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The incorporation of non-covalent albumin binding moieties (ABMs) into radiotracers results in increased circulation time, leading to a higher uptake in the target tissues such as the tumor, and, in some cases, reduced kidney retention. We previously developed [18F]AlF NOTA-K(ABM)-αvβ6-BP, where αvβ6-BP is a peptide with high affinity for the cell surface receptor integrin αvβ6 that is overexpressed in several cancers, and the ABM is an iodophenyl-based moiety. [18F]AlF NOTA-K(ABM)-αvβ6-BP demonstrated prolonged blood circulation compared to the non-ABM parent peptide, resulting in high, αvβ6-targeted uptake with continuously improving detection of αvβ6(+) tumors using PET/CT. To further extend the imaging window beyond that of fluorine-18 (t1/2 = 110 min) and to investigate the pharmacokinetics at later time points, we radiolabeled the αvβ6-BP with copper-64 (t1/2 = 12.7 h). Two peptides were synthesized without (1) and with (2) the ABM and radiolabeled with copper-64 to yield [64Cu]1 and [64Cu]2, respectively. The affinity of [natCu]1 and [natCu]2 for the integrin αvβ6 was assessed by enzyme-linked immunosorbent assay. [64Cu]1 and [64Cu]2 were evaluated in vitro (cell binding and internalization) using DX3puroβ6 (αvβ6(+)), DX3puro (αvβ6(-)), and pancreatic BxPC-3 (αvβ6(+)) cells, in an albumin binding assay, and for stability in both mouse and human serum. In vivo (PET/CT imaging) and biodistribution studies were done in mouse models bearing either the paired DX3puroβ6/DX3puro or BxPC-3 xenograft tumors. [64Cu]1 and [64Cu]2 were synthesized in ≥97% radiochemical purity. In vitro, [natCu]1 and [natCu]2 maintained low nanomolar affinity for integrin αvβ6 (IC50 = 28 ± 3 and 19 ± 5 nM, respectively); [64Cu]1 and [64Cu]2 showed comparable binding to αvβ6(+) cells (DX3puroβ6: ≥70%, ≥42% internalized; BxPC-3: ≥19%, ≥12% internalized) and ≤3% to the αvβ6(-) DX3puro cells. Both radiotracers were ≥98% stable in human serum at 24 h, and [64Cu]2 showed a 6-fold higher binding to human serum protein than [64Cu]1. In vivo, selective uptake in the αvβ6(+) tumors was observed with tumor visualization up to 72 h for [64Cu]2. A 3-5-fold higher αvβ6(+) tumor uptake of [64Cu]2 vs [64Cu]1 was observed throughout, at least 2.7-fold improved BxPC-3-to-kidney and BxPC-3-to-blood ratios, and 2-fold improved BxPC-3-to-stomach ratios were noted for [64Cu]2 at 48 h. Incorporation of an iodophenyl-based ABM into the αvβ6-BP ([64Cu]2) prolonged circulation time and resulted in improved pharmacokinetics, including increased uptake in αvβ6(+) tumors that enabled visualization of αvβ6(+) tumors up to 72 h by PET/CT imaging.

The purpose of this study was to develop and evaluate two αvβ6-targeted fluorescent imaging agents. The integrin subtype αvβ6 is significantly upregulated in a wide range of epithelial derived cancers, plays a key role in invasion and metastasis, and expression is often located at the invasive edge of tumors. αvβ6-targeted fluorescent imaging agents have the potential to guide surgical resection leading to improved patient outcomes. Both imaging agents were based on the bi-PEGylated peptide NH2-PEG28-A20FMDV2-K16R-PEG28 (1), a peptide that has high affinity and selectivity for the integrin αvβ6: (a) 5-FAM-X-PEG28-A20FMDV2-K16R-PEG28 (2), and (b) IRDye800-PEG28-A20FMDV2-K16R-PEG28 (3). Peptides were synthesized using solid-phase peptide synthesis and standard Fmoc chemistry. Affinity for αvβ6 was evaluated by ELISA. In vitro binding, internalization, and localization of 2 was monitored using confocal microscopy in DX3puroβ6 (αvβ6+) and DX3puro (αvβ6−) cells. The in vivo imaging and ex vivo biodistribution of 3 was evaluated in three preclinical mouse models, DX3puroβ6/DX3puro and BxPC-3 (αvβ6+) tumor xenografts and a BxPC-3 orthotopic pancreatic tumor model. Peptides were obtained in > 99% purity. IC50 values were 28 nM (2) and 39 nM (3). Rapid αvβ6-selective binding and internalization of 2 was observed. Fluorescent intensity (FLI) measurements extracted from the in vivo images and ex vivo biodistribution confirmed uptake and retention of 3 in the αvβ6 positive subcutaneous and orthotopic tumors, with negligible uptake in the αvβ6-negative tumor. Blocking studies with a known αvβ6-targeting peptide demonstrated αvβ6-specific binding of 3. Two fluorescence imaging agents were developed. The αvβ6-specific uptake, internalization, and endosomal localization of the fluorescence agent 2 demonstrates potential for targeted therapy. The selective uptake and retention of 3 in the αvβ6-positive tumors enabled clear delineation of the tumors and surgical resection indicating 3 has the potential to be utilized during image-guided surgery.

