Autonomous robotics is driving Perseverance rover’s progress on Mars

Verma, Vandi; Maimone, Mark; Gaines, Daniel M.; Francis, Raymond; Estlin, Tara; Kühn, Stephen; Rabideau, Gregg; Chien, Steve; McHenry, Michael; Graser, Evan; Rankin, Arturo L.; Thiel, Ellen

doi:10.1126/scirobotics.adi3099

Cited by 23 publications

(1 citation statement)

References 33 publications

(42 reference statements)

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“…Robotic explorers already serve many purposes varying from the highly specific, such as locating search and rescue targets [194]- [196], to the highly general, such as surveying the deep ocean [197]- [199] or even other worlds [200]. At both extremes, greater autonomy has significantly increased robotic explorers' operational efficiency.…”

Section: Related Workmentioning

confidence: 99%

Enabling human-multi-robot collaborative visual exploration in underwater environments

Jamieson

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This thesis presents novel approaches to vision-based autonomous exploration in underwater environments using human-multi-robot systems, enabling robots to adapt to evolving mission priorities learned via a human supervisor’s responses to images collected in situ. The robots model the spatial distribution of various habitats and terrain types in the environment using semantic classes learned online, and send image queries to the supervisor to learn which of these classes are associated with the highest concentration of targets of interest. The robots do not require prior examples of these targets, and learn these concentration parameters online. This approach is suitable for exploration in unfamiliar environments where unexpected phenomena are frequently discovered, such as coral reefs. A novel risk-based online learning algorithm identifies the concentration parameters using the fewest possible number of queries, enabling the robots to adapt quickly and reducing the operational burden on the supervisor. I introduce four primary contributions to address prevalent challenges in underwater exploration. Firstly, a multi-robot semantic representation matching algorithm enables interrobot sharing of semantic maps, generating consistent global maps with 20-60% higher quality scores than those produced by other methods. Next, we present DeepSeeColor, a novel real-time algorithm for correcting underwater image color distortions, which achieves up to 60 Hz processing speeds, thereby enabling improved semantic mapping and target recognition accuracy online. Thirdly, an efficient risk-based online learning algorithm ensures effective communication between robots and human supervisors, and, while remaining computationally tractable, overcomes the myopia which would cause previous algorithms to underestimate a query’s value. Lastly, we propose a new reward model and planning algorithm tailored for autonomous exploration, together enabling a 25-75% increase in the number of targets of interest located when compared to baseline surveys. These experiments were conducted with simulated robots exploring real coral reef maps and with real, ecologically meaningful targets of interest. Collectively, these contributions overcome key barriers to vision-based autonomous underwater exploration, and enhance the capability of autonomous underwater vehicles to adapt to new and evolving mission objectives in situ. Beyond marine exploration, these contributions have value in broader applications, such as space exploration, ecosystem monitoring, and other online learning problems.

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Section: Related Workmentioning

confidence: 99%

Enabling human-multi-robot collaborative visual exploration in underwater environments

Jamieson

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Human feedback enhanced autonomous intelligent systems: a perspective from intelligent driving

Yuan,

Huang,

Guo

et al. 2024

Auton. Intell. Syst.

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Artificial intelligence empowers the rapid development of autonomous intelligent systems (AISs), but it still struggles to cope with open, complex, dynamic, and uncertain environments, limiting its large-scale industrial application. Reliable human feedback provides a mechanism for aligning machine behavior with human values and holds promise as a new paradigm for the evolution and enhancement of machine intelligence. This paper analyzes the engineering insights from ChatGPT and elaborates on the evolution from traditional feedback to human feedback. Then, a unified framework for self-evolving intelligent driving (ID) based on human feedback is proposed. Finally, an application in the congested ramp scenario illustrates the effectiveness of the proposed framework.

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A lunar reconnaissance drone for cooperative exploration and high-resolution mapping of extreme locations

Tonasso,

Tataru,

Rauch

et al. 2024

Acta Astronautica

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Autonomous robotics is driving Perseverance rover’s progress on Mars

Cited by 23 publications

References 33 publications

Enabling human-multi-robot collaborative visual exploration in underwater environments

Enabling human-multi-robot collaborative visual exploration in underwater environments

Human feedback enhanced autonomous intelligent systems: a perspective from intelligent driving

A lunar reconnaissance drone for cooperative exploration and high-resolution mapping of extreme locations

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