. polymorpha upside down from a 10 cm height and recorded their self-righting behaviors using top and side view high-speed cameras. Using kinematic analysis, we hypothesize that these behaviors can be prescribed by two traveling waves superimposed in the body's lateral and vertical planes, respectively. We tested our hypothesis on an elongate robot with static (non-actuated) limbs, and we successfully reconstructed these self-righting behaviors. We further evaluated how wave parameters affect self-righting effectiveness. We identified two key wave parameters: the spatial frequency, which characterizes the sequence of body-rolling, and the wave amplitude, which characterizes body curvature. By empirically obtaining a behavior diagram of spatial frequency and amplitude, we identify effective and versatile self-righting strategies for general elongate multi-legged robots, which greatly enhances these robots' mobility and robustness in practical applications such as agricultural terrain inspection and search-and-rescue.

Centipedes exhibit great maneuverability in diverse environments due to their many legs and body-driven control. By leveraging similar morphologies and control strategies, their robotic counterparts also demonstrate effective terrestrial locomotion. However, the success of these multi-legged robots is largely limited to forward locomotion; steering is substantially less studied, in part because of the difficulty in coordinating a high degree-of-freedom robot to follow predictable, planar trajectories. To resolve these challenges, we take inspiration from control schemes based on geometric mechanics(GM) in elongate systems’ locomotion through highly damped environments. We model the elongate, multi-legged system as a "terrestrial swimmer" in highly frictional environments and implement steering schemes derived from low-order templates. We identify an effective turning strategy by superimposing two traveling waves of lateral body undulation and further explore variations of the "turning wave" to enable a spectrum of arc-following steering primitives. We test our hypothesized modulation scheme on a robophysical model and validate steering trajectories against theoretically predicted displacements producing steering radii between 0 and 0.6 body length. We then apply our control framework to Ground Control Robotics’ elongate multi-legged robot, Major Tom, using these motion primitives to autonomously navigate around obstacles and corners on indoor and outdoor terrain. Our work creates a systematic framework for controlling these highly mobile devices in the plane using a low-order model based on sequences of body shape changes.

We report in experiment and simulation the spontaneous formation of dynamically bound pairs of shape changing robots undergoing locally repulsive collisions. These physical `gliders' robustly emerge from an ensemble of individually undulating three-link two-motor robots and can remain bound for hundreds of undulations and travel for multiple robot dimensions. Gliders occur in two distinct binding symmetries and form over a wide range of angular oscillation extent. This parameter sets the maximal concavity which influences formation probability and translation characteristics. Analysis of dynamics in simulation reveals the mechanism of effective dynamical attraction – a result of the emergent interplay of appropriately oriented and timed repulsive interactions. Tactile sensing stabilizes the short-lived conformation via concavity modulation.

Modern two and four legged robots exhibit impressive mobility on complex terrain, largely attributed to advancement in learning algorithms. However, these systems often rely on high-bandwidth sensing and onboard computation to perceive/respond to terrain uncertainties. Further, current locomotion strategies typically require extensive robot-specific training, limiting their generalizability across platforms. Building on our prior research connecting robot-environment interaction and communication theory, we develop a new paradigm to construct robust and simply controlled multi-legged elongate robots (MERs) capable of operating effectively in cluttered, unstructured environments. In this framework, each leg-ground contact is thought of as a basic active contact (bac), akin to bits in signal transmission. Reliable locomotion can be achieved in open-loop on "noisy" landscapes via sufficient redundancy in bacs. In such situations, robustness is achieved through passive mechanical responses. We term such processes as those displaying mechanical intelligence (MI) and analogize these processes to forward error correction (FEC) in signal transmission. To augment MI, we develop feedback control schemes, which we refer to as computational intelligence (CI) and such processes analogize automatic repeat request (ARQ) in signal transmission. Integration of these analogies between locomotion and communication theory allow analysis, design, and prediction of embodied intelligence control schemes (integrating MI and CI) in MERs, showing effective and reliable performance (approximately half body lengths per cycle) on complex landscapes with terrain "noise" over twice the robot's height. Our work provides a foundation for systematic development of MER control, paving the way for terrain-agnostic, agile, and resilient robotic systems capable of operating in extreme environments.

Intrusions into granular media are common in natural and engineered settings (e.g. during animal locomotion and planetary landings). While intrusion of complex shapes in dry non-cohesive granular materials is well studied, less is known about intrusion in cohesive powders. Granular resistive force theory (RFT) — a reduced-order frictional fluid model — quantitatively predicts intrusion forces in dry, non-cohesive granular media by assuming a linear superposition of angularly dependent elemental stresses acting on arbitrarily shaped intruders. Here we extend RFT’s applicability to cohesive dry powders, enabling quantitative modeling of forces on complex shapes during intrusion. To do so, we first conduct intrusion experiments into dry cornstarch powder to create stress functions. These stresses are similar to non-cohesive media; however, we observe relatively higher resistance to horizontal intrusions in cohesive powder compared to non-cohesive media. We use the model to identify geometries that enhance resistance to intrusion in cohesive powder, aiming to minimize sinkage. Our calculations, supported by experimental verification, suggest that a flat surface generates the largest stress across various intrusion angles while a curved surface exhibits the largest resistive force to vertical intrusion. Our model can thus facilitate optimizing design and movement strategies for robotic platforms (e.g. extraterrestrial landers) operating in such environments.

Future planetary exploration missions will require reaching challenging regions such as craters and steep slopes. Such regions are ubiquitous and present science-rich targets potentially containing information regarding the planet's internal structure. Steep slopes consisting of low-cohesion regolith are prone to flow downward under small disturbances, making it very challenging for autonomous rovers to traverse. Moreover, the navigation trajectories of rovers are heavily limited by the terrain topology and future systems will need to maneuver on flowable surfaces without getting trapped, allowing them to further expand their reach and increase mission efficiency. In this work, we used a laboratory-scale rover robot and performed maneuvering experiments on a steep granular slope of poppy seeds to explore the rover's turning capabilities. The rover is capable of lifting, sweeping, and spinning its wheels, allowing it to execute leg-like gait patterns. The high-dimensional actuation capabilities of the rover facilitate effective manipulation of the underlying granular surface. We used Bayesian Optimization (BO) to gain insight into successful turning gaits in high dimensional search space and found strategies such as differential wheel spinning and pivoting around a single sweeping wheel. We then used these insights to further fine-tune the turning gait, enabling the rover to turn 90 degrees at just above 4 seconds with minimal slip. Combining gait optimization and human-tuning approaches, we found that fast turning is empowered by creating anisotropic torques with the sweeping wheel.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Daniel Ruben Soto; Addressing a Sensible and Latent Heat Misconception Using an Inquiry-Based Activity. Phys. Teach. 1 April 2024; 62 (4): 284–286. https://doi.org/10.1119/5.0101418 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search

We study the dynamics of an oscillating, free-floating robot that generates radially expanding gravity capillary waves at a fluid surface. In open water, the device does not self-propel; near a rigid boundary, it can be attracted or repelled. Visualization of the wave field dynamics reveals that when near a boundary, a complex interference of generated and reflected waves induces a wave amplitude fluctuation asymmetry. Attraction increases as wave frequency increases or robot-boundary separation decreases. Theory on confined gravity-capillary wave radiation dynamics developed by Hocking in the 1980s captures the observed parameter dependence due to these "Hocking fields." The flexibility of the robophysical system allows detailed characterization and analysis of locally generated nonequilibrium fluctuation-induced forces [M. Kardar and R. Golestanian, Rev. Mod. Phys. 71, 1233 (1999)].