The incorporation of non-covalent albumin binding moieties (ABMs) into radiotracers results in increased circulation time, leading to a higher uptake in the target tissues such as the tumor, and, in some cases, reduced kidney retention. We previously developed [

The purpose of this study was to develop and evaluate two αPeptides were synthesized using solid-phase peptide synthesis and standard Fmoc chemistry. Affinity for αPeptides were obtained in 99% purity. ICTwo fluorescence imaging agents were developed. The α

Payload manipulation with aerial robots has been an active research area for many years. Recent approaches have sought to plan, control, and execute maneuvers with large, yet deliberate, load swings for more agile, energy-optimal maneuvering. Unfortunately, the system's nonlinear dynamics make executing such trajectories a significant challenge and experimental demonstrations thus far have relied completely on a motion capture system and non-negligible simplifications like restriction of the system to a two-dimensional workspace or closing of the control loop on the quadrotor, instead of the payload. In this work, we observe the payload using a downward-facing camera and estimate its state relative to the quadrotor using an extended Kalman filter. We demonstrate closed-loop payload control in the full three-dimensional workspace, with the planning, estimation, and control pipeline implemented on an onboard processor. We show control of load swings up to 53o from the vertical axis. To the best of our knowledge, this represents the first realization of closed-loop control of agile slung-load maneuvers and the largest achieved payload angle.

In this work, we present Hold Or take Optimal Plan (HOOP), a centralized trajectory generation algorithm for labeled multi-robot systems operating in obstacle-free, two-dimensional, continuous workspaces. Given a team of N robots, each with nth-order dynamics, our algorithm finds trajectories that navigate vehicles from their start positions to non-interchangeable goal positions in a collision-free manner. The algorithm operates in two phases. In the motion planning step, a geometric algorithm finds a collision-free, piecewise-linear trajectory for each robot. In the trajectory generation step, each robot’s trajectory is refined into a higher-order piecewise polynomial with a quadratic program. The novelty of our method is in this problem decomposition. The motion plan, through abstracting away robots’ dynamics, can be found quickly. It is then subsequently leveraged to construct collision avoidance constraints for N decoupled quadratic programs instead of a single, coupled optimization problem, decreasing computation time. We prove that this method is safe, complete, and generates smooth trajectories that respect robots’ dynamics. We demonstrate the algorithm’s practicality through extensive quadrotor experiments.

This article was updated to correct the axes in Figures 4e and 5d.

In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tackle the unlabeled multi-robot motion planning problem with motion constraints as a multi-agent reinforcement learning problem with some sparse global reward. In contrast with previous works, which formulate an entirely new hand-crafted optimization cost or trajectory generation algorithm for a different robot dynamic model, our framework is a general approach that is applicable to arbitrary robot models. Further, by using the velocity obstacle, we devise a smooth projection that guarantees collision free trajectories for all robots with respect to their neighbors and obstacles. The efficacy of our algorithm is demonstrated through varied simulations. A video describing our method and results can be found here.

In this paper, we consider the problem of planning collision-free trajectories to navigate a team of labeled robots from a set of start locations to a set of goal locations, where robots have pre-assigned and non-interchangeable goals. We present a solution to this problem for a centralized team operating in an obstacle-free, two-dimensional workspace. Our algorithm allows robots to follow Optimal Motion Plans (OMPs) to their goals when possible and has them enter Circular HOlding Patterns (CHOPs) to safely navigate congested areas. This OMP\(+\)CHOP algorithm is shown to be safe and complete, and simulation results show scalability to hundreds of robots.

In this work, we solve the labeled multi-robot planning problem. Most proposed algorithms to date have modeled robots as kinematic or kinodynamic agents in planar environments, making them impractical for real-world systems. Here, we present experiments to validate a centralized multi-robot planning and trajectory generation method that explicitly accounts for robots with higher-order dynamics. First, we demonstrate successful execution of solution trajectories. Next, we verify the robustness of the robots’ trajectory tracking to unmodeled external disturbances, in particular, the aerodynamic interactions between co-planar neighbors. Finally, we apply our algorithm to navigating quadrotors away from the downwash of their neighbors to improve safety in three-dimensional workspaces.

