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Many applications require the deployment of legged-robot teams to effectively and efficiently carry out missions. The use of multiple robots allows tasks to be executed concurrently, expediting mission completion. It also enhances resilience by enabling task transfer in case of a robot failure. This paper presents a formulation based on Mixed Integer Linear Programming (MILP) for allocating tasks to robots by taking into account travel time and ensuring efficient execution of collaborative tasks. We extended the MILP formulation to account for complexities with legged robot teams. Our results demonstrate that this approach leads to improved performance in terms of the makespan of the mission. We demonstrate the usefulness of this approach using a case study involving the disinfection of a building consisting of multiple rooms. 

Abstract Legged robots have a unique capability of traversing rough terrains and negotiating cluttered environments. Recent control development of legged robots has enabled robust locomotion on rough terrains. However, such approaches mainly focus on maintaining balance for the robot body. In this work, we are interested in leveraging the whole body of the robot to pass through a permeable obstacle (e.g., a small confined opening) with height, width, and terrain constraints. This paper presents a planning framework for legged robots manipulating their body and legs to perform collision-free locomotion through a permeable obstacle. The planner incorporates quadrupedal gait constraint, biasing scheme, and safety margin for the simultaneous body and foothold motion planning. We perform informed sampling for the body poses and swing foot position based on the gait constraint while ensuring stability and collision avoidance. The footholds are planned based on the terrain and the contact constraint. We also integrate the planner with robot control to execute the planned trajectory successfully. We validated our approach in high-fidelity simulation and hardware experiments on the Unitree A1 robot navigating through different representative permeable obstacles. 

Recent studies on quadruped robots have focused on either locomotion or mobile manipulation using a robotic arm. However, legged robots can manipulate large objects using non-prehensile manipulation primitives, such as planar pushing, to drive the object to the desired location. This paper presents a novel hierarchical model predictive control (MPC) for contact optimization of the manipulation task. Using two cascading MPCs, we split the loco-manipulation problem into two parts: the first to optimize both contact force and contact location between the robot and the object, and the second to regulate the desired interaction force through the robot locomotion. Our method is successfully validated in both simulation and hardware experiments. While the baseline locomotion MPC fails to follow the desired trajectory of the object, our proposed approach can effectively control both object's position and orientation with minimal tracking error. This capability also allows us to perform obstacle avoidance for both the robot and the object during the loco-manipulation task. 

Abstract Flexible sensors with accurate detection of environmental stimuli (e.g., humidity and chemical substances) have drawn increasing research interests in biomedical engineering and environmental science. However, most work is focused on isotropic sensing of liquid occurrence due to the limitation of material development, sensor design, and fabrication capability. 3D printing is used to build multifunctional flexible liquid sensors with multimaterials enabling anisotropic detection of microliquid droplets, and described herein. Electrical conductive composite hydrogels capable of detecting chemical liquid are developed with poly (ethylene diacrylate) (PEGDA) and multiwalled carbon nanotube (MWCNT). Due to the absorption of the liquid droplet and related swelling behavior, the resistance of PEGDA/MWCNT composite hydrogel increases dramatically, while the resistance of pure PEGDA hydrogel decreases significantly. Based on the two composite hydrogels and the related 3D printing method, a mesh‐shaped liquid sensor that can effectively identify the position and volume of liquid leakage in a short time is developed. Furthermore, a three‐layered liquid sensor to enable bidirectional monitor and detection of the liquid leakage in two different sides is demonstrated. The 3D‐printed liquid sensor offers a distinctive perspective on the potential applications in various fields for detection of liquid leakage in accurate position and direction. 

Abstract Microneedle arrays show many advantages in drug delivery applications due to their convenience and reduced risk of infection. Compared to other microscale manufacturing methods, 3D printing easily overcomes challenges in the fabrication of microneedles with complex geometric shapes and multifunctional performance. However, due to material characteristics and limitations on printing capability, there are still bottlenecks to overcome for 3D printed microneedles to achieve the mechanical performance needed for various clinical applications. The hierarchical structures in limpet teeth, which are extraordinarily strong, result from aligned fibers of mineralized tissue and protein‐based polymer reinforced frameworks. These structures provide design inspiration for mechanically reinforced biomedical microneedles. Here, a bioinspired microneedle array is fabricated using magnetic field‐assisted 3D printing (MF‐3DP). Micro‐bundles of aligned iron oxide nanoparticles (aIOs) are encapsulated by polymer matrix during the printing process. A bioinspired 3D‐printed painless microneedle array is fabricated, and suitability of this microneedle patch for drug delivery during long‐term wear is demonstrated. The results reported here provide insights into how the geometrical morphology of microneedles can be optimized for the painless drug delivery in clinical trials.