Matthew Travers

Abstract Introduction: Tumor Treating Fields (TTFields) are a noninvasive cancer therapy that employ alternating electric fields in the 100-500 kHz range to disrupt tumor cell division. TTFields are clinically approved for glioblastoma, mesothelioma, and non-small cell lung cancer. The LB10000 is a novel TTFields device designed to deliver high-dose fields adaptively across large body regions. Unlike existing systems that use four fixed transducer arrays, the LB10000 employs a large matrix of programmable transducers whose phases can be dynamically switched between 0°, 180°, and off. This enables dynamic field shaping, targeted delivery to multiple anatomical sites, and effective thermal management. We present both in-vivo and in-silico evidence demonstrating the safety of this new system. Methods: Six female Yucatan pigs were treated with the LB10000. Arrays were removed daily between 7-9 a.m. and reapplied between 12-3 p.m., targeting ≥16 hours of active treatment per day for 24-32 days within a period of 40 days from treatment initiation. If a pig reached the desired quota of per-protocol days, treatment was stopped and the animal euthanized. During treatment, skin temperature, treatment duty cycle, current, and voltage were continuously monitored, and animal well-being was evaluated daily by a veterinarian. Following euthanasia, gross examination of major organs was performed. To assess safety in humans, computational modeling was performed using the Sim4Life (ZMT Zurich, Switzerland) ohmic solver. Virtual LB10000 arrays were applied to anthropomorphic phantoms—DUKE (adult male), ELLA (adult female), and FATS (obese male)—and electric field delivery simulated at 200 Vpp, the device’s maximum output. Specific Absorption Rate (SAR) distributions were calculated to evaluate potential heating. Results: In vivo, the LB10000 delivered fields continuously at 130 V and 6 A (∼100 W) with 78-90 % on-time per animal. Five of six pigs completed the full protocol(24-32 day with 16hr/day treatment), totaling 2,780 hours of treatment without adverse events or tissue injury. Gross examination revealed no abnormalities. In silico, SAR values in superficial skin layers were 10-100× higher than in internal organs, indicating that any potential heating is localized beneath the transducers. The risk of thermal damage to tissues increases drastically when tissue temperatures exceed 109°F (43°C). The LB10000 continuously monitors skin temperature below the transudcers and controls delivered power to maintain skin temperature below a safety threshold of 105 °F. Together, these findings demonstrate that the LB10000 operates safely at high doses in vivo and that the risk that the device will cause thermal damage in humans is negligible. Conclusion: These results provide key evidence supporting advancement toward first-in-human clinical evaluation. Citation Format: Ze'ev Bomzon, Scott Krywick, Matthew Travers, Kenneth L. Watkins, Martin Pribula, Michael Winegar, Peter Travers. From simulation to reality: Evidence supporting safe high-dose TTFields delivery with the LB10000 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7199.

