Robots operating in unpredictable environments require versatile, hardware-agnostic
frameworks capable of adapting to various tasks. While a recent screw-based
affordance approach shows promise, it faces challenges in avoiding undesirable
configurations, singularity navigation, and task success prediction. To address these
limitations, we propose the Closed-Chain Affordance (CCA) planner – a novel
framework that incorporates explicit gripper orientation control and generates
complete joint trajectories in real time for screw-based task affordance execution.
Our method models the affordance and manipulator as a closed-chain mechanism,
introducing an innovative approach to solving closed-chain inverse kinematics. It
encapsulates task constraints and simplifies task definitions, while remaining
hardware and robot agnostic, robust to errors, and invariant to the initial grasp. We
validate our framework with simulations on a UR5 robot and real-world
implementation on a Boston Dynamics Spot robot. Our experiments demonstrate
rapid joint trajectory generation (0.0077–0.098 s) for various tasks, including a 420°
valve turn with gripper orientation control. Comparison with state-of-the-art methods
shows a 4x improvement in planning time, reduced joint movement, and achievement
of greater task goals.

Demo Video:
https://www.youtube.com/watch?v=Ukv93hbNrOM

Open-source Code:
Standalone planner: https://github.com/UTNuclearRoboticsPublic/closed-chain-affordance
ROS2 interface: https://github.com/UTNuclearRoboticsPublic/closed-chain-affordance-ros

Publications:

• Panthi, Janak, Farshid Alambeigi, and Mitch Pryor. “A Closed-Chain Approach to Generating Affordance Joint Trajectories for Robotic Manipulators.” IEEE Transactions on Robotics (2025). Link

A Closed-Chain Approach to Generating Affordance Joint Trajectories for Robotic Manipulators 

Multi-Robot Collaborative Mapping w/ Remote Mobile Agents:

Author: Daniel I. Meza

Funded By: Los Alamos National Laboratory

This project contributes to a broader initiative to develop a comprehensive hardware and software solution for routine radiation surveying in nuclear facilities.

The Minibot system includes a central server computer that serves as the coordination and processing hub, and a team of compact, low-powered, cost-effective robot agents. Each agent features a differential drive platform and a small footprint, enabling navigation through confined and hazardous areas where other robots cannot safely operate, such as under furniture and along narrow corridors.

Coordinated by the server, the agents continuously offload environmental data from onboard 2D and 3D LiDAR sensors. The server then executes collaborative SLAM and 3D mapping algorithms to produce detailed maps of the environment, which other robots use for task planning and execution.

Thesis Publication: https://hdl.handle.net/2152/124970, https://doi.org/10.26153/tsw/51571

Youtube Video: https://www.youtube.com/watch?v=9eT9DMGf5lk

Project AVES is a multifunctional, autonomous device capable of monitoring environmental conditions and bird populations in a variety of ecosystems. The core functions of the device include recording and analyzing bird calls while simultaneously collecting local data such as soil moisture, and temperature, as well as air temperature, moisture, and humidity. The device will be placed in various environments for bird monitoring purposes. Through audio detection, the device will be able to identify what species of birds are in the area as well as localize the audio to determine the location of where the bird is calling from. This will allow for species monitoring, population monitoring, and migration tracking.

Project Website: https://nairarj000.github.io/paves.github.io/

AugRE (Augmented Robot Environment) is a scalable mixed reality-based human-robot teaming system that enables users to easily command, control, and supervise large multi-agent unmanned ground and aerial vehicles. The current system enables multiple users to collaborate with up to 50 robotic agents simultaneously in unknown indoor and outdoor environments. The system features real-time localization, an efficient by directional communication layer, and spatially aligned holographic representations that enable users to easily visualize and interact with real-time robot sensor data. Check out the AugRE project page to learn more: https://utnuclearroboticspublic.github.io/Augmented-Robot-Environment/

Publications:

AugRE: Augmented Robot Environment to Facilitate Human-Robot Teaming and Communication: https://ieeexplore.ieee.org/document/9900721

Augmented Reality User Interface for Command, Control, and Supervision of Large Multi-Agent Teams: https://arxiv.org/abs/2401.05665
 

Summary: Over 90% of SNF dry cask storage systems in the United States use welded dry storage canisters (DSCs). Typically, these canisters represent the confinement barrier in the dry storage system preventing any release of SNF or radioactive noble gases to the environment. As the dry storage terms of SNF are extended, DSC monitoring issues become more important for safe operation of the dry cask storage systems. The objective of this research program is to develop a technology to enable the next generation of “intelligent spent nuclear fuel (SNF) dry storage canisters (DSCs),” that is, canisters with integrated sensing and processing capabilities to enable real-time state awareness of the DSC.

Collaborator: Dr. Salvatore Salamone (UT Austin), Orano TN, Idaho National Lab

Source: DOE NEUP

Summary: The purpose of this thesis is to develop a robotic system that is fully capable of autonomous survey tasks with little to no human interaction. The system uses a Veloydne VLP-32 LIDAR sensor and an Intel RealSense D435 camera to simultaneously map the environment and avoid all obstacles autonomously. Combination of these sensor to detect positive, negative, and overhead obstacles results in 3D object detection, which is then used to create a 2D map using SLAM mapping algorithms.