Mark Yim

Modular reconfigurable robot (MRR) systems have attracted growing attention because of their versatility and ability to operate across diverse domains. Research on MRR systems has made notable progress, evolving from laboratory settings to real-world applications. However, a gap remains in aligning MRR technologies with specific needs, and a comprehensive review of challenges in practical applications is still lacking. To address these issues, this Review adopts an application-oriented perspective. Focusing on locomotion, manipulation, and construction, it proposes a previously unreported perspective centered on real-world demands. The Review outlines these needs and identifies major challenges facing MRR systems, categorized into three areas: general hardware challenges, general software challenges, and application-specific challenges. This Review aims to inform the development of MRR systems from theory to practice through a demand-technology-application approach. By working backward from practical applications to technical requirements, it identifies technological gaps in specific use cases. It also surveys current research, highlighting strengths, limitations, and potential directions while emphasizing key challenges for future progress.

Robot-aided exploration of planetary surfaces is essential for understanding geologic processes, yet many scientifically valuable regions, such as Martian dunes and lunar craters, remain hazardous due to loose, deformable regolith. We present a scout-rover cooperation framework that expands safe access to such terrain using a hybrid team of legged and wheeled robots. In our approach, a high-mobility legged robot serves as a mobile scout, using proprioceptive leg-terrain interactions to estimate regolith strength during locomotion and construct spatially resolved terrain maps. These maps are integrated with rover locomotion models to estimate traversal risk and inform path planning. We validate the framework through analogue missions at the NASA Ames Lunar Simulant Testbed and the White Sands Dune Field. Experiments demonstrate (1) online terrain strength mapping from legged locomotion and (2) rover-specific traversal-risk estimation enabling safe navigation to scientific targets. Results show that scout-generated terrain maps reliably capture spatial variability and predict mobility failure modes, allowing risk-aware path planning that avoids hazardous regions. By combining embodied terrain sensing with heterogeneous rover cooperation, this framework enhances operational robustness and expands the reachable science workspace in deformable planetary environments.

Robot-aided exploration of planetary surfaces is essential for understanding geologic processes, yet many scientifically valuable regions, such as Martian dunes and lunar craters, remain hazardous due to loose, deformable regolith. We present a scout-rover cooperation framework that expands safe access to such terrain using a hybrid team of legged and wheeled robots. In our approach, a high-mobility legged robot serves as a mobile scout, using proprioceptive leg-terrain interactions to estimate regolith strength during locomotion and construct spatially resolved terrain maps. These maps are integrated with rover locomotion models to estimate traversal risk and inform path planning. We validate the framework through analogue missions at the NASA Ames Lunar Simulant Testbed and the White Sands Dune Field. Experiments demonstrate (1) online terrain strength mapping from legged locomotion and (2) rover-specific traversal-risk estimation enabling safe navigation to scientific targets. Results show that scout-generated terrain maps reliably capture spatial variability and predict mobility failure modes, allowing risk-aware path planning that avoids hazardous regions. By combining embodied terrain sensing with heterogeneous rover cooperation, this framework enhances operational robustness and expands the reachable science workspace in deformable planetary environments.

Additive and low-cost manufacturing techniques promise increased access to robotic actuation at the cost of mechanical precision. In this work, we employ principled Design of Experiments (DoE), including Taguchi orthogonal arrays, in parallel to sequential experimentation enabled by Bayesian Optimization (BO) for co-optimization of design and manufacturing parameters across two design case studies. We optimize for a combination of gear ratio and backdrivability in a 3D-printed compound Wolfrom bilateral gearbox. We also optimize for crack pressure and steady-state pressure differential of an injection-molded silicone check valve. Using BO, we find a 3D-printing compatible gear design with a gear ratio of 63.6 that backdrives without ever needing more than 0.35 Nm of input torque. This represents a 49% increase in ‘score’ over the Taguchi method. Similarly, we find a BO valve with lower combined crack and steady-state pressure errors than the Taguchi trials, decreasing cumulative error by 55%. Tracking model uncertainty throughout training, we conclude that further model training is necessary to reach optimal results in both cases. We further conclude that BO via the Ax platform is not yet as "plug-and-play" as Taguchi arrays.

