Christopher Tatsch

Camndon Reed, Christopher Tatsch, Jason N. Gross et al.

Natural environments pose significant challenges for autonomous robot navigation, particularly due to their unstructured and ever-changing nature. Hiking trails, with their dynamic conditions influenced by weather, vegetation, and human traffic, represent one such challenge. This work introduces a novel approach to autonomous hiking trail navigation that balances trail adherence with the flexibility to adapt to off-trail routes when necessary. The solution is a Traversability Analysis module that integrates semantic data from camera images with geometric information from LiDAR to create a comprehensive understanding of the surrounding terrain. A planner uses this traversability map to navigate safely, adhering to trails while allowing off-trail movement when necessary to avoid on-trail hazards or for safe off-trail shortcuts. The method is evaluated through simulation to determine the balance between semantic and geometric information in traversability estimation. These simulations tested various weights to assess their impact on navigation performance across different trail scenarios. Weights were then validated through field tests at the West Virginia University Core Arboretum, demonstrating the method's effectiveness in a real-world environment.

Cagri Kilic, Bernardo Martinez R., Christopher A. Tatsch et al.

Plans for establishing a long-term human presence on the Moon will require substantial increases in robot autonomy and multi-robot coordination to support establishing a lunar outpost. To achieve these objectives, algorithm design choices for the software developments need to be tested and validated for expected scenarios such as autonomous in-situ resource utilization (ISRU), localization in challenging environments, and multi-robot coordination. However, real-world experiments are extremely challenging and limited for extraterrestrial environment. Also, realistic simulation demonstrations in these environments are still rare and demanded for initial algorithm testing capabilities. To help some of these needs, the NASA Centennial Challenges program established the Space Robotics Challenge Phase 2 (SRC2) which consist of virtual robotic systems in a realistic lunar simulation environment, where a group of mobile robots were tasked with reporting volatile locations within a global map, excavating and transporting these resources, and detecting and localizing a target of interest. The main goal of this article is to share our team's experiences on the design trade-offs to perform autonomous robotic operations in a virtual lunar environment and to share strategies to complete the mission requirements posed by NASA SRC2 competition during the qualification round. Of the 114 teams that registered for participation in the NASA SRC2, team Mountaineers finished as one of only six teams to receive the top qualification round prize.

Cagri Kilic, Christopher A. Tatsch, Bernardo Martinez R. et al.

Team Mountaineers launched efforts on the NASA Space Robotics Challenge Phase-2 (SRC2). The challenge will be held on the lunar terrain with virtual robotic platforms to establish an in-situ resource utilization process. In this report, we provide an overview of a simulation environment, a virtual mobile robot, and a software architecture that was created by Team Mountaineers in order to prepare for the competition's qualification round before the competition environment was released.

Jared Strader, Jennifer Nguyen, Christopher Tatsch et al.

Robotic pollinators not only can aid farmers by providing more cost effective and stable methods for pollinating plants but also benefit crop production in environments not suitable for bees such as greenhouses, growth chambers, and in outer space. Robotic pollination requires a high degree of precision and autonomy but few systems have addressed both of these aspects in practice. In this paper, a fully autonomous robot is presented, capable of precise pollination of individual small flowers. Experimental results show that the proposed system is able to achieve a 93.1% detection accuracy and a 76.9% 'pollination' success rate tested with high-fidelity artificial flowers.