An Interconnected Soft Modular Robot with Locomotive Modules and Flexible Structures Actuated Through a Single Method.

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

The application of granular jamming in soft robotics is a recent and promising new technology offer exciting possibilities for creating higher performance robotic devices. Granular jamming is achieved via the application of a vacuum pressure inside a membrane containing particulate matter, and is particularly interesting from a design perspective, as a myriad of design parameters can potentially be exploited to induce a diverse variety of useful behaviours. To date, the effect of variables such as grain shape and size, as well as membrane material, have been studied as a means of inducing bespoke gripping performance, however the other main contributing factor, membrane morphology, has not been studied due to its particular complexities in both accurate modelling and fabrication. This research presents the first study that optimises membrane morphology for granular jamming grippers, combining multi-material 3D printing and an evolutionary algorithm to search through a varied morphology design space in materio. Entire generations are printed in a single run and gripper retention force is tested and used as a fitness measure. Our approach is relatively scalable, circumvents the need for modelling, and guarantees the real-world performance of the grippers considered. Results show that membrane morphology is a key determinant of gripper performance. Common high performance designs are seen to optimise all three of the main identified mechanisms by which granular grippers generate grip force, are significantly different from a standard gripper morphology, and generalise well across a range of test objects.

The variable stiffness capability can broaden the range of applications by providing both the adaptability through conformability inherent in soft robots and the ability to transmit large forces and high payloads in a rigid state. The stiffness modulation ability of compliant-bodied living creatures, according to the tasks, makes them interact efficiently with unstructured surroundings. The design of stiffness–modulation structures and methods, which can be well incorporated with the flexible body of a soft robot, can be informed by animal physiology. This study presents an echinoderm-inspired stiffness-modulation method with a rigid structure reinforced with porous material actuated by vacuum, causing deformation and compressive force. We demonstrate a continuative stiffness change (15-fold increase), and the device is shown to be mechanically programmable. Furthermore, we apply the proposed method to the gripper and robotic paw to demonstrate the advantages of stiffness modulation in the soft robotics. Consequently, it was demonstrated that the stiffness of a soft robot can be increased in situations requiring a higher load-bearing capacity or impact response tuning, while retaining its inherent adaptability.

This paper presents a novel piezoelectric‐driven and sensing soft robot designed to address the lack of intrinsic sensing capabilities in soft robotic systems. While significant progress is made in soft robot locomotion, most designs still rely on external sensors or complex embedded systems for environmental perception, increasing system complexity, weight, and energy consumption. Developing soft robots with integrated sensing functionalities is crucial for enhancing autonomous interaction and detection, particularly in unstructured environments. The proposed robot features a PVDF/Cu‐based piezoelectric flexible actuator and two asymmetrical arc‐shaped legs, fabricated using a simple and cost‐effective process. Finite element simulations and experiments reveal that its motion is influenced by structural dimensions, voltage amplitude, frequency, and waveform. The optimal configuration—11 mm body length, 6 mm rear leg, 4 mm front leg, 200 Vpp, and ≈100.56 Hz resonance frequency—enables slope climbing, obstacle traversal, and load carrying. Beyond locomotion, the robot leverages the inverse piezoelectric effect to sense mechanical wave signals, similar to how spiders detect web vibrations. By analyzing wave amplitudes, it identifies excitation sources, demonstrating potential for structural health monitoring, environmental sensing, and intelligent micro‐robotics.

Granular jamming grippers are a versatile and adaptable alternative for object manipulation in soft robotics (Amend et al., 2012; Brown et al., 2010; Shintake et al., 2018). Their operating principle is based on taking advantage of the variable stiffness of granular material when a vacuum is applied. This technology allows for adapting to complex geometries without the need for sensors or active control, making it especially useful for fragile or irregularly shaped objects (Delaney & Cleary, 2010). While progress has been made in topics such as the influence of grains on gripper performance, the influence of membranes, applications with fluids other than water, and collaborative robotics applications, the precise nature of contacts between the granular material and the target body during the grasping process remains unclear due to low visibility. Using discrete element method (DEM) simulations, an ovoid gripper containing the granular material is assembled, the interaction with the target body is simulated, and a vacuum is applied to achieve granular jamming (Dierks et al., 2025). This work aims to present an analysis of surface affectation on the target body, as well as its relationship with the granular material used in the gripper, modeling the target bodies with computer aided design (CAD) tools and simulating the interactions of the particles with discrete element method (DEM) it is evidenced that the contacts are concentrated in specific areas of the target body, depending on its geometry, and evolve as the gripper performs vertical displacement on the object, manifesting behaviors such as local interlocks and non-contact zones that change the total contact area and vary the grip force. These findings mark an improvement path for future designs of granular jamming grippers, as well as the integration of DEM simulations in early stages of adaptive manipulation device development.

