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We present a dynamic multi-robot mapping framework that combines Blockchain technology for swarm management with a Hybrid Ant Colony Optimization (HACO) algorithm for path planning. Blockchain-based swarm contracts enable decentralized, transparent, and secure task allocation, acceptance, tracking, and reward distribution among multiple robots. HACO facilitates efficient path planning in complex environments through cooperative and competitive strategies. We deploy multiple LiDAR-equipped Unitree Go2 dog robots to collaboratively and competitively map divided sub-areas, with task reassignment based on real-time feedback and the selected strategy. In cooperative mode, robots share data to boost efficiency and accuracy; in competitive mode, they work independently to reduce redundancy and optimize resources. Swarm contracts also verify full sub-area coverage via the merged map. Results show that integrating blockchain-based management with HACO significantly enhances mapping performance, delivering a robust and scalable solution for realworld multi-robot systems. 

Soft robots, valued for their compliance and deformable nature, have demonstrated their outstanding abilities in complex environments. However, the nonlinear dynamics make it challenging to derive efficient locomotion patterns from analytical methods. This is largely due to the high computational cost associated with simulating soft-bodied models. Conversely, rigid-body models, such as those used in Gazebo, offer computational efficiency but cannot directly represent soft robots. We address these challenges by introducing customized Gazebo plugins that enable the simulation and analysis of soft robot locomotion dynamics. These plugins are complemented by a novel JointStiffnessPlugin, integrated with ROS services, for fine-tuning effort-controlled parameters. The system identification process is followed to match the simulation dynamics with the real soft robot to minimize the sim-to-real gap. Utilizing the proposed simulation framework and Bayesian Optimization, we derived a body-induced locomotion strategy that achieves enhanced efficiency. This strategy, relying solely on periodic spine bending and robot pose for forward propulsion, demonstrably reduces energy consumption compared to conventional gaits. Experimental results confirm a 42 % energy expenditure reduction relative to four-legged crawling. 

This paper presents a novel framework for memory-based navigation for terrestrial robots, utilizing a customized multimodal large language model (MLLM) to interpret visual inputs and generate navigation commands. The system employs a Unitree GO1 robot equipped with a camera to capture environmental images, which are processed by the customized MLLM for navigation. By leveraging a memory-based approach, the robot efficiently reuses previously traversed paths, reducing the need for re-exploration and enhancing navigation efficiency. The hybrid controller in this work features a deliberation unit and a reactive controller for high-level commands and robot alignment. Experimental validation in a hallway-like environment demonstrates that memory-driven navigation improves path retracing and overall performance. 

Fruit flies or Drosophila larvae exhibit a diverse range of locomotion gaits enabled by their soft, segmented bodies and intricate muscle arrangements. Their bodies, composed of multiple segments, are synchronously activated to propel forward through a combination of muscle elongation and contraction. Soft robotic systems, inspired by such biological marvels, face significant challenges in replicating these complex behaviors due to the intricate interplay between muscle activation, soft body dynamics, and frictional forces. To address these challenges, we propose a reduced-order model that captures the essential features of larval crawling. By modeling segments as a combination of prismatic and revolute joints, we can simulate the nonlinear motion resulting from muscle activation and body deformation. Our model demonstrates the potential of this approach to accurately describe larval movement, as validated by comparisons with actual larval trajectories. This research offers valuable insights into the design and control of soft robots and provides a framework for biologists to investigate the complex mechanisms of neuromuscular coordination in larvae. 

Soft robots hold significant potential in legged locomotion due to their inherent deformability, enabling enhanced adaptability to various environmental conditions and the generation of diverse locomotion gaits. While various soft robots have been proposed for terrestrial locomotion, research on dynamically-stable locomotion, such as trotting, with actuated soft bending limbs remains limited. We introduce a pneumatically-actuated soft quadruped featuring a soft body capable of a variety of dynamically-stable trotting locomotion. We utilize soft limb kinematics and parameterize fundamental limb locomotion to obtain quadrupedal locomotion trajectories for both linear and curvilinear motions. We also employ a physics-enabled dynamic model to optimize and evaluate trotting locomotion trajectories for dynamic stability. We further validate the stable locomotion trajectories through empirical experiments conducted on a soft quadruped prototype. The results demonstrate that the quadruped trots at a peak speed of 1.24 body lengths per second when traversing flat and uneven terrains, including slopes, cluttered areas, and naturalistic irregular surfaces. Furthermore, we compare the energy efficiency between trotting and crawling locomotion. The findings reveal that trotting is significantly more energy-efficient than crawling, with an average energy saving of up to 42%.Note to Practitioners—This paper was motivated by the challenge of achieving dynamically stable and efficient locomotion in soft quadrupeds. Many soft-legged robots are typically designed for statically stable, albeit inefficient and slow, locomotion gaits such as crawling. Our research aims to address this practical challenge of improving mobility in soft-legged robots. We develop a novel soft quadruped with pneumatically-actuated soft limbs that achieves efficient trotting that is 42% more energy-efficient than crawling. This work is particularly relevant for industries requiring adaptable and efficient navigation in environments, such as search and rescue, agricultural monitoring, and exploration. The development and optimization of trotting gaits through a physics-enabled dynamic model for dynamic stability provide a foundational framework for enhancing the adaptability and operational utility of soft robots. While our findings mark a significant step forward, challenges remain in deploying these locomotion strategies on autonomous untethered robots with onboard sensor feedback. Future research will focus on these areas, aiming to improve the practical deployment and robustness of soft robotic locomotive systems. 

