Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion (2010.09259v3)

Abstract: This paper serves as one of the first efforts to enable large-scale and long-duration autonomy using the Boston Dynamics Spot robot. Motivated by exploring extreme environments, particularly those involved in the DARPA Subterranean Challenge, this paper pushes the boundaries of the state-of-practice in enabling legged robotic systems to accomplish real-world complex missions in relevant scenarios. In particular, we discuss the behaviors and capabilities which emerge from the integration of the autonomy architecture NeBula (Networked Belief-aware Perceptual Autonomy) with next-generation mobility systems. We will discuss the hardware and software challenges, and solutions in mobility, perception, autonomy, and very briefly, wireless networking, as well as lessons learned and future directions. We demonstrate the performance of the proposed solutions on physical systems in real-world scenarios.

• The paper introduces NeBula, an advanced autonomy architecture that integrates probabilistic planning for exploring and mapping extreme, unstructured environments.
• It details a robust multi-sensor odometry and traversability mapping system that enables accurate localization and safe navigation in GPS-denied areas.
• Field experiments show Spot covering over 4 km and handling complex terrain, underscoring its potential for search and rescue, planetary exploration, and urban inspection.

Overview of "Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion"

The paper "Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion" presents a comprehensive paper on large-scale, long-duration autonomy using the Boston Dynamics Spot robot. The focus of this research is on equipping legged robotic systems to autonomously navigate real-world, complex missions in challenging environments, specifically targeting the scenarios encountered in the DARPA Subterranean Challenge.

NeBula Autonomy Architecture

Central to this paper is the integration of the autonomy architecture known as NeBula, which stands for Networked Belief-aware Perceptual Autonomy. NeBula is designed to enable reliable, coordinated multi-robot exploration of unknown and hard-to-access terrains. It does so by employing probabilistic methods to handle uncertainty in unknown environments. This is vital for missions on planetary terrains, where autonomous robots need to assess risks and make informed decisions. The architecture includes components like a multi-sensor odometry system, a belief manager for environment modeling, robust planning modules, and a communication framework.

Key Components and Algorithmic Features

The paper highlights several critical advances in enabling Spot with high-level autonomy:

1. Odometry and Localization: The proposed framework offers a robust solution for odometry estimation in perceptually-degraded environments, integrating multiple sources of data, including LiDAR and visual odometry, which are crucial for maintaining accurate localization in environments typically devoid of GPS.
2. Traversability Planning: The authors present a multi-layer traversability mapping technique, capturing various elemental risks and incorporating uncertainty into planning. This enables the legged robot to safely navigate complex terrains by minimizing risk through perception-aware path planning.
3. Coverage and Search Behavior: Through a strategic use of graph policies and reward-based mechanisms, the research successfully demonstrates large-area coverage required for mapping tasks under stringent constraints, such as those posed by the SubT Challenge.

Empirical Validation and Performance

The practical application and rigorous field-testing of the proposed methodologies were conducted in challenging environments. The experiments showcased the ability of the autonomous Spot to explore and map multilevel, subterranean environments. The integration of NeBula allowed the robot to navigate over 4 kilometers in industrial environments while effectively managing to traverse challenging features such as staircases and uneven terrain. The exploration and artifact localization capabilities significantly contributed to team CoSTAR's success in the SubT Challenge.

Implications and Future Directions

The developments presented in this work have substantial implications for autonomous robotics, particularly in search and rescue operations, planetary exploration, and urban inspection environments. By advancing legged locomotion capabilities paired with sophisticated autonomy architectures, there is potential to further expand the operational scope and reliability of autonomous robots in extreme settings. Future research might focus on further enhancing the adaptability of these systems to new forms of challenging terrain, improving multi-robot coordination and communication, and extending the autonomy duration for longer missions on extraterrestrial landscapes.

In conclusion, this paper provides valuable insights and empirical evidence on the efficacy of integrating advanced autonomy systems with legged robots, setting a strong precedent for future exploration missions in isolated, unknown, and inaccessible environments.