Although miniaturization has been a goal in robotics for nearly 40 years, roboticists have struggled to access submillimeter dimensions without making sacrifices to onboard information processing because of the unique physics of the microscale. Consequently, microrobots often lack the key features that distinguish their macroscopic cousins from other machines, namely, on-robot systems for decision-making, sensing, feedback, and programmable computation. Here, we take up the challenge of building a robot comparable in size to a single-celled paramecium that can sense, think, and act using onboard systems for computation, sensing, memory, locomotion, and communication. Built massively in parallel with fully lithographic processing, these microrobots can execute digitally defined algorithms and autonomously change behavior in response to their surroundings. Combined, these results pave the way for general-purpose microrobots that can be programmed many times in a simple setup and can work together to carry out tasks without supervision in uncertain environments.

Abstract Drilling performance in the Cooper Basin has historically been constrained by a combination of abrasive sandstones, hard carbonate stringers, and plastic shales, which collectively create severe challenges for bit durability and directional control. Conventional roller-cone bits have offered robustness but limited rate of penetration (ROP), while earlier generations of polycrystalline diamond compact (PDC) bits frequently experienced rapid cutter wear, impact damage, or loss of stability when run in extended production hole sections. As a result, operators have faced high bit consumption and non-productive time (NPT) from multiple trips, directly impacting well costs. Recent advances in PDC bit design—including cutter technology, gauge reinforcement, and hydraulic optimization - have opened new opportunities to improve drilling efficiency in these deep and abrasive formations. At the same time, operators such as Santos have increasingly emphasized the need for durable bit solutions that not only sustain high ROP but also maintain toolface control when run with positive displacement motors (PDMs) in directional assemblies. This paper presents a case study from the Cooper Basin that demonstrates the successful application of a customized PDC bit specifically engineered for long production sections in deep directional wells. The new design incorporated reinforced gauge features, optimized back-raked cutters within carbide arrestors, and a robust cutting structure layout intended to balance durability with directional efficiency. The case study highlights the bit's ability to drill 2,380 m in a single run—including casing shoe drill-out—while maintaining hole quality and showing only minor cutter wear at pull-out. The results not only confirm the field-proven durability of the bit but also illustrate the potential for significant cost and time savings in similar deep basin environments.

This paper presents a Continuous Impact-Minimizing (CIM) rolling locomotion method for Variable-Topology Truss (VTT) robots, addressing limitations of conventional stepwise motion. Traditional VTT locomotion depends on discrete reference transitions, resulting in pauses, slow progress, and unintended ground impacts. Inertia-driven rotation at each step also generates impact forces on joints, raising durability concerns. CIM rolling continuously adjusts joint lengths by tracking the center of gravity in real time, enabling smoother motion and minimizing impacts. This approach allows VTTs to move directly to targets without unnecessary resets. Simulations validate the effectiveness of CIM rolling, demonstrating a 50% increase in speed and a 49% reduction in nodal impact force compared to conventional methods.

We propose a new formulation for the multi-robot task allocation problem that incorporates (a) complex precedence relationships between tasks, (b) efficient intra-task coordination, and (c) cooperation through the formation of robot coalitions. A task graph specifies the tasks and their relationships, and a set of reward functions models the effects of coalition size and preceding task performance. Maximizing task rewards is NP-hard; hence, we propose network flow-based algorithms to approximate solutions efficiently. A novel online algorithm performs iterative re-allocation, providing robustness to task failures and model inaccuracies to achieve higher performance than offline approaches. We comprehensively evaluate the algorithms in a testbed with random missions and reward functions and compare them to a mixed-integer solver and a greedy heuristic. Additionally, we validate the overall approach in an advanced simulator, modeling reward functions based on realistic physical phenomena and executing the tasks with realistic robot dynamics. Results establish efficacy in modeling complex missions and efficiency in generating high-fidelity task plans while leveraging task relationships.

