Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems as underground settings present key challenges that can render robot autonomy hard to achieve. This has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, the CERBERUS system-of-systems is presented as a unified strategy towards subterranean exploration using legged and flying robots. As primary robots, ANYmal quadruped systems are deployed considering their endurance and potential to traverse challenging terrain. For aerial robots, both conventional and collision-tolerant multirotors are utilized to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, a complementary multi-modal sensor fusion approach utilizing camera, LiDAR, and inertial data for resilient robot pose estimation is proposed. Individual robot pose estimates are refined by a centralized multi-robot map optimization approach to improve the reported location accuracy of detected objects of interest in the DARPA-defined coordinate frame. Furthermore, a unified exploration path planning policy is presented to facilitate the autonomous operation of both legged and aerial robots in complex underground networks. Finally, to enable communication between the robots and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station, alongside breadcrumbing of wireless nodes by our legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge Tunnel and Urban Circuits, along with the current limitations and the lessons learned for the benefit of the community.
翻译:地下环境的自主探索是机器人系统的主要前沿,因为地下环境是使机器人难以实现自主的关键挑战。这促成了DARPA Subterrane Allenge 挑战,在此挑战中,机器人团队在各种地下环境中寻找感兴趣的物体。作为回应,CERBERUS 系统系统被介绍为利用脚踏和飞行机器人进行地下探索的统一战略。主要机器人正在部署Anymal 4rubed系统,考虑到其耐受力和对挑战性地形的反向潜力。对于空中机器人来说,常规和碰撞耐力多式机器人都被利用在地面系统上太窄或无法达到的轨道。预测退化的感测条件、利用相机、LIDAR和惯性数据进行辅助的多式感应感应方法。个体机器人的估算由中央多机器人地图优化法进行完善,以提高所测到的地形图在DARPA定义的协调框架中的位置准确度。此外,一个统一的探索路径规划政策被用于探索空间系统在地面轨道上太窄,或者以其他方式无法进入地面系统进行空间。一个自主操作的多式多式感应变的系统,在地面上,在地面上,在地面和地面上,在地面上与地面上与地面上进行自动智能数据库数据库数据库数据库中进行自动定位上进行自我定位上进行自我定位上进行自我定位上的自我定位的自动连接。