Abstract Introduction: Tumor treating fields (TTFields) are a non-invasive cancer therapy employing alternating electric fields in the 100-500 kHz range to disrupt mitosis. Current TTFields systems use four fixed transducer arrays positioned on the skin near the tumor. This static configuration imposes two critical limitations: (1) electric fields remain confined between arrays, precluding treatment of multifocal or metastatic disease. (2) Delivery of TTFields through the skin leads to localized heating of the skin below the transducers. To avoid thermal damage to tissue, the power delivered must be controlled in a manner that maintains skin temperature below a safety limit of around 105°F, and if skin temperature exceeds this threshold, the device must temporarily halt field delivery until the skin cools to a safe temperature, leading to a reduction in delivered dose. The LB10000 system was engineered to overcome these barriers. It comprises a large array of individually addressable transducers whose phases can be dynamically modulated between three states (0°, 180°, off). This enables adaptive field steering, effective heat dispersion, and targeted energy delivery across the body. We present experimental and computational evidence demonstrating that the LB10000 delivers TTFields to multiple targets at doses substantially exceeding those achievable with existing systems. Methods: The LB10000 was applied to six female Yucatan pigs for 30-40 days to assess sustained high-dose delivery. Arrays were removed daily between 7-9 a.m. and reapplied between 12-3 p.m., targeting ≥16 hours of treatment per day. Skin temperature, treatment duty cycle, and current-voltage output were continuously recorded. To assess dose-delivery in humans, simulations were performed using the Sim4Life (ZMT Zurich, Switzerland) platform. Virtual arrays were applied to the DUKE (adult male), ELLA (adult female), and FATS (obese male) anatomical phantoms, with power levels matched to those measured in vivo. Electric field distributions were computed for tumors in multiple organ sites. Results: In vivo, the LB10000 delivered continuous fields at 130 V and 6 A (≈100 W[SK1] ) with 78-90[SK2] [זב3] % on-time, maintaining skin temperatures within safety limits and confirming robust thermal control. Simulations demonstrated that the LB10000 achieved mean intratumoral field intensities exceeding 2 V/cm in lung and liver targets—representing at least a two-fold increase over reported values from existing TTFields devices. Conclusions: The LB10000 enables adaptive, high-dose TTFields delivery with effective thermal regulation and extended coverage, supporting treatment of disseminated or multifocal disease. This technology represents a new paradigm for TTFields therapy, with the potential to significantly broaden its clinical impact. Citation Format: Ze'ev Bomzon, Scott Krywick, Matthew Travers, Kenneth L. Watkins, Martin Pribula, Michael Winegar, Peter Travers. High-dose adaptive tumor treating fields (TTFields): Expanding the boundaries of electric field therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7200.

Efficient coverage of unknown environments requires robots to adapt their paths in real time based on on-board sensor data. In this paper, we introduce CAP, a connectivity-aware hierarchical coverage path planning algorithm for efficient coverage of unknown environments. During online operation, CAP incrementally constructs a coverage guidance graph to capture essential information about the environment. Based on the updated graph, the hierarchical planner determines an efficient path to maximize global coverage efficiency and minimize local coverage time. The performance of CAP is evaluated and compared with five baseline algorithms through high-fidelity simulations as well as robot experiments. Our results show that CAP yields significant improvements in coverage time, path length, and path overlap ratio.

For robots to operate autonomously in densely cluttered environments, they must reason about and potentially physically interact with obstacles to clear a path. Safely clearing a path on challenging terrain, such as a cluttered staircase, requires controlled interaction. For example, a quadrupedal robot that pushes objects out of the way with one leg while maintaining a stable stance with its three other legs. However, tightly coupled physical actions, such as one-legged pushing, create new constraints on the system that can be difficult to predict at design time. In this work, we present a new method that addresses one such constraint, wherein the object being pushed by a quadrupedal robot with one of its legs becomes occluded from the robot's sensors during manipulation. To address this challenge, we present a tightly coupled perception-action framework that enables the robot to perceive clutter, reason about feasible push paths, and execute the clearing maneuver. Our core contribution is an interaction-aware state estimation loop that uses proprioceptive feedback regarding foot contact and leg position to predict an object's displacement during the occlusion. This prediction guides the perception system to robustly re-detect the object after the interaction, closing the loop between action and sensing to enable accurate tracking even after partial pushes. Using this feedback allows the robot to learn from physical outcomes, reclassifying an object as immovable if a push fails due to it being too heavy. We present results of implementing our approach on a Boston Dynamics Spot robot that show our interaction-aware approach achieves higher task success rates and tracking accuracy in pushing objects on stairs compared to open-loop baselines.

