Efficient Sampling-Based Planning for Subterranean Exploration

Ahmad, Shakeeb; Humbert, J. Sean

doi:10.1109/iros47612.2022.9982169

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A sampling-based path planner ( Ahmad and Humbert, 2022 ) utilizes the mapping method described thus far to quickly and safely explore large, unstructured environments amidst rough terrain and stairs. The planner maintains a graph of nodes over the environment and searches the graph via A* for paths that contribute towards the exploration mission.…”

Section: Methodsmentioning

confidence: 99%

Terrain-aware semantic mapping for cooperative subterranean exploration

Miles,

Biggie,

Heckman

2023

Front. Robot. AI

View full text Add to dashboard Cite

Navigation over torturous terrain such as those in natural subterranean environments presents a significant challenge to field robots. The diversity of hazards, from large boulders to muddy or even partially submerged Earth, eludes complete definition. The challenge is amplified if the presence and nature of these hazards must be shared among multiple agents that are operating in the same space. Furthermore, highly efficient mapping and robust navigation solutions are absolutely critical to operations such as semi-autonomous search and rescue. We propose an efficient and modular framework for semantic grid mapping of subterranean environments. Our approach encodes occupancy and traversability information, as well as the presence of stairways, into a grid map that is distributed amongst a robot fleet despite bandwidth constraints. We demonstrate that the mapping method enables safe and enduring exploration of subterranean environments. The performance of the system is showcased in high-fidelity simulations, physical experiments, and Team MARBLE’s entry in the DARPA Subterranean Challenge which received third place.

show abstract

Section: Methodsmentioning

confidence: 99%

Terrain-aware semantic mapping for cooperative subterranean exploration

Miles,

Biggie,

Heckman

2023

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…During testing and final event runs, we found our approach to be scalable for environments of various sizes without a need for tuning gains for different environment types. The details of the sampling-based path planner can be found in (Ahmad and Humbert, 2022). In this work, a simulation comparison of the proposed planner with an existing sampling-based planner (Dang et al, 2020) is presented, highlighting the improvement in scalability and computational efficiency.…”

Section: Solution Search and Multi-robot Coordinationmentioning

confidence: 99%

Flexible Supervised Autonomy for Exploration in Subterranean Environments

Biggie¹,

R.²,

Riley³

et al. 2023

View full text Add to dashboard Cite

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in realworld underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission, and make high-impact strategic decisions.We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and reconfigurable communications.

show abstract