Soft pneumatic actuators (SPA) made from elastomeric materials can provide large strain and large force. The behavior of locally strain-restricted hyperelastic materials under inflation has been investigated thoroughly for shape reconfiguration, but requires further investigation for trajectories involving external force. In this work we model force-pressure-height relationships for a concentrically strain-limited class of soft pneumatic actuators and demonstrate the use of this model to design SPA response for object lifting. We predict relationships under different loadings by solving energy minimization equations and verify this theory by using an automated test rig to collect rich data for n=22 Ecoflex 00-30 membranes. We collect data using an active learning pipeline to efficiently model the design space. We show that this learned model outperforms the theory-based model and a naive regression. We use our model to optimize membrane design for different lift tasks and compare this performance to other designs. These contributions represent a step towards understanding the natural response for this class of actuator and embodying intelligent lifts in a single-pressure input actuator system.

Modular robotics research has long been preoccupied with perfecting the modules themselves -- their actuation methods, connectors, controls, communication, and fabrication. This inward focus results, in part, from the complexity of the task and largely confines modular robots to sterile laboratory settings. The latest generation of truss modular robots, such as the Variable Topology Truss and the Truss Link, have begun to focus outward and reveal a key insight: the environment is not just a backdrop; it is a tool. In this work, we shift the paradigm from building better robots to building better robot environment interactions for modular truss robots. We study how modular robots can effectively exploit their surroundings to achieve faster locomotion, adaptive self-reconfiguration, and complex three-dimensional assembly from simple two-dimensional robot assemblies. By using environment features -- ledges, gaps, and slopes -- we show how the environment can extend the robots' capabilities. Nature has long mastered this principle: organisms not only adapt, but exploit their environments to their advantage. Robots must learn to do the same. This study is a step towards modular robotic systems that transcend their limitations by exploiting environmental features.

Recent progress in reinforcement learning (RL) and tactile sensing has significantly advanced dexterous manipulation. However, these methods often utilize simplified tactile signals due to the gap between tactile simulation and the real world. We introduce a sensor model for tactile skin that enables zero-shot sim-to-real transfer of ternary shear and binary normal forces. Using this model, we develop an RL policy that leverages sliding contact for dexterous inhand translation. We conduct extensive real-world experiments to assess how tactile sensing facilitates policy adaptation to various unseen object properties and robot hand orientations. We demonstrate that our 3-axis tactile policies consistently outperform baselines that use only shear forces, only normal forces, or only proprioception. Videos and details available on the project website.

Urban winds are a serious hazard for low-altitude autonomous aerial operations in urban airspaces. Previous methods for motion planning in urban winds require global knowledge of the obstacles and flow field and do not lend themselves to real-time application. In this paper, a planning and control framework is proposed for safe and energy-efficient navigation through urban flow fields that strictly relies on onboard sensing. The algorithm incorporates predictions of local wind flow fields into a receding horizon optimal controller, balancing energy consumption with obstacle avoidance on the fly to reach a goal destination. Simulation studies on a procedurally generated urban map with diverse wind conditions demonstrate that the energy-aware motion planner reduces energy consumption by as much as 30% and results in 32% fewer crashes on average compared to the wind-agnostic baseline. Comparisons to a global wind-aware planner indicate only minor trade-offs associated with planning on a local horizon.