Integrated consideration of production planning and maintenance processes is a real world assumption. Specifically, by improving the monitoring equipment such as various sensors or product-embedded information devices in recent years, joint assessment of these processes is inevitable for enhancing the level of the system optimization. By means of this equipment, managers can benefit from a condition-based maintenance (CBM) for monitoring and managing their system. The chief aim of the paper is to develop a stochastic maintenance problem based on CBM activities engaged with a complex applied production problem called flexible job shop scheduling problem (FJSP). This integrated problem considers two maintenance scenarios in terms of corrective maintenance (CM) and preventive maintenance (PM). The activation of scenario is done by monitoring the degradation condition of the system and comparing the associated value by predetermined PM and CM levels. Moreover, to make it more realistic, the developed problem allows breakdown of the system between inspection intervals. The event time and duration of performing the maintenance activities are also considered stochastic. The developed methodology copes with high stochastic complexity of proposed problem through simulation-based optimization (SBO) approach, which works based on harmony search optimization algorithm. The developed items of SBO are discussed on different generated test problems and assessed through statistical methods and new visualization approach. Numerical example shows that the proposed method is practical for proposed integrated CBM and FJSP problem. It also offers a basis for any implementation of CBM on production problems.

Despite their soft and compliant bodies, most of today’s soft robots have limitations when it comes to elongation or extension of their main structure. In contrast to this, a new type of soft robot called the eversion robot can grow longitudinally, exploiting the principle of eversion. Eversion robots can squeeze through narrow openings, giving the possibility to access places that are inaccessible by conventional robots. The main drawback of these types of robots is their limited bending capability due to the tendency to move along a straight line. In this paper, we propose a novel way to fuse bending actuation with the robot’s structure. We devise an eversion robot whose body forms both the central chamber that acts as the backbone as well as the actuators that cause bending and manoeuvre the manipulator. The proposed technique shows a significantly improved bending capability compared to externally attaching actuators to an eversion robot showing a 133% improvement in bending angle. Due to the increased manoeuvrability, the proposed solution is a step towards the employment of eversion robots in remote and difficult-to-access environments.

As a natural result of the continual growth and progress in soft robotics to establish fully compliant bodies primarily made of soft materials using additive manufacturing techniques, there are also increasing efforts towards realizing flexible and stretchable sensors which can seamlessly be embedded in soft robotic devices. In this study, we report on the performance evaluation of two soft strain sensors based on different fabrication techniques and soft materials. We fabricated one of them in a laboratory environment using a silicone rubber substrate (EcoFlex 0010) engraved with microchannels, which were filled with liquid carbon black (CB). The other one is a commercially available sensor (flexible stretch sensor, Images SI) made of a polymer which changes its resistance when stretched. The electromechanical responses and strain sensing performances of these two sensors are experimentally quantified and their usefulness in practical soft robotics applications is discussed. Although both sensors display a similar strain sensing performance, the commercial sensor is durable over a longer period of time while the microfluidic silicone sensor has a much smaller time constant, making it more suitable for applications with rapidly changing inputs.

As new tactile technologies continually aim to improve the perceptive capabilities of humanoid systems, multiplexed monitoring methods—particularly those using electrical impedance tomography (EIT) hardware—are rapidly gaining popularity in soft robotics. Notably, their potential for system‐level adaptability is promising: by multiplexing between different electrodes, the amount and type of information being generated can be altered in real time, balancing accuracy and speed requirements across new environments and tasks. To solidify this approach, we introduce an approach —Economy‐of‐Touch —which poses tactile sensing as an information‐budgeting problem, enabling operating points to be identified across different tasks. We first propose a three‐part method of quantifying task complexity, grounded in image‐based similarity measures (normalized cross‐correlation, Chamfer distance, and edge‐based convex hulls) which capture both visual similarity and spatial distribution of the tactile stimuli. Validating our approach with thousands of real‐world EIT experiments, we then derive a task‐agnostic mapping between this complexity and the required amount of information: the task's operating point. Three tactile tasks (localization, classification, and pattern recognition) are evaluated with real‐world experiments, across which we demonstrate operating point MAEs as low as 0.659 channels for previously unseen data.