<div class="section abstract"><div class="htmlview paragraph">We introduce a LiDAR inertial odometry (LIO) framework, called LiPO, that enables direct comparisons of different iterative closest point (ICP) point cloud registration methods. The two common ICP methods we compare are point-to-point (P2P) and point-to-feature (P2F). In our experience, within the context of LIO, P2F-ICP results in less drift and improved mapping accuracy when robots move aggressively through challenging environments when compared to P2P-ICP. However, P2F-ICP methods require more hand-tuned hyper-parameters that make P2F-ICP less general across all environments and motions. In real-world field robotics applications where robots are used across different environments, more general P2P-ICP methods may be preferred despite increased drift. In this paper, we seek to better quantify the trade-off between P2P-ICP and P2F-ICP to help inform when each method should be used. To explore this trade-off, we use LiPO to directly compare ICP methods and test on relevant benchmark datasets as well as on our custom unpiloted ground vehicle (UGV). We find that overall, P2F-ICP has reduced drift and improved mapping accuracy, but, P2P-ICP is more consistent across all environments and motions with minimal drift increase.</div></div>

<div class="section abstract"><div class="htmlview paragraph">We develop a set of communications-aware behaviors that enable formations of robotic agents to travel through communications-deprived environments while remaining in contact with a central base station. These behaviors enable the agents to operate in environments common in dismounted and search and rescue operations. By operating as a mobile ad-hoc network (MANET), robotic agents can respond to environmental changes and react to the loss of any agent. We demonstrate in simulation and on custom robotic hardware a methodology that constructs a communications network by “peeling-off” individual agents from a formation to act as communication relays. We then present a behavior that reconfigures the team’s network topology to reach different locations within an environment while maintaining communications. Finally, we introduce a recovery behavior that enables agents to reestablish communications if a link in the network is lost. Our hardware trials demonstrate the systems capability to operate in real-world environments.</div></div>

Reliable communication networks are essential for the remote operation of automated teams of robotic agents. For unknown (no prior map) communications-deprived (no existing communication infrastructure) environments, the robotic agents must construct the network as the robots move through the terrain. We present a novel method for automated network construction tailored for mobile robotic teams that require communication with a central base station. Our key innovation is the introduction of a maximin spanning tree structure, which guarantees a minimum level of communication performance between nodes. By directly optimizing node placement based on signal-based metrics, instead of relying on geometric surrogates like distance and visibility, we also achieve significant decreases in agent utilization while maintaining coverage for the traversed area. By using the robotic agents themselves as mobile repeaters in a communication network, each robotic agent can be individually assigned to prioritize network connectivity during critical operations. Numerical simulations on common Multi-Agent Path Finding benchmarks demonstrate up to a 36% reduction in the number of required nodes compared to existing techniques. Furthermore, this work guarantees robust network connectivity in dynamic environments, outperforming strongest-neighbor approaches that are vulnerable to link disruptions. Lastly, hardware tests confirm the robustness of our method in challenging scenarios encountered in real-world deployments.

Future ground logistics missions will require multiple robots to travel in a convoy between locations. As each location may require a different number of robots (e.g. resupply vehicles), these missions will require a mutable convoy formation structure that may be divided to meet operational needs at each location. We model this mission type by modifying the vehicle routing problem with multiple synchronizations (VRPMS) to enforce convoy constraints (VRPMS-CC). This centralized approach to organizing and routing convoys is represented as a graph-based routing problem and then solved as a mixed integer program. A solution of the VRPMS-CC forms convoys by ensuring that agents participating in the same convoy remain spatially and temporally coupled, traversing the same edge of the graph simultaneously. We demonstrate our approach through numerical studies, where we route up to six simulated agents through twenty convoying tasks, and on robotic hardware. These demonstrations motivate two further contributions to specialize our approach to robotic systems. We introduce: 1) a warm-starting heuristic that improves solver times by up to eighty-nine percent and 2) an online multi-depot variant of the VRPMS-CC that responds to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> unknown impassable environmental obstacles.