Steerable needles offer a minimally invasive method to deliver treatment to hard-to-reach tissue regions. We introduce a new class of tape-spring steerable needles capable of sharp turns ranging from 15 to 150 degrees with a turn radius as low as 3 mm, which minimizes surrounding tissue damage. In this work, we derive and experimentally validate a geometric model for our steerable needle design. We evaluate both manual and robotic steering of the needle along a Dubins path in 7 kPa and 13 kPa tissue phantoms, simulating our target clinical application in healthy and unhealthy liver tissue. We conduct experiments to measure needle robustness to stiffness transitions between non-homogeneous tissues. We demonstrate progress towards clinical use with needle tip tracking via ultrasound imaging, navigation around anatomical obstacles, and integration with a robotic autonomous steering system.

Nature manufactures structures via decentralized processes involving groups of agents. This is fundamentally different from traditional manufacturing, where objects are produced via sequences of predefined steps. In this work, we explore the idea of using simulated "swarms" of simple agents to generate new designs for architected materials in a decentralized, bioinspired manner. Individual agents choose their own actions based solely on information in their immediate environment, with no centralized control. The structures that these processes produce are the result of the collective action of the individual agents, rather than a predetermined design. We build an integrated platform for determining "rule-structure-property" relationships, analogous to process-structure-property relationships in materials science. The platform simulates agent behaviors to show how different rules and different environments result in different structures. We then three-dimensional print these and perform finite element analysis to experimentally and numerically characterize mechanical properties, including tensile strength and energy dissipation.

This paper focuses on how to make a growing Vine robot steer in different directions with a novel approach to real-time steering control by autonomously applying adhesive tape to induce a surface wrinkles. This enabling real-time directional control with arbitrary many turns while maintaining the robot's soft structure. This system feeds growing material external to the tube. The design achieves fixed-angle turns in 2D space. Through experimental validation, we demonstrate repeated 21-degree turns using a Dubins path planner with minimal error, establishing a foundation for more versatile Vine robot applications. This approach combines real-time control, multi-degree-of-freedom steering, and structural flexibility, addressing key challenges in soft robotics.

THE sense of touch is an indispensable requirement for humans to effectively interact with the physical world around them and perform dexterous tasks. Similarly, this should be no different for robots. Imagine, for example, a robot that can open a bottle of medicine and dispense pills to an elderly person. Although this might seem a straightforward task for a human, it remains a significant challenge for a robot. Critically, the completion of the task depends on tactile sensing: the robot needs to receive and interpret the feedback from interacting with the bottle, determine the appropriate force based on the size and hardness of the pills, and adjust its pose to safely dispense them. Each step involves contact-rich interactions that can only be effectively deciphered through tactile sensing. Typically, tactile sensing works in conjunction with other modalities, such as vision, enabling the robot to adjust its actions dynamically and complete the task. In response to this vision of robots interacting with the physical world through touch, tactile robotics has now emerged as a key research area. Tactile robots can be defined as intelligent systems equipped with tactile sensors that can extract and process tactile data to guide their operations and interactions. The development of tactile robots presents scientific challenges, ranging from the design and fabrication of tactile sensors to methodologies for processing tactile data, integrating tactile feedback into task execution, and combining it with other sensory modalities to improve robot perception. As a result, tactile robotics demands collaborative efforts across several disciplines, involving material and data scientists …

Despite their growing popularity, swarms of robots remain limited by the operating time of each individual. We present algorithms that allow a human to sculpt a swarm of robots into a shape that persists in space perpetually, independent of onboard energy constraints, such as batteries. Robots generate a path through a shape such that robots cycle in and out of the shape. Robots inside the shape react to human initiated changes and adapt the path through the shape accordingly. Robots outside the shape recharge and return to the shape so that the shape can persist indefinitely. The presented algorithms communicate shape changes throughout the swarm using message passing and robot motion. These algorithms enable the swarm to persist through any arbitrary changes to the shape. We describe these algorithms in detail and present their performance in simulation and on a swarm of mobile robots. The result is a swarm behavior more suitable for extended duration, dynamic shape-based tasks in applications, such as entertainment, agriculture, and emergency response.