We address the problem of high speed autonomous navigation of quadrotor micro aerial vehicles with limited onboard sensing and computation. In particular, we propose a dual range planning horizon method to safely and quickly navigate quadrotors to specified goal locations in previously unknown and unstructured environments. In each planning epoch, a short-range planner uses a local map to generate a new trajectory. At the same time, a safe stopping policy is found. This allows the robot to come to an emergency halt when necessary. Our algorithm guarantees collision avoidance and demonstrates important advances in real-time planning. First, our novel short range planning method allows us to generate and re-plan trajectories that are dynamically feasible, comply with state and input constraints, and avoid obstacles in real-time. Further, previous planning algorithms abstract away the obstacle detection problem by assuming the instantaneous availability of geometric information about the environment. In contrast, our method addresses the challenge of using the raw sensor data to form a map and navigate in real-time. Finally, in addition to simulation examples, we provide physical experiments that demonstrate the entire algorithmic pipeline from obstacle detection to trajectory execution.

In this paper, we present a trajectory planning method to navigate a quadrotor with a cable-suspended payload through known obstacle-filled environments. We model the system as a hybrid dynamical system and formulate the trajectory generation problem as a Mixed Integer Quadratic Program (MIQP). Specifically, we address two novel challenges. First, we plan for a multi-body system, and obstacle avoidance must be guaranteed for the quadrotor, load, and the cable. Second, our method accommodates transitions between subsystems of the hybrid dynamical system, allowing for maneuvers that would otherwise be infeasible if the cable were constrained to remain taut. Numerical and experimental results validate the proposed approach for the full hybrid system.

In this work, we present a decentralized controller for the tracking and following of mobile targets, specifically addressing considerations of: 1) not altering target behavior, 2) target states represented by multiple hypotheses, and 3) limited information from bearing-only sensors. The proposed controller drives a team of n robots to circumnavigate an arbitrary distribution of target points at a desired radius from the targets. The controller also dictates robot spacing around their circular trajectory by tracking a desired relative phase angle between neighbors. Simulation results show the functionality of the controller for arbitrary-sized teams and arbitrary stationary and moving particle distributions. Additionally, the controller was implemented on OceanServer Iver2 AUVs. Tracking results demonstrate the controller’s capability to track a desired radius as well as maintain phase with respect to a second AUV.

This paper presents a system of multiple coordinating autonomous underwater vehicles (AUV) that can localize and track a shark tagged with an acoustic transmitter. Each AUV is equipped with a stereo-hydrophone system that provides measurements of the relative bearing to the transmitter, as well as an acoustic modem that allows for inter-AUV communication and hence cooperative shark state estimation and decentralized tracking control. Online state estimation of the shark's state is performed using a Particle Filter in which measurements are shared between AUVs. The decentralized control system enables the AUVs to circumnavigate a dynamic target, (i.e. the estimated shark location). Each AUV circles the target by tracking circles of different radii and at different phase angles with respect to the target so as to obtain simultaneous sensor vantage points and minimize chance of AUV collision. A series of experiments using two AUVs were conducted in Big Fisherman's Cove in Santa Catalina Island, CA and demonstrated the ability to track a tagged leopard shark (Triakis semifasciata). The performance of the tracking was compared to standard manual tracking performed using an directional hydrophone operated by a researcher in a boat. In an additional experiment, the AUVs tracked an acoustic tag attached to the tracking boat to quantify the error of the state estimation of the system.

Caves on other planetary bodies offer sheltered habitat for future human explorers and numerous clues to a planet's past for scientists. While recent orbital imagery provides exciting new details about cave entrances on the Moon and Mars, the interiors of these caves are still unknown and not observable from orbit. Multi-robot teams offer unique solutions for exploration and modeling subsurface voids during precursor missions. Robot teams that are diverse in terms of size, mobility, sensing, and capability can provide great advantages, but this diversity, coupled with inherently distinct low-level behavior architectures, makes coordination a challenge. This paper presents a framework that consists of an autonomous frontier and capability-based task generator, a distributed market-based strategy for coordinating and allocating tasks to the different team members, and a communication paradigm for seamless interaction between the different robots in the system. Robots have different sensors, (in the representative robot team used for testing: 2D mapping sensors, 3D modeling sensors, or no exteroceptive sensors), and varying levels of mobility. Tasks are generated to explore, model, and take science samples. Based on an individual robot's capability and associated cost for executing a generated task, a robot is autonomously selected for task execution. The robots create coarse online maps and store collected data for high resolution offline modeling. The coordination approach has been field tested at a mock cave site with highly-unstructured natural terrain, as well as an outdoor patio area. Initial results are promising for applicability of the proposed multi-robot framework to exploration and modeling of planetary caves.