Autonomous robot navigation in complex environments requires robust perception as well as high-level scene understanding due to perceptual challenges, such as occlusions, and uncertainty introduced by robot movement. For example, a robot climbing a cluttered staircase can misinterpret clutter as a step, misrepresenting the state and compromising safety. This requires robust state estimation methods capable of inferring the underlying structure of the environment even from incomplete sensor data. In this letter, we introduce a novel method for robust state estimation of staircases. To address the challenge of perceiving occluded staircases extending beyond the robot's field-of-view, our approach combines an infinite-width staircase representation with a finite endpoint state to capture the overall staircase structure. This representation is integrated into a Bayesian inference framework to fuse noisy measurements enabling accurate estimation of staircase location even with partial observations and occlusions. Additionally, we present a segmentation algorithm that works in conjunction with the staircase estimation pipeline to accurately identify clutter-free regions on a staircase. Our method is extensively evaluated on real robots across diverse staircases, demonstrating significant improvements in estimation accuracy and segmentation performance compared to baseline approaches.

Efficient coverage of unknown environments requires robots to adapt their paths in real time based on on-board sensor data. In this paper, we introduce CAP, a connectivity-aware hierarchical coverage path planning algorithm for efficient coverage of unknown environments. During online operation, CAP incrementally constructs a coverage guidance graph to capture essential information about the environment. Based on the updated graph, the hierarchical planner determines an efficient path to maximize global coverage efficiency and minimize local coverage time. The performance of CAP is evaluated and compared with five baseline algorithms through high-fidelity simulations as well as robot experiments. Our results show that CAP yields significant improvements in coverage time, path length, and path overlap ratio.

Autonomous robot navigation in complex environments requires robust perception as well as high-level scene understanding due to perceptual challenges, such as occlusions, and uncertainty introduced by robot movement. For example, a robot climbing a cluttered staircase can misinterpret clutter as a step, misrepresenting the state and compromising safety. This requires robust state estimation methods capable of inferring the underlying structure of the environment even from incomplete sensor data. In this paper, we introduce a novel method for robust state estimation of staircases. To address the challenge of perceiving occluded staircases extending beyond the robot's field-of-view, our approach combines an infinite-width staircase representation with a finite endpoint state to capture the overall staircase structure. This representation is integrated into a Bayesian inference framework to fuse noisy measurements enabling accurate estimation of staircase location even with partial observations and occlusions. Additionally, we present a segmentation algorithm that works in conjunction with the staircase estimation pipeline to accurately identify clutter-free regions on a staircase. Our method is extensively evaluated on real robot across diverse staircases, demonstrating significant improvements in estimation accuracy and segmentation performance compared to baseline approaches.

Abstract Active colloids are modular assemblies of distinct micro‐ and nanoscale components that can perform complex robotic tasks. While recent advances in templated assembly methods enable high‐throughput fabrication of multi‐material active colloids, their limitations reduce the ability to construct flexibly linked colloidal systems, restricting their complexity, agility, and functionality. Here, templated assembly by selective removal (TASR) is leveraged to construct multicomponent colloidal microstructures that are connected with compliant DNA nanotube linkages. Polycarbonate heat (PCH) molding is employed to create high‐surface‐energy templates for improved polystyrene microsphere assembly via TASR. This increase in template surface energy improves microsphere assembly by more than 100‐fold for two‐sphere microstructures. An inverse relationship between microstructure complexity (i.e., the number of microspheres) and assembly yields is observed. PCH‐assisted TASR is leveraged to construct larger colloidal structures containing up to 26 microspheres, multi‐sphere microrotors, and structurally homogeneous populations of flexibly linked, two‐sphere microswimmers that locomote in fluid environments. Real‐time modification of a microswimmer is also demonstrated through nuclease‐mediated degradation of the DNA linkages, highlighting the DNA‐enabled reconfiguration and responsiveness capabilities of these microswimmers. These results establish PCH‐assisted TASR as a versatile method for constructing flexibly linked, modular microrobots with controlled geometry, enhanced agility, and dynamic response.