Abstract Steerable needles offer a minimally invasive method to deliver treatment to hard-to-reach tissue regions. We introduce a new class of tape-spring steerable needles capable of sharp turns ranging from 15 to 150 degrees with a turn radius as low as 3mm, which minimizes surrounding tissue damage. In this work, we derive and experimentally validate a geometric model for our steerable needle design. We evaluate both manual and robotic steering of the needle along a Dubins path in 7 kPa and 13 kPa tissue phantoms, simulating our target clinical application in healthy and unhealthy liver tissue. We conduct experiments to measure needle robustness to stiffness transitions between non-homogeneous tissues. We demonstrate progress towards clinical use with needle tip tracking via ultrasound imaging, navigation around anatomical obstacles, and integration with a robotic autonomous steering system.

Communication and position sensing are among the most important capabilities for swarm robots to interact with their peers and perform tasks collaboratively.However, the hardware required to facilitate communication and position sensing is often too complicated, expensive, and bulky to be carried on swarm robots.Here we present Maneuverable Piccolissimo 3 (MP3), a minimalist, single motor drone capable of executing inter-robot communication via infrared light and triangulationbased sensing of relative bearing, distance, and elevation using message arrival time.Thanks to its novel design, MP3 can communicate with peers and localize itself using simple components, keeping its size and mass small and making it inherently safe for human interaction.Here we present the hardware and software design of MP3 and demonstrate its capability to localize itself, fly stably, and maneuver in the environment using peer-to-peer communication and sensing.

Obtaining accurate and timely predictions of the wind through an urban environment is a challenging task, but has wide-ranging implications for the safety and efficiency of autonomous aerial vehicles in future urban airspaces. Prior work relies strongly on global information about the environment, such as a precise map of the city and in-situ wind measurements at various locations, to run expensive computational fluid dynamics solvers to predict the entire wind flow field. In contrast, this letter introduces a new method to estimate the wind flow field in a region around the robot in real time, utilizing on-board range measurements to sense nearby buildings and sparse wind measurements to infer windspeed and direction. We propose that this information sufficiently characterizes the structure of the wind flow field in the local region of interest. To that end, we introduce a deep learning-based approach to predict local flow fields from range measurements. Our results indicate that a neural network trained on numerous simulated winds through small randomized maps is capable of reconstructing local wind flows while generalizing to larger environments with over 200 buildings. This contribution empowers computationally-constrained aerial robots to reason about the structure of local wind flow fields, thereby enabling new planning, control, and estimation strategies in windy urban environments without <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of the map.

Recent progress in reinforcement learning (RL) and tactile sensing has significantly advanced dexterous manipulation. However, these methods often utilize simplified tactile signals due to the gap between tactile simulation and the real world. We introduce a sensor model for tactile skin that enables zero-shot sim-to-real transfer of ternary shear and binary normal forces. Using this model, we develop an RL policy that leverages sliding contact for dexterous in-hand translation. We conduct extensive real-world experiments to assess how tactile sensing facilitates policy adaptation to various unseen object properties and robot hand orientations. We demonstrate that our 3-axis tactile policies consistently outperform baselines that use only shear forces, only normal forces, or only proprioception. Website: https://jessicayin.github.io/tactile-skin-rl/

This paper presents the design of a compliant spherical joint for the Variable Topology Truss (VTT) system that enables topology self-reconfiguration. Self-reconfiguration is a common feature of modular robot systems. In a truss-type modular system, self-reconfiguration requires care to prevent collapse and control truss position for docking. This paper proposes maintaining structure stability while disconnecting truss members during reconfiguration by adding joint stiffness between members. Keeping the members in equilibrium allows predictive positioning to aid reconfiguration. In this study, we present the self-reconfiguration of a tetrahedral VTT system as a simple demonstration. The required stiffness value for the spherical joint mechanism was calculated by statics simulation. A joint and docking prototype is demonstrated with docking to show the feasibility of this proposed self-reconfiguration procedure.

