Ella M. Atkins

JAAMAS Journal 2026 Journal Article

Planning and Resource Allocation for Hard Real-time, Fault-Tolerant Plan Execution

• Ella M. Atkins
• Tarek F. Abdelzaher
• Edmund H. Durfee

Abstract We describe the interface between a real-time resource allocation system with an AI planner in order to create fault-tolerant plans that are guaranteed to execute in hard real-time. The planner specifies the task set and all execution deadlines required to ensure system safety, then the resource utilization. A new interface module combines information from planning and resource allocation to enforce development of plans feasible for execution during a variety of internal system faults. Plans that over-utilize any system resource trigger feedback to the planner, which then searches for an alternate plan. A valid plan for each specified fault, including the nominal no-fault situation, is stored in a plan cache for subsequent real-time execution. We situate this work in the context of CIRCA, the Cooperative Intelligent Real-time Control Architecture, which focuses on developing and scheduling plans that make hard real-time safety guarantees, and provide an example of an autonomous aircraft agent to illustrate how our planner-resource allocation interface improves CIRCA performance.

ICRA Conference 2025 Conference Paper

Gradient Guided Search for Aircraft Contingency Landing Planning

• H. Emre Tekaslan
• Ella M. Atkins

This paper presents a three-dimensional discrete search path planner for fixed-wing aircraft emergency landing planning that manages state-space complexity by incorporating cost gradients to assure descent flight path angle and runway heading alignment constraints are met. Our approach incorporates steady wind and maximizes margin from flight envelope boundaries to accommodate wind variation in a manner commensurate with a loss of thrust condition. A novel multi-objective cost function that combines gradient-based path guidance and population risk metrics is implemented to efficiently enable discrete search to find a robust solution. The proposed method is demonstrated through use cases with population data for a region of Long Island, New York that highlight our algorithm's effectiveness.

ICRA Conference 2023 Conference Paper

Simplifying Aerial Manipulation Using Intentional Collisions

• Mark Nail
• Nick Jänne
• Olivia Ma
• Gabriel Arellano
• Ella M. Atkins
• R. Brent Gillespie

Aerial manipulation describes a process that includes physical interaction between an unmanned aircraft system (UAS) and its environment. We aim to apply aerial manipulation to sample leaves and small branches from rain forest trees. Current approaches to aerial manipulation involve extended periods of UAS-environment interaction, during which forces and moments can lead to a loss in attitude or position control in underactuated multicopters. By adapting intelligent foot placement strategies found in dynamically stable hopping robots, this work proposes a strategy involving carefully managed intentional collisions between the UAS and its environment. We designed an attitude controller denoted a Velocity Matching controller that aligns a UAS-mounted pogo-stick foot with the center of mass velocity vector during collision approach to maximize UAS ability to recover a hover state after collision. We propose the use of a flight envelope involving altitude and horizontal speed states to assess recoverability prior to initiating each approach to collision. We identify this flight envelope from a simulation study built on a model of flight in Conventional Waypoint Following and Velocity Matching control modes as well as a model of collision response. Experimental flight testing evaluates the simulation-based envelope resulting in an actual envelope that is somewhat smaller but similarly shaped to the envelope identified in simulation.

ICRA Conference 2019 Conference Paper

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

• Yu Yao 0006
• Mingze Xu
• Chiho Choi
• David J. Crandall
• Ella M. Atkins
• Behzad Dariush

Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle. We present a multi-stream recurrent neural network (RNN) encoder-decoder model that separately captures both object location and scale and pixel-level observations for future vehicle localization. We show that incorporating dense optical flow improves prediction results significantly since it captures information about motion as well as appearance change. We also find that explicitly modeling future motion of the ego-vehicle improves the prediction accuracy, which could be especially beneficial in intelligent and automated vehicles that have motion planning capability. To evaluate the performance of our approach, we present a new dataset of first-person videos collected from a variety of scenarios at road intersections, which are particularly challenging moments for prediction because vehicle trajectories are diverse and dynamic. Code and dataset have been made available at: https://usa.honda-ri.com/hevi.

IROS Conference 2019 Conference Paper

Unsupervised Traffic Accident Detection in First-Person Videos

• Yu Yao 0006
• Mingze Xu
• Yuchen Wang
• David J. Crandall
• Ella M. Atkins

Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems. However, most work on video anomaly detection suffers from two crucial drawbacks. First, they assume cameras are fixed and videos have static backgrounds, which is reasonable for surveillance applications but not for vehicle-mounted cameras. Second, they pose the problem as one-class classification, relying on arduously hand-labeled training datasets that limit recognition to anomaly categories that have been explicitly trained. This paper proposes an unsupervised approach for traffic accident detection in first-person (dashboard-mounted camera) videos. Our major novelty is to detect anomalies by predicting the future locations of traffic participants and then monitoring the prediction accuracy and consistency metrics with three different strategies. We evaluate our approach using a new dataset of diverse traffic accidents, AnAn Accident Detection (A3D), as well as another publicly-available dataset. Experimental results show that our approach outperforms the state-of-the-art. Code and the dataset developed in this work are available at: https://github.com/MoonBlvd/tad-IROS2019

AAMAS Conference 2003 Conference Paper

Resource Allocation for a Limited Real-time Agent

• Haksun Li
• Ella M. Atkins
• Edmund H. Durfee
• Kang G. Shin

AAAI Conference 1997 Short Paper

Avoiding Failure via Pre-Planned Responses and Time-Bounded Planning

• Ella M. Atkins

AAAI Conference 1997 Conference Paper

Detecting and Reacting to Unplanned-for World States

• Ella M. Atkins

IJCAI Conference 1997 Conference Paper

Development of Iterative Real-time Scheduler to Planner Feedback

• Charles B. McVey
• Ella M. Atkins
• Edmund H. Durfee
• Kang G. Shin

Planning for real-time applications involves decisions not only about what actions to take in what states to progress toward achieving goals (the traditional decision problem faced by AI planning systems), but also about how to realize those actions within hard real-time deadlines given the inherent limitations of an execution platform. Determining how to arrange actions in a sequence such that timely execution is guaranteed within constraints is a manifestation of the scheduling problem. All cases of the scheduling problem in any domain of nontrivial complexity are difficult to solve (NP-Hard). To more efficiently solve the real-time plan scheduling problem, we propose and analyze an iterative feedback/constraint relaxation method in which a scheduler and planner iteratively interact to efficiently develop a well-utilized schedule which includes as many planned actions as possible. This method has been successfully implemented within the Cooperative Intelligent Real-time Control Architecture (CIRCA).

AAAI Conference 1996 Short Paper

Expecting the Unexpected: Detecting and Reacting to Unplanned-for World States

• Ella M. Atkins

Developing autonomous systems is challenging because complete and correct models do not exist for complex domains such as aircraft flight. Realistic systems bound the state set expanded during planning and compensate for unexpected situations with reactive mechanisms. This abstract describes a method by which a system can determine if it is unprepared for the current world state and a means to successfully respond to such an unhandled state.

UAI Conference 1996 Conference Paper

Plan Development using Local Probabilistic Models

• Ella M. Atkins
• Edmund H. Durfee
• Kang G. Shin

Approximate models of world state transitions are necessary when building plans for complex systems operating in dynamic environments. External event probabilities can depend on state feature values as well as time spent in that particular state. We assign temporally -dependent probability functions to state transitions. These functions are used to locally compute state probabilities, which are then used to select highly probable goal paths and eliminate improbable states. This probabilistic model has been implemented in the Cooperative Intelligent Real