In this paper, we present a novel motion planning algorithm that inherits the strengths of both optimization and search-based planners. Optimization-based planners use the gradient of an objective function to generate a desired path, whereas search-based planners operate on a graph capturing the salient topology of a robot’s free space. A class of optimization-based planners leverages prior information, modeled as a probability distribution of target locations in an environment, to guide path generation. We embrace one specific measure, referred to as ergodicity, which encourages a robot to spend a proportion of its time, weighted by the distribution, where it is likely to find targets of interest. Methods that minimize ergodicity were not designed to handle obstacles in the environment, and augmented approaches that add "soft" constraints for obstacles to the cost function may still yield a path that collides with an obstacle. In this work, we present a hybrid approach that first generates a graph of the environment’s free space, followed by searching the graph with ergodicity as a heuristic. Our approach not only restricts the search to the free space, thereby avoiding obstacles by design, but also generates trajectories with low ergodicity values. Extensive testing on 125 test scenarios with varying degrees of clutter, information distribution, and robot start locations illustrate the efficacy of our algorithm.

Centralized control of a multi-agent system improves upon distributed control especially when multiple agents share a common task e.g., sorting different materials in a recycling facility. Traditionally, each agent in a sorting facility is tuned individually which leads to suboptimal performance if one agent is less efficient than the others. Centralized control overcomes this bottleneck by leveraging global system state information, but it can be computationally expensive. In this work, we propose a novel framework called Longitudinal Control Volumes (LCV) to model the flow of material in a recycling facility. We then employ a Kalman Filter that incorporates local measurements of materials into a global estimation of the material flow in the system. We utilize a model predictive control algorithm that optimizes the rate of material flow using the global state estimate in real-time. We show that our proposed framework outperforms distributed control methods by 40-100% in simulation and physical experiments.

Field robotics applications, such as search and rescue, involve robots operating in large, unknown areas. These environments present unique challenges that compound the difficulties faced by a robot operator. The use of multi-robot teams, assisted by carefully designed autonomy, help reduce operator workload and allow the operator to effectively coordinate robot capabilities. In this work, we present a system architecture designed to optimize both robot autonomy and the operator experience in multi-robot scenarios. Drawing on lessons learned from our team's participation in the DARPA SubT Challenge, our architecture emphasizes modularity and interoperability. We empower the operator by allowing for adjustable levels of autonomy ("sliding mode autonomy"). We enhance the operator experience by using intuitive, adaptive interfaces that suggest context-aware actions to simplify control. Finally, we describe how the proposed architecture enables streamlined development of new capabilities for effective deployment of robot autonomy in the field.

Ice worlds are at the forefront of astrobiological interest because of the evidence of subsurface oceans. Enceladus in particular is unique among the icy moons because there are known vent systems that are likely connected to a subsurface ocean, through which the ocean water is ejected to space. An existing study has shown that sending small robots into the vents and directly sampling the ocean water is likely possible. To enable such a mission, NASA's Jet Propulsion Laboratory is developing a snake-like robot called Exobiology Extant Life Surveyor (EELS) that can navigate Enceladus' extreme surface and descend an erupting vent to capture unaltered liquid samples and potentially reach the ocean. However, navigating to and through Enceladus' environment is challenging: Because of the limitations of existing orbital reconnaissance, there is substantial uncertainty with respect to its geometry and the physical properties of the surface/vents; communication is limited, which requires highly autonomous robots to execute the mission with limited human supervision. Here, we provide an overview of the EELS project and its development effort to create a risk-aware autonomous robot to navigate these extreme ice terrains/environments. We describe the robot's architecture and the technical challenges to navigate and sense the icy environment safely and effectively. We focus on the challenges related to surface mobility, task and motion planning under uncertainty, and risk quantification. We provide initial results on mobility and risk-aware task and motion planning from field tests and simulated scenarios.

Centralized control of a multi-agent system improves upon distributed control especially when multiple agents share a common task e.g., sorting different materials in a recycling facility. Traditionally, each agent in a sorting facility is tuned individually which leads to suboptimal performance if one agent is less efficient than the others. Centralized control overcomes this bottleneck by leveraging global system state information, but it can be computationally expensive. In this work, we propose a novel framework called Longitudinal Control Volumes (LCV) to model the flow of material in a recycling facility. We then employ a Kalman Filter that incorporates local measurements of materials into a global estimation of the material flow in the system. We utilize a model predictive control algorithm that optimizes the rate of material flow using the global state estimate in real-time. We show that our proposed framework outperforms distributed control methods by 40-100% in simulation and physical experiments.