Communication and position sensing are among the most important capabilities for swarm robots to interact with their peers and perform tasks collaboratively. However, the hardware required to facilitate communication and position sensing is often too complicated, expensive, and bulky to be carried on swarm robots. Here we present Maneuverable Piccolissimo 3 (MP3), a minimalist, single motor drone capable of executing inter-robot communication via infrared light and triangulation-based sensing of relative bearing, distance, and elevation using message arrival time. Thanks to its novel design, MP3 can communicate with peers and localize itself using simple components, keeping its size and mass small and making it inherently safe for human interaction. We present the hardware and software design of MP3 and demonstrate its capability to localize itself, fly stably, and maneuver in the environment using peer-to-peer communication and sensing.

A minimal number of rigid constraints makes soft robots versatile, but many of these robots use soft pneumatic actuators (SPAs) designed to inflate through a single trajectory. In an unloaded actuator, this trajectory is dictated by the arrangement of in-extensible and elastic materials. External strain limiters can be added post-fabrication to SPAs, but these are passive devices. In this paper, we offer design and control techniques for an electrically active strain limiter that is easily adhered to existing SPAs to provide signal-controlled force output. These sheathed electroadhesive (EA) clutches apply antagonistic forces through the constitutive properties of their silicone sheathing and through the variable friction of the clutch itself. We are able to design the sheathing to passively support loads or minimize passive stiffness. We control clutch forces via an augmented pulse-width-modulation (PWM) of the high voltage square-wave input. We perform an initial, empirical characterization on the system with tensile material testing. The clutch system resists motion with sustained forces ranging from 0.5N to 22N. We then demonstrate its ability to apply predictable nonconservative work in a dynamic catching task, where it can limit catching height from 15cm to 1cm. Finally, we attach it to an inverse pneumatic artificial muscle (IPAM) to show that variable strain limitation can control position of the SPA endpoint.

Despite their growing popularity, swarms of robots remain limited by the operating time of each individual. We present algorithms which allow a human to sculpt a swarm of robots into a shape that persists in space perpetually, independent of onboard energy constraints such as batteries. Robots generate a path through a shape such that robots cycle in and out of the shape. Robots inside the shape react to human initiated changes and adapt the path through the shape accordingly. Robots outside the shape recharge and return to the shape so that the shape can persist indefinitely. The presented algorithms communicate shape changes throughout the swarm using message passing and robot motion. These algorithms enable the swarm to persist through any arbitrary changes to the shape. We describe these algorithms in detail and present their performance in simulation and on a swarm of mobile robots. The result is a swarm behavior more suitable for extended duration, dynamic shape-based tasks in applications such as agriculture and emergency response.

This paper presents a preliminary study on the development of dynamic rolling locomotion for Variable Topology Truss (VTT) robots. Rolling locomotion allows the robot to move in any direction, which is advantageous for various terrains. However, they are constantly affected by the relatively slow speed and impact. We propose the 2D and 3D dynamic models with a contact force simulation. Based on the calculation, the simulation model could show the movement of VTT.

This paper explores the design of Compliant Nodes to improve a modular robot called Variable Topology Truss (VTT), allowing it to change shapes automatically. The study also suggests practical methods for using these nodes effectively. In earlier VTT models, nodes were limited to spherical joints, making it challenging to maintain their shape when constraints were incomplete. This often required manual reassembly to modify the robot’s structure. To overcome this, current research focuses on adding flexibility to the nodes, enabling them to reshape autonomously. In this effort, the paper examines the design concepts of the nodes and establishes conditions for their movement once new hardware is developed. The study identifies key features and shapes that nodes should have, contributing significantly to future research in the field. These proposed improvements aim to address past challenges, making it easier for the VTT robot to transform its structure seamlessly and automatically.

