analiza aiqbench
This research introduces a mapless control algorithm for articulated tracked robots that eliminates reliance on odometry integration, thereby avoiding drift errors caused by occluded terrain beneath the chassis. By utilizing only IMU data, joint angles, and non-accumulated motion cues, the proposed Stabilizing Anti-Stuck (SAS) method successfully navigates obstacles invisible to standard LiDAR sensors where traditional map-based controllers fail. The study demonstrates that this approach achieves traversal quality comparable to state-of-the-art systems while significantly improving robustness in unstructured environments.
Mapless, Stabilizing, and Anti-Stuck Flipper Control for Articulated Tracked Robots
arXiv:2610.13838v1 Announce Type: new Abstract: Nearly every autonomous or semi-autonomous flipper controller assumes access to an elevation map of the terrain under and in front of the robot. The terrain beneath the chassis is occluded in the sensors, so the map is remembered through odometry integration, whose drift corrupts it. We present Stabilizing Anti-Stuck (SAS), a mapless controller that combines a stabilization term for body roll and pitch with an anti-stuck term that frees the robot when it gets stuck. As input, SAS uses the IMU, the flipper joint angles, and a motion cue that only tells whether the robot is moving. The cue is computed over a short window and never accumulated, so SAS does not inherit the odometry-integration drift that corrupts the map. We deploy SAS on a tracked robot with four flippers and evaluate it against manual teleoperation and prior flipper controllers in a large comparison, both in simulation and on a real obstacle. In simulation and on the real robot, SAS achieves traversal quality comparable to state-of-the-art map-based controllers. On a log hidden in tall grass, invisible to the LiDAR, the best map-based controller gets stuck while SAS traverses the hidden obstacle without a problem.
Abstract:Nearly every autonomous or semi-autonomous flipper controller assumes access to an elevation map of the terrain under and in front of the robot. The terrain beneath the chassis is occluded in the sensors, so the map is remembered through odometry integration, whose drift corrupts it. We present Stabilizing Anti-Stuck (SAS), a mapless controller that combines a stabilization term for body roll and pitch with an anti-stuck term that frees the robot when it gets stuck. As input, SAS uses the IMU, the flipper joint angles, and a motion cue that only tells whether the robot is moving. The cue is computed over a short window and never accumulated, so SAS does not inherit the odometry-integration drift that corrupts the map. We deploy SAS on a tracked robot with four flippers and evaluate it against manual teleoperation and prior flipper controllers in a large comparison, both in simulation and on a real obstacle. In simulation and on the real robot, SAS achieves traversal quality comparable to state-of-the-art map-based controllers. On a log hidden in tall grass, invisible to the LiDAR, the best map-based controller gets stuck while SAS traverses the hidden obstacle without a problem.
Robotics (cs.RO)
arXiv:2610.13838 [cs.RO]
(or arXiv:2610.13838v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.13838 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 287 cuvinte
Subiecte conexe

Game-Theoretic Disempowerment: Loss of Control to Agencyless AI
LessWrong · 27 sept. 2026
Problems of Proliferation
LessWrong · 29 sept. 2026

Informe de riesgos de la IA de frontera (febrero–marzo de 2026)
METR · 19 mai 2026

Frontier AI Risk Report (February–March 2026)
METR · 19 mai 2026