Energy Efficient Navigation for Running Legged Robots

Harper, Mario; Nicholson, John; Collins, Emmanuel G.; Pusey, Jason; Clark, Jonathan E.

doi:10.1109/icra.2019.8793599

Cited by 20 publications

(5 citation statements)

References 27 publications

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“…Dynamic planning using a reduced robot model has recently shown promising results (Norby and Johnson, 2020) but has not been evaluated in deployment scenarios. Other work on navigation planning specifically for legged robots either only considers cases of obstacle avoidance on flat terrain (Zhao et al, 2018;Harper et al, 2019) or does additional contact planning, which pushes computational complexity past the real-time mark (Belter et al, 2019;Lin and Berenson, 2017;Reid et al, 2020). Approaches which learn traversability (Chavez-Garcia et al, 2017) or motion cost (Guzzi et al, 2020;Yang et al, 2021a) are powerful, but are either too slow due to the sequential querying of neural networks during sampling-based planning (Guzzi et al, 2020) or struggle in tight spaces where precise motion checking is necessary (Yang et al, 2021a).…”

Section: Related Workmentioning

confidence: 99%

ArtPlanner: Robust Legged Robot Navigation in the Field

Wellhausen¹,

Hutter²

2023

Preprint

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Due to the highly complex environment present during the DARPA Subterranean Challenge, all six funded teams relied on legged robots as part of their robotic team. Their unique locomotion skills of being able to step over obstacles require special considerations for navigation planning. In this work, we present and examine ArtPlanner, the navigation planner used by team CERBERUS during the Finals. It is based on a sampling-based method that determines valid poses with a reachability abstraction and uses learned foothold scores to restrict areas considered safe for stepping. The resulting planning graph is assigned learned motion costs by a neural network trained in simulation to minimize traversal time and limit the risk of failure. Our method 1 achieves real-time performance with a bounded computation time. We present extensive experimental results gathered during the Finals event of the DARPA Subterranean Challenge, where this method contributed to team CERBERUS winning the competition. It powered navigation of four ANYmal quadrupeds for 90 minutes of autonomous operation without a single planning or locomotion failure.

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Section: Related Workmentioning

confidence: 99%

ArtPlanner: Robust Legged Robot Navigation in the Field

Wellhausen¹,

Hutter²

2023

Preprint

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“…One of the reasons for this growing interest in MR teams is the limitation in capabilities of available single robots. Since no single robot can perform all conceivable tasks due to design [56,57], size [58] and power consumption limitations [59,60], it may be useful to combine the functionalities of several robots to accomplish a goal. The main reason for this is the need to accomplish tasks within a given timeframe where a diverse robotic fleet completing a collection of inspection tasks is more efficient and effective.…”

Section: Related Work: Mr Fleetsmentioning

confidence: 99%

Lessons learned: Symbiotic autonomous robot ecosystem for nuclear environments

Mitchell,

Emor Baniqued,

Zahid

et al. 2023

IET Cyber-Syst and Robotics

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Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out (POCO) around nuclear facilities each year, resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases. The UK Nuclear Decommissioning Authority supports the view that human‐in‐the‐loop (HITL) robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites. The authors present a novel implementation of a Cyber‐Physical System (CPS) deployed in an analogue nuclear environment, comprised of a multi‐robot (MR) team coordinated by a HITL operator through a digital twin interface. The development of the CPS created efficient partnerships across systems including robots, digital systems and human. This was presented as a multi‐staged mission within an inspection scenario for the heterogeneous Symbiotic Multi‐Robot Fleet (SMuRF). Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together, where a single robot would face challenges in full characterisation of radiation. Key contributions include the demonstration of symbiotic autonomy and query‐based learning of an autonomous mission supporting scalable autonomy and autonomy as a service. The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.

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“…Dynamic planning using a reduced robot model has recently shown promising results (Norby and Johnson, 2020), but it has not been evaluated in deployment scenarios. Other work on navigation planning specifically for legged robots either only considers cases of obstacle avoidance on flat terrain (Zhao et al, 2018;Harper et al, 2019), or it does additional contact planning, which pushes computational complexity past the real-time mark (Belter et al, 2019;Lin and Berenson, 2017;Reid et al, 2020). Approaches that learn traversability (Chavez-Garcia et al, 2017) or motion cost (Guzzi et al, 2020;Yang et al, 2021a) are powerful, but they are either too slow due to the sequential querying of neural networks during sampling-based planning (Guzzi et al, 2020), or they struggle in tight spaces where precise motion checking is necessary (Yang et al, 2021a).…”

Section: Related Workmentioning

confidence: 99%

ArtPlanner: Robust Legged Robot Navigation in the Field

Wellhausen¹,

Hutter²

2023

View full text Add to dashboard Cite

Due to the highly complex environment present during the DARPA Subterranean Challenge, all six funded teams relied on legged robots as part of their robotic team. Their unique locomotion skills of being able to step over obstacles require special consideration for navigation planning. In this work, we present and examine ArtPlanner, the navigation planner used by team CERBERUS during the Finals. It is based on a sampling-based method that determines valid poses with a reachability abstraction and uses learned foothold scores to restrict areas considered safe for stepping. The resulting planning graph is assigned learned motion costs by a neural network trained in simulation to minimize traversal time and limit the risk of failure. Our method1 achieves real-time performance with a bounded computation time. We present extensive experimental results gathered during the Finals event of the DARPA Subterranean Challenge, where this method contributed to team CERBERUS winning the competition. It powered navigation of four ANYmal quadrupeds for 90 minutes of autonomous operation without a single planning or locomotion failure.

show abstract

Energy Efficient Navigation for Running Legged Robots

Cited by 20 publications

References 27 publications

ArtPlanner: Robust Legged Robot Navigation in the Field

ArtPlanner: Robust Legged Robot Navigation in the Field

Lessons learned: Symbiotic autonomous robot ecosystem for nuclear environments

ArtPlanner: Robust Legged Robot Navigation in the Field

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