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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. 

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 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. 

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. 

Image data plays a pivotal role in the current data-driven era, particularly in applications such as computer vision, object recognition, and facial identification. Google Maps ® stands out as a widely used platform that heavily relies on street view images. To fulfill the pressing need for an effective and distributed mechanism for image data collection, we present a framework that utilizes smart contract technology and open-source robots to gather street-view image sequences. The proposed framework also includes a protocol for maintaining these sequences using a private blockchain capable of retaining different versions of street views while ensuring the integrity of collected data. With this framework, Google Maps ® data can be securely collected, stored, and published on a private blockchain. By conducting tests with actual robots, we demonstrate the feasibility of the framework and its capability to seamlessly upload privately maintained blockchain image sequences to Google Maps ® using the Google Street View ® Publish API. 

“Data is the new oil” has become a popular catch-phrase in the world of technology, emphasizing the immense value of data in today's digital age. Most services and platforms rely on data, but collecting this data can be challenging and costly. To address this issue, we leverage a novel distributed crowdsourcing framework - termed Swarm Contracts - that utilizes blockchain and is applied to robotics technologies. The framework encourages an incentivized crowdsourcing model through open-source robots and a secure, decentralized, and transparent blockchain-based incentive system. As a demonstration of the framework's capabilities, we use it to collect Google Street View ® map data, which can be a resource-intensive task to keep up to date using traditional centralized methods. Our Swarm Contract framework uses Google Street View ® Publish API, which allows for the contribution of street view data to Google Maps @to implement the incentive-based crowdsourcing of street view images. By incorporating a swarm contract-powered framework with the Google Street View ® Publish API, we show that the incentivized crowdsourcing of street view data can be a practical solution to maintain accurate and up-to-date Google Street View ® maps. 

Soft robotic snakes (SRSs) have a unique combination of continuous and compliant properties that allow them to imitate the complex movements of biological snakes. Despite the previous attempts to develop SRSs, many have been limited to planar movements or use wheels to achieve locomotion, which restricts their ability to imitate the full range of biological snake movements. We propose a new design for the SRSs that is wheelless and powered by pneumatics, relying solely on spatial bending to achieve its movements. We derive a kinematic model of the proposed SRS and utilize it to achieve two snake locomotion trajectories, namely side winding and helical rolling. These movements are experimentally evaluated under different gait parameters on our SRS prototype. The results demonstrate that the SRS can successfully mimic the proposed spatial locomotion trajectories. This is a significant improvement over the previous designs, which were either limited to planar movements or relied on wheels for locomotion. The ability of the SRS to effectively mimic the complex movements of biological snakes opens up new possibilities for its use in various applications. 

Soft robotic snakes made of compliant materials can continuously deform their bodies and, therefore, mimic the biological snakes' flexible and agile locomotion gaits better than their rigid-bodied counterparts. Without wheel support, to date, soft robotic snakes are limited to emulating planar locomotion gaits, which are derived via kinematic modeling and tested on robotic prototypes. Given that the snake locomotion results from the reaction forces due to the distributed contact between their skin and the ground, it is essential to investigate the locomotion gaits through efficient dynamic models capable of accommodating distributed contact forces. We present a complete spatial dynamic model that utilizes a floating-base kinematic model with distributed contact dynamics for a pneumatically powered soft robotic snake. We numerically evaluate the feasibility of the planar and spatial rolling gaits utilizing the proposed model and experimentally validate the corresponding locomotion gait trajectories on a soft robotic snake prototype. We qualitatively and quantitatively compare the numerical and experimental results which confirm the validity of the proposed dynamic model.