Threading the Needle: T est and Evaluation of Early Stage UAS Capabilities to Autonomously Navigate GPS-Denied Environments in the DARPA Fast Lightweight Autonomy (FLA) Program Adam Norton 1 and Holly A. Y anco 1 Abstract -- The DARPA Fast Lightweight Autonomy (FLA) program (2015-2018) served as a significant milestone in the development of UAS, particularly for autonomous navigation through unknown GPS-denied environments. Three performing teams developed UAS using a common hardware platform, focusing their contributions on autonomy algorithms and sensing. Several experiments were conducted that spanned indoor and outdoor environments, increasing in complexity over time. This paper reviews the testing methodology developed in order to benchmark and compare the performance of each team, each of the FLA Phase 1 experiments that were conducted, and a summary of the Phase 1 results. I NTRODUCTION The past 25 years of research and development in aerial robotics has seen tremendous growth in the adoption of systems as well as the advancement of capabilities including increased speed, more reliable autonomy, and powerful onboard computing.

This study introduces a shared-control approach for collision avoidance in a self-balancing riding ballbot, called PURE, marked by its dynamic stability, omnidirectional movement, and hands-free interface. Integrated with a sensor array and a novel Passive Artificial Potential Field (PAPF) method, PURE provides intuitive navigation with deceleration assistance and haptic/audio feedback, effectively mitigating collision risks. This approach addresses the limitations of traditional APF methods, such as control oscillations and unnecessary speed reduction in challenging scenarios. A human-robot interaction experiment, with 20 manual wheelchair users and able-bodied individuals, was conducted to evaluate the performance of indoor navigation and obstacle avoidance with the proposed shared-control algorithm. Results indicated that shared-control significantly reduced collisions and cognitive load without affecting travel speed, offering intuitive and safe operation. These findings highlight the shared-control system's suitability for enhancing collision avoidance in self-balancing mobility devices, a relatively unexplored area in assistive mobility research.

Now that Toyota has unveiled its latest self-driving car prototype, it needs a good test course to put the vehicle through its paces... and thankfully, there's already one lined up. Toyota has struck a deal to test its autonomous vehicle tech at GoMentum Station, the California test course known for its tough, realistic conditions. The automaker's cars will be subjected to "extreme driving events" that wouldn't be safe to test on public roads, and will experience a wide variety of conditions that include bridges and tunnels. There's still a long way to go before Toyota's technology is ready for the street. With that said, an expansion like this is an important step.