Steerable needles are a novel technology that offers a wide range of uses in medical diagnostics and therapeutics. Currently, there exist several steerable needle designs in the literature, however, they are limited in their use by the number of possible turns, turn radius, and tissue damage. We introduce a novel design of a tape spring steerable needle, capable of multiple turns, that minimizes tissue damage. In this study, we measure the turning radius of our steerable needle in porcine liver tissue in vitro with ultrasound and estimate tissue damage in gel blocks using image analysis and 3D plaster casting. We were able to demonstrate our steerable needle's ability to steer through biological tissue, as well as introduce a novel method for estimating tissue damage. Our findings show that our needle design showed lower damage compared to similar designs in literature, as well as tissue stiffness being a protective factor against tissue damage.

Micro aerial vehicles are making a large impact in applications such as search-and-rescue, package delivery, and recreation. Unfortunately, these diminutive drones are currently constrained to carrying small payloads, in large part because they use propellers optimized for larger aircraft and inviscid flow regimes. Fully realizing the potential of emerging microflyers requires next-generation propellers that are specifically designed for low Reynolds number conditions and that include new features advantageous in highly viscous flows. One aspect that has received limited attention in the literature is the addition of roughness to propeller blades as a method of reducing drag and increasing thrust. To investigate this possibility, we used direct numerical simulation to conduct a numerical investigation of smooth and rough propellers. Our results indicate that roughness produces a 2% increase in thrust and a 5% decrease in power relative to a baseline smooth propeller operating at the same Reynolds number of Rec = 6500, held constant by rotational speed. We complement our numerical findings using thrust-stand-based experiments of 3D-printed propellers identical to those of the numerical simulations. Our study indicates that surface roughness is an additional parameter within the design space for micro-propellers, which may offer improved drone efficiencies and payloads.

This paper proposes a new locomotion algorithm for truss robots on irregular terrain, in particular, for the Variable Topology Truss (VTT) system. The previous Polygon-based Random Tree (PRT) search algorithm for support polygon generation is extended to irregular terrain while considering friction and internal force limitations. By characterizing terrain, unreachable areas are excluded from search to increase efficiency. A one-step rolling motion primitive is generated based on the kinematics, statics, and constraints of VTT. The locomotion planning is completed by transforming and connecting multiple motion primitives with respect to the desired support polygons. The algorithm's performance is verified by conducting simulations in multiple types of environments.

We introduce a multi-modal reconfiguration planner for the Variable Topology Truss (VTT) modular robot system. The VTT system is a truss-architecture modular self-reconfigurable robot. When a VTT is restricted to a single topology, the collision constraints between the truss members divide the configuration space into many connected components, which makes collision-free planning difficult. This new planner leverages a mathematical invariant based on link theory to find topological reconfiguration actions that can connect these different regions and make progress towards a goal. We show that this planner is effective at finding paths between configurations with different truss topologies.

We propose a mechanism for low Reynolds num-ber walking (e.g., legged microscale robots). Whereas locomotion for legged robots has traditionally been classified as dynamic (where inertia plays a role) or static (where the system is always statically stable), we introduce a new locomotion modality we call buoyancy enabled non-inertial dynamic walking in which inertia plays no role, yet the robot is not statically stable. Instead, falling and viscous drag play critical roles. This model assumes squeeze flow forces from fluid interactions combined with a well timed gait as the mechanism by which forward motion can be achieved from a reciprocating legged robot. Using two physical demonstrations of robots with Reynold's number ranging from 0.0001 to 0.02 (a microscale robot in water and a centimeter scale robot in glycerol) we find the model qualitatively describes the motion. This model can help understand microscale locomotion and design new microscale walking robots including controlling forward and backwards motion and potentially steering these robots.