1. Bidirectional Locomotion of Soft Inchworm Crawler Using Dynamic Gaits (Liang Du et al.) This paper investigates a crawling mechanism that enables a soft robot to perform bidirectional locomotion using body deformation. The authors propose two different differential friction forces integrated into the robot's body structure, allowing the robot to generate two different motion directions as the body deforms. With the proposed dynamic gaits, the robot can move in multiple directions with a simple system configuration and a minimalist actuation input. This paper provides an interesting example of how soft structure vibrations can be used for challenging robotic tasks.2. Controlling the motion of gas-lubricated adhesive disks using multiple vibration sources (Chengzhe Jia et al.) This paper discusses a new approach for robots that can generate adhesive force on a surface and move freely by overcoming their gravity. The authors developed a system that allows adhesion while moving along a surface using multiple vibration sources. The experiments show that gas-lubricated adhesive disks can move on the surface without any auxiliary operating system. This study constitutes a step towards constructing smallsized wireless robots that can overcome gravity and move freely in a general environment.This study considers a new design in which a wheeled robot is equipped with an enhanced pantograph-type suspension. The research includes mathematical modeling, computer simulation, and experimental tests of the robot's locomotion conditions. The results show the time dependencies of different kinematic parameters in the robot operating conditions and reveal the potential of this robot design for use in applications such as inspection and cleaning of pipelines. The study provides an essential roadmap by suggesting further analysis of power consumption, average speed, and operating efficiency.4. Robust self-propulsion in sand using simply controlled vibrating cubes (Bangyuan Liu et al.) This paper explores a self-propulsion mechanism via vibration as an alternative solution to the difficulty of moving on loose granular surfaces. The authors developed a cube-shaped robot with an embedded vibration motor and conducted various experiments on granular surfaces. The results show that such a robot can move faster and more stably on granular surfaces. The numerical simulations confirm that the robotmovement is driven by oscillations triggered at the distance from the center of mass. This simple design and control structure highlights vibratory locomotion as a valuable way to provide reliable movement on granular surfaces.These four studies present various innovative vibration-based solutions to enhance the locomotion capabilities of different robotic systems. The proposed innovative designs, such as soft robots, gas-lubricated adhesive disks, improved suspension systems, and vibrating cubes, aim to increase the efficiency of robots regardless of the type of soil. Each study makes significant contributions to the field of robotics by combining applied and theoretical knowledge to enhance the mobility of mobile robots in complex environmental conditions. In the future, the findings of these studies may inspire more advanced and efficient robot designs and applications.

This study proposes a simulation-based optimization approach to address the recharging scheduling problem of electric buses to minimize charging waiting time. Poor scheduling could lead to longer waiting times and potentially affect operation schedules regarding time and service quality. This study addresses a simulation-based optimization framework to evaluate various performance metrics during electric bus service, including waiting times, charging costs, and the utilization of charging piles. In this study, we propose a hybrid approach, simplified swarm optimization (SSO), which is an evolutionary algorithm with a backtracking (BT) mechanism and dynamic charging in a simulation framework. Based on the dynamic charging, SSO is used to determine the additional charging in terms of battery capacities, and a BT mechanism is employed to enhance algorithm efficiency and achieve breakthroughs in solution quality. A case study from Taiwan with 43 generated datasets was conducted in deterministic and stochastic situations to compare the effectiveness and efficiency among three charging rules (i.e., full charging rule, flexible charging rule, dynamic charging rule) and two algorithms (i.e., particle swarm optimization and SSO) The results indicate the superior performance in all scenarios by using a statistical test, which offers effective decision support for bus operators’ electric bus recharging scheduling.

Scenarios for the integration of renewable gases into the German natural gas market – A simulation-based optimisation approach

Optimization and control are important objectives across biology and biomedicine, and mathematical models are a key enabling technology. This paper reports a computational study of model-based multi-objective optimization in the setting of microbial ecology, using agent-based models. This modeling framework is well-suited to the field, but is not amenable to standard control-theoretic approaches. Furthermore, due to computational complexity, simulation-based optimization approaches are often challenging to implement. This paper presents the results of an approach that combines control-dependent coarse-graining with Pareto optimization, applied to two models of multi-species bacterial biofilms. It shows that this approach can be successful for models whose computational complexity prevents effective simulation-based optimization.

We present a simulation-regression based method for obtaining inventory policies for a two-echelon distribution system with service level constraints. Our motivation comes from a wholesale distributor in the consumer products industry with thousands of products that have different cost, demand, and lead time characteristics. We need to obtain good inventory policies quickly so that supply chain managers can run and analyze multiple scenarios effectively in reasonable amount of time. While simulation-based optimization approaches can be used, the time required to solve the inventory problem for a large number of products is prohibitive. On the other hand, available quick approximations are not guaranteed to provide satisfactory solutions. Our approach involves sampling the universe of products with different problem parameters, obtaining their optimal inventory policies via simulation-based optimization and then using regression methods to characterize the inventory policy for similar products. We show that our method obtains near-optimal policies and is quite robust.

Using structural and nonstructural measures for flood damage reduction is a long-standing problem in water resources planning and management. In the present study, an algorithm is presented for the optimal design of structural and nonstructural flood mitigation measures based on simulation-based optimization approach. For this purpose, the MIKE-11 simulation model, a one-dimensional hydrodynamic model, was used to calculate the potential damages of different flood scenarios under the various combinations of structural and nonstructural measures and this model was coupled with the NSGA-II multi-objective optimization model to provide the optimal Pareto solutions between two conflict objectives of minimizing the investment costs of flood mitigation measures and the potential damages of the floodplain. The proposed model was then applied to a small watershed in central part of Iran as a case study and the optimal trade-off solutions were calculated for different flood scenarios. Using these trade-offs, for each level of funding, decision makers can assign the optimal design of flood mitigation measures considering decision criteria.