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Evelyn Madewell

dblp:344/3056 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0007-2369-055XORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Legged, aerial and field robots · 40% Motion planning and robot control · 40% Planning, search and constraint satisfaction · 20%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
aerial robots
0.712023
HALO: Hazard-Aware Landing Optimization for Autonomous Systems · ICRA 2023
Robotics › Legged, aerial and field robots › aerial robots
autonomous landing
0.712023
HALO: Hazard-Aware Landing Optimization for Autonomous Systems · ICRA 2023
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning under uncertainty
contingency planning
0.712023
HALO: Hazard-Aware Landing Optimization for Autonomous Systems · ICRA 2023
Robotics › Motion planning and robot control
motion planning
0.712023
HALO: Hazard-Aware Landing Optimization for Autonomous Systems · ICRA 2023
Robotics › Motion planning and robot control
trajectory optimization
0.712023
HALO: Hazard-Aware Landing Optimization for Autonomous Systems · ICRA 2023

Methods — techniques the papers use, named apart from their topics

point cloud processing · 0.7adaptive deferred-decision trajectory optimization · 0.7
YearPublicationVenuePosition
2023 HALO: Hazard-Aware Landing Optimization for Autonomous Systems
abstract
With autonomous aerial vehicles enacting safety-critical missions, such as the Mars Science Laboratory Curiosity rover's landing on Mars, the tasks of automatically identifying and reasoning about potentially hazardous landing sites is paramount. This paper presents a coupled perception-planning solution which addresses the hazard detection, optimal landing trajectory generation, and contingency planning challenges encountered when landing in uncertain environments. Specifically, we develop and combine two novel algorithms, Hazard-Aware Landing Site Selection (HALSS) and Adaptive Deferred-Decision Trajectory Optimization (Adaptive-DDTO), to address the perception and planning challenges, respectively. The HALSS framework processes point cloud information to identify feasible safe landing zones, while Adaptive-DDTO is a multi-target contingency planner that adaptively replans as new perception information is received. We demonstrate the efficacy of our approach using a simulated Martian environment and show that our coupled perception-planning method achieves greater landing success whilst being more fuel efficient compared to a non-adaptive DDTO approach.
Christopher R. Hayner, Samuel C. Buckner, Daniel Broyles, Evelyn Madewell, Karen Leung, Behçet Açikmese
ICRA4