Soft robots, due to their flexibility, adaptability, and gentle handling over rigid robots, have shown better potential in numerous applications requiring operating in constrained spaces. Most of the soft robotic prototypes are of a linear form that can be modeled as a curve in space and are found in manipulators and limbs of locomoting robots. Planar soft robots have been proposed recently that are modeled as a surface and deform in 3D. Research on planar soft robots has been less extensive due to the challenges associated with modeling surface deformations efficiently. We present a curve-parametric approach for the deformation modeling of planar soft robot modules. Along with the Bezier patch method to approximate the surface at 30 Hz. Experimental evaluations on a prototype were developed and tested to validate that the proposed model can reasonably approximate the planar robot boundaries, and the surface derived from it. 

Soft pneumatic actuators (SPAs) offer a promising alternative for biomedical applications requiring high sensitivity and precise manipulation due to their inherent compliance. 3D- printed multi-modal zig-zag SPAs exhibit potential in this area by achieving repeatable and precise shape changes due to their chambered design. However, achieving accurate position control remains a challenge. This work proposes a hybrid control strategy for multi-modal zig-zag SPAs that combines feed-forward and proportional-integral-derivative (PID) control to enhance positioning accuracy. A Pseudo Rigid Body (PRB) based inverse dynamic model is employed for the feed-forward component. The effectiveness of the controller is evaluated through extensive simulations and experiments. Results demonstrate that the hybrid control strategy achieves up to 29.5% and 31.6% improvement in accuracy compared to the PID and feed-forward controllers, respectively, within the operational bandwidth. 

Soft robots, known for their compliance and deformable nature, have emerged as a transformative field, giving rise to various prototypes and locomotion capabilities. Despite continued research efforts that have shown significant promise, the quest for energy-efficient mobility in soft-limbed robots remains relatively elusive. We introduce a discrete locomotion gait called “tumbling,” designed to conserve energy and implemented in a topologically symmetric soft-limbed robot. The incorporation of tumbling enhances the overall locomotive abilities of soft-limbed robots, offering advantages such as increased agility, adaptability, and the ability to correct orientation, which are essential for navigating non-engineered environments that include natural-like irregular terrains with obstacles. The principle behind tumbling locomotion involves a deliberate shift in the robot's center of gravity in the direction of motion, guided by the kinematics of its soft limbs. To validate this locomotion strategy, we developed a robot simulation model operating within a virtual environment that incorporates physics and contact interactions. After optimizing the tumbling locomotion strategy through simulations, we conducted experimental tests on a physical robot prototype. The experiments validate the effectiveness of the proposed tumbling gait. We conducted an energy cost analysis to compare the tumbling locomotion with the previously reported crawling gait of the robot. The results of this analysis demonstrate that tumbling represents an energy-efficient mode of locomotion for soft robots, saving up to 60% and 65% energy than crawling locomotion on flat and natural-like irregular terrains, respectively. 

The multimodal Zig-zag Soft Pneumatic Actuator (SPA) provides an effective design approach for achieving de- sired extensions and bending geometries under specific pressure conditions. The rigid body approximated model introduced in this study brings valuable insights into SPA dynamics by enabling faster simulations when compared to methods such as Finite Element Analysis (FEA). The model outlined in this paper forecasts static behavior by estimating the linear expansion of linear SPA and the bending angle of bending SPA. These two modes of motion can be combined to expand the degree of freedom. Depending on the configuration of the Strain Limiting Layer (SLL), the bending angle can be adjusted by controlling the actuator stiffness, a parameter that can be precisely characterized using the proposed actuator model. To address the hysteresis phenomena in linear expansion SPA, the Bouc-Wen hysteresis model is employed to model the actuator hysteresis responses at higher actuation rates. The validity of the proposed model is experimentally confirmed through the use of 3D-printed SPA prototypes that are designed for both extension and bending actuation. 

Multisection continuum arms are bio-inspired manipulators that combine compliance, payload, dexterity, and safety to serve as co-robots in human-robot collaborative domains. Their hyper redundancy and complex kinematics, however, pose many challenges when performing path planning, especially in dynamic environments. In this paper, we present a W-Space based Rapidly Exploring Random Trees * path planner for multisection continuum arm robots in dynamic environments. The proposed planner improves the existing state-of-art planners in terms of computation time and the success rate, while removing the need for offline computation. On average, the computation time of our approach is below 2 seconds, and its average success rate is around 70 %. The computation time of the proposed planner significantly improves that of the state-of-the-art planner by roughly a factor of 20, making the former suitable for real-time applications. Moreover, for application domains where the obstacle motion is not very predictable (e.g., human obstacles), the proposed planner significantly improves the success rate of state-of-the-art planners by nearly 50 %. Lastly, we demonstrate the feasibility of several generated trajectories by replicating the motion on a physical prototype arm.