Abstract There are times when robots or robot parts must be improvised from found materials. These times include disaster recovery scenarios, lunar or martian exploration or in areas that are highly resource constrained such as third world countries. For these situations, found materials can be used to construct new robots or robotic elements such as exo-skeletons or orthoses as well as repair damaged components or structures. This paper quantifies the use of various fastening techniques for attaching together unstructured members, such as tree branches, with actuators to act as the building blocks for robotic links and joints. We quantify which fastener styles are ideal for fixed joints, or for revolute and spherical joints. The techniques considered range from more traditional methods such as diagonal and square lashings to using nuts and bolts to connect components together. In each area we analyze the pros and cons of the technique employed from a complexity, accuracy, and strength perspective. The techniques are then employed to build arm and wrist orthoses as well as a robotic gripper and walker. The range of motion of the orthoses are analyzed. The branch gripper has comparable performance to a commercially available gripper in terms of size and payload. In each case we discuss the pros and cons of these designs and how they can be improved upon.

Modular self-assembling systems typically assume that modules are present to assemble. But in sparsely observed ocean environments modules of an aquatic modular robotic system may be separated by distances they do not have the energy to cross, and the information needed for optimal path planning is often unavailable. In this work we present a flow-based rendezvous and docking controller that allows aquatic robots in gyre-like environments to rendezvous with and dock to a target by leveraging environmental forces. This approach does not require complete knowledge of the flow, but suffices with imperfect knowledge of the flow's center and shape. We validate the performance of this control approach in both simulations and experiments relative to naive rendezvous and docking strategies and show that energy efficiency improves as the scale of the gyre increases.

Abstract Objective To make steerable needles more effective, researchers have been trying to minimize turning radius, develop mechanics-based models, and simplify control. This paper introduces a novel cable-driven steerable needle that has a 3mm turning radius based on tape spring mechanics, which sets a new minimum turn radius in stiffness-matched tissue models. Methods: We characterize the turn radius and the forces that affect control and performance and create predictive models to estimate required insertion forces and maximum insertion depth. Finally, we demonstrate the performance of a task outside the capabilities of a conventional needle. Results: Minimal force is required to maintain bends, allowing surrounding tissue to fix them in place, and minimal energy is required to propagate bends, allowing the device to navigate easily through various tissue phantoms. The turn radius of the device is independent of surrounding tissue stiffness, making for simple and precise control. We show that all aspects of performance depend on minimizing the tip cutting force. Under ultrasound guidance, we successfully navigate into and then follow a deep blood vessel model at a steep angle of approach. Conclusion: This design allows the system to accurately control the direction of the device while maintaining a smaller turn radius than other steerable needles, providing the potential to broaden access to challenging targets in patients.

We present the Maneuverable Piccolissimo 2 (MP2), an autonomous, controllable, single motor micro aerial vehicle (MAV). The small size of MP2 makes it safe to operate in the presence of humans, and its simple design facilitates the creation of large swarms of capable MAVs. MP2 is equipped with on-board sensing capabilities and uses active environmental beacons to compute its three-dimensional position and yaw orientation. Its novel design enables autonomous takeoff, flight, and landing while maintaining a small, simple form factor. We describe a feedback controller and demonstrate its feasibility in a series of flight tests that display position holding, step response, and path following capabilities. The results indicate that MP2 is capable of controlled autonomous 3D flight with only one actuator.

Truss robots are highly redundant parallel robotic systems that can be applied in a variety of scenarios. The variable topology truss (VTT) is a class of modular truss robots. As self-reconfigurable modular robots, a VTT is composed of many edge modules that can be rearranged into various structures depending on the task. These robots change their shape by not only controlling joint positions as with fixed morphology robots but also reconfiguring the connectivity between truss members in order to change their topology. The motion planning problem for VTT robots is difficult due to their varying morphology, high dimensionality, the high likelihood for self-collision, and complex motion constraints. In this article, a new motion planning framework to dramatically alter the structure of a VTT is presented. It can also be used to solve locomotion tasks that are much more efficient compared with previous work. Several test scenarios are used to show its effectiveness.