This paper introduces NEO, a novel autonomy framework for controlling a versatile high- degree-of-freedom (DOF) robots such as EELS (a screw-driven snake-like robot), aimed at exploring unknown and extreme environments like the geysers of Enceladus or the subsurface oceans of icy worlds. Distinct from conventional Mars mission strategies, NEO embodies resilience, adaptivity, and risk awareness. NEO supports fault-aware perception using both exteroception and proprioception, inspired by a blind climber’s feat of scaling El Capitan. NEO tightly couples planning, perception, and control, along with leveraging machine-learning- based methods for adaptation. Moreover, NEO incorporates risk-aware decision making with integrated task and motion planning under consideration of uncertainty, enabling autonomous adaptation of actions to mitigate risks and maximize mission success. This paper presents the architecture of NEO, along with experimental results showcasing these capabilities and discusses the potential for NEO in spearheading a new paradigm in space exploration.

The discovery of ocean worlds such as Enceladus, Titan, and Europa motivates the development of versatile autonomous mobility systems to enable the next era of space exploration where there is large uncertainty in terrain specifications due to a lack of prior surface reconnaissance missions. To explore these environments, we propose Exobiology Extant Life Surveyor (EELS): the first large-scale (4 lm long with 400 Nm peak torque) snake robot. The large scale is achieved by using a screw-based active skin mechanism to decouple motion and shape control. Autonomous mobility for such a system remains an open problem due to its many Degrees of Freedom (DoFs), complex terrain interactions, and intermittent localization failures in GPS-denied perceptually degraded environments due to the presence of fog, dust, featureless terrains, etc. We propose NEO, an autonomy architecture that scales to large DoFs to generate a versatile set of gaits to achieve mobility in unknown extreme environments. We also discuss the resilience capabilities of NEO that achieves closed-loop tracking performance by leveraging exteroception when available but can also operate with proprioception only, leading to resiliency against localization failures via graceful degradation in performance rather than unsafe behaviors. A quantitative hardware evaluation of exteroceptive leader-follower gait is performed indoors on synthetic ice along with qualitative results of field deployment of the proprioceptive leader-follower and sidewinding gaits in extreme environments of icy and sandy terrains with mobility-stressing elements such as trenches, undulations, and steep slopes (up to 35 degrees). We present a set of lessons learned from field deployments with a summary of challenges and open research problems. Video: www.rohanthakker.in/eels-neo-autonomy.html

Modular robots can be rearranged into a new design, perhaps each day, to handle a wide variety of tasks by forming a customized robot for each new task. However, reconfiguring just the mechanism is not sufficient: each design also requires its own unique control policy. One could craft a policy from scratch for each new design, but such an approach is not scalable, especially given the large number of designs that can be generated from even a small set of modules. Instead, we create a modular policy framework where the policy structure is conditioned on the hardware arrangement, and use just one training process to create a policy that controls a wide variety of designs. Our approach leverages the fact that the kinematics of a modular robot can be represented as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">design graph</i> , with nodes as modules and edges as connections between them. Given a robot, its design graph is used to create a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">policy graph</i> with the same structure, where each node contains a deep neural network, and modules of the same type share knowledge via shared parameters (e.g., all legs on a hexapod share the same network parameters). We developed a model-based reinforcement learning algorithm, interleaving model learning and trajectory optimization to train the policy. We show the modular policy generalizes to a large number of designs that were not seen during training without any additional learning. Finally, we demonstrate the policy controlling a variety of designs to locomote with both simulated and real robots.

Autonomous robot navigation in austere environments is critical to missions like “search and rescue”, yet it remains difficult to achieve. The recent DARPA Subterranean Challenge (SubT) highlights prominent challenges including GPS-denied navigation through rough terrains, rapid exploration in large-scale three-dimensional (3D) space, and interrobot coordination over unreliable communication. Solving these challenges requires both mechanical resilience and algorithmic intelligence. Here, we present our approach that leverages a fleet of custom-built heterogeneous robots and an autonomy stack for robust navigation in challenging environments. Our approach has demonstrated superior navigation performance in the SubT Final Event, resulting in the fastest traversal and most thorough exploration of the environment, which won the “Most Sectors Explored Award.” This paper details our approach from two aspects: mechanical designs of a marsupial ground-and-aerial system to overcome mobility challenges and autonomy algorithms enabling collective rapid exploration. We also provide lessons learned in the design, development and deployment of complex but resilient robotic systems to overcome real-world navigation challenges.

Robotic systems need advanced mobility capabili-ties to operate in complex, three-dimensional environments designed for human use, e.g., multi-level buildings. Incorporating some level of autonomy enables robots to operate robustly, reliably, and efficiently in such complex environments, e.g., automatically “returning home” if communication between an operator and robot is lost during deployment. This work presents a novel method that enables mobile robots to robustly operate in multi-level environments by making it possible to autonomously locate and climb a range of different staircases. We present results wherein a wheeled robot works together with a quadrupedal system to quickly detect different staircases and reliably climb them. The performance of this novel staircase detection algorithm that is able to run on the heterogeneous platforms is compared to the current state-of-the-art detection algorithm. We show that our approach significantly increases the accuracy and speed at which detections occur.

Magnetically-actuated swimming microrobots are an emerging tool for navigating and manipulating materials in confined spaces. Recent work has demonstrated that it is possible to build such systems at the micro and nanoscales using polymer microspheres, magnetic particles and DNA nanotechnology. However, while these materials enable an unprecedented ability to build at small scales, such systems often demonstrate significant polydispersity resulting from both the material variations and the assembly process itself. This variability makes it difficult to predict, let alone optimize, the direction or magnitude of microswimmer velocity from design parameters such as link shape or aspect ratio. To isolate questions of a swimmer's design from variations in its physical dimensions, we present a novel experimental platform using two-photon polymerization to build a two-link, buoyant milliswimmer with a fully customizable shape and integrated flexible linker (the swimmer is underactuated, enabling asymmetric cyclic motion and net translation). Our approach enables us to control both swimming direction and repeatability of swimmer performance. These studies provide ground truth data revealing that neither the first order nor second order models currently capture the key features of milliswimmer performance. We therefore use our experimental platform to develop design guidelines for tuning the swimming speeds, and we identify the following three approaches for increasing speed: (1) tuning the actuation frequency for a fixed aspect ratio, (2) adjusting the aspect ratio given a desired range of operating frequencies, and (3) using the weaker value of linker stiffness from among the values that we tested, while still maintaining a robust connection between the links. We also find experimentally that spherical two-link swimmers with dissimilar link diameters achieve net velocities comparable to swimmers with cylindrical links, but that two-link spherical swimmers of equal diameter do not.

Practical deployments of coordinated fleets of mobile robots in different environments have revealed the benefits of maintaining small distances between robots, especially as they move at higher speeds. However, this is counter-intuitive in that as speed increases, reducing the amount of space between robots also reduces the time available to the robots to respond to sudden motion variations in surrounding robots. However, in certain examples, the benefits in performance due to traveling at closer distances can outweigh the potential instability issues, for instance, autonomous trucks on highways that optimize energy by vehicle “drafting” or smaller robots in cluttered environments that need to maintain close, line of sight communication, etc. To achieve this kind of closely coordinated fleet behavior, this work introduces a model predictive optimal control framework that directly takes non-linear dynamics of the vehicles in the fleet into account while planning motions for each robot. The robots are able to follow each other closely at high speeds by proactively making predictions and reactively biasing their responses based on state information from the adjacent robots. This control framework is naturally decentralized and, as such, is able to apply to an arbitrary number of robots without any additional computational burden. We show that our approach is able to achieve lower inter-robot distances at higher speeds compared to existing controllers. We demonstrate the success of our approach through simulated and hardware results on mobile ground robots.