Microspine grippers are small spines commonly found on insect legs that reinforce surface interaction by engaging with asperities to increase shear force and traction. An array of such microspines, when integrated into the limbs or undercarriage of a robot, can provide the ability to maneuver uneven terrains, traverse inclines, and even climb walls. Meanwhile, the conformability and adaptability of soft robots makes them ideal candidates for applications involving traversal of complex, unstructured terrains. However, there remains a real-life realization gap for soft locomotors pertaining to their transition from controlled lab environment to the field that can be bridged by improving grip stability through effective integration of microspines. In this research, a passive, compliant microspine stacked array design is proposed to enhance the locomotion capabilities of mobile soft robots. A microspine array integration method effectively addresses the stiffness mismatch between soft, compliant, and rigid components. Additionally, a reduction in complexity results from actuation of the surface-conformable soft limb using a single actuator. The two-row, stacked microspine array configuration offers improved gripping capabilities on steep and irregular surfaces. This design is incorporated into three different robot configurations - the baseline without microspines and two others with different combinations of microspine arrays. Field experiments are conducted on surfaces of varying surface roughness and non-uniformity - concrete, brick, compact sand, and tree roots. Experimental results demonstrate that the inclusion of microspine arrays increases planar displacement an average of 10 times. The improved grip stability, repeatability, and, terrain traversability is reflected by a decrease in the relative standard deviation of the locomotion gaits.

Locomotion gaits are fundamental for control of soft terrestrial robots. However, synthesis of these gaits is challenging due to modeling of robot-environment interaction and lack of a mathematical framework. This work presents an environment-centric, data-driven, and fault-tolerant probabilistic model-free control framework that allows for soft multilimb robots to learn from their environment and synthesize diverse sets of locomotion gaits for realizing open-loop control. Here, discretization of factors dominating robot-environment interactions enables an environment-specific graphical representation where the edges encode experimental locomotion data corresponding to the robot motion primitives. In this graph, locomotion gaits are defined as simple cycles that are transformation invariant, i.e., the locomotion is independent of the starting vertex of these periodic cycles. Gait synthesis, the problem of finding optimal locomotion gaits for a given substrate, is formulated as binary integer linear programming problems with a linearized cost function, linear constraints, and iterative simple cycle detection. Experimentally, gaits are synthesized for varying robot-environment interactions. Variables include robot morphology—three-limb and four-limb robots, TerreSoRo-III and TerreSoRo-IV; substrate—rubber mat, whiteboard and carpet; and actuator functionality—simulated loss of robot limb actuation. On an average, gait synthesis improves the translation and rotation speeds by 82% and 97%, respectively. The results highlight that data-driven methods are vital to soft robot locomotion control due to complex robot-environment interactions and simulation-to-reality gaps, particularly when biological analogues are unavailable.

The adaptability of soft robots makes them ideal candidates to maneuver through unstructured environments. However, locomotion challenges arise due to complexities in modeling the body mechanics, actuation, and robot-environment dynamics. These factors contribute to the gap between their potential and actual autonomous field deployment. A closed-loop path planning framework for soft robot locomotion is critical to close the real-world realization gap. This paper presents a generic path planning framework applied to TerreSoRo (Tetra-Limb Terrestrial Soft Robot) with pose feedback. It employs a gait-based, lattice trajectory planner to facilitate navigation in the presence of obstacles. The locomotion gaits are synthesized using a data-driven optimization approach that allows for learning from the environment. The trajectory planner employs a greedy breadth-first search strategy to obtain a collision-free trajectory. The synthesized trajectory is a sequence of rotate-then-translate gait pairs. The control architecture integrates high-level and low-level controllers with real-time localization (using an overhead webcam). Terre-SoRo successfully navigates environments with obstacles where path re-planning is performed. To best of our knowledge, this is the first instance of real-time, closed-loop path planning of a non-pneumatic soft robot.

Soft robot modeling tends to prioritize soft robot dynamics in order to recover how they might behave. Soft robot design tends to focus on how to use compliant elements with actuation to effect certain canonical movement profiles. For soft robot locomotors, these profiles should lead to locomotion. Naturally, there is a gap between the emphasis of computational modeling and the needs of locomotion design. This paper proposes to consider modeling and computation efforts directed more toward understanding soft robot-world interactions with locomotion in mind. With a SMA-actuated inchworm as the soft robot to model and control, the framework is a combination of shape identification and geometric modeling that culminates in control equations of motion. When applied to the task of gait-based locomotion, the equations operate in a low dimensional shape-based gait space. Simulated and experimentally applied gaits for an inchworm model showed qualitatively similar outcomes, while the measured net displacement per gait cycle coincided within 9%. This result advances the idea that a shape-centric approach to soft robot modeling for control and locomotion may provide predictive locomotive models.

Robots built from soft materials can alter their shape and size in a particular profile. This shape-changing ability could be extremely helpful for rescue robots and those operating in unknown terrains and environments. In changing shape, soft materials also store and release elastic energy, a feature that can be exploited for effective robot movement. However, design and control of these moving soft robots are nontrivial. This paper presents design methodology for a 3-D printed motor-tendon actuated soft robot that is capable of locomotion. The modular design of the robot facilitates rapid fabrication, deployment, and repair. In addition to shape change, the robot uses friction-manipulation mechanisms to effect locomotion. The motor-tendon actuators are comprised of nylon tendons embedded inside the soft body structure along a given path with one end fixed on the body and the other attached to a motor. These actuators directly control the deformation of the soft body, which influences the robot locomotion behavior. Static stress analysis is used as a tool for designing the shape of the paths of these tendons embedded inside the body. This paper also presents a novel model-free learning-based control approach for soft robots, which interact with the environment at discrete contact points. This approach involves discretization of factors dominating robot-environment interactions as states, learning the results as robot transitions between these robot states, and evaluation of desired periodic state control sequences optimizing a cost function corresponding to a locomotion task (rotation or translation). The clever discretization allows the framework to exist in a robot's task space, hence facilitating calculation of control sequences without modeling the actuator, body material, or details of the friction mechanisms. The flexibility of the framework is experimentally explored by applying it to robots with different friction mechanisms and different shapes of tendon paths.

The deformable and continuum nature of soft robots promises versatility and adaptability. However, control of modular, multi-limbed soft robots for terrestrial locomotion is challenging due to the complex robot structure, actuator mechanics and robot-environment interaction. Traditionally, soft robot control is performed by modeling kinematics using exact geometric equations and finite element analysis.

Soft and continuum robots have the useful capability of adopting intricate postures and conforming to complex shapes. Furthermore, structures built from soft materials propagate mechanical energy from one part of the body to another, depending on its body shape, boundary condition, stiffness distribution, and so on. This makes the robots capable of producing a large number of force profiles to achieve useful behaviors and functionalities, even using a small number of actuators. Exploiting the soft mechanical property also enables to generate functional frictional forces, which is a key issue in controlling robot locomotion. In this paper, a highly deformable 3-D printed soft robot (PS robot) is presented, which is capable of generating complex, robust gaits on different inclines using a novel variable friction leg design. This design changes the frictional force depending on the robot posture and shape to facilitate robot locomotion. Shape memory alloy (SMA) coils are embedded into the robot in such a way that they act both as structural elements and actuators. This is the first soft robot platform produced by 3-D printing making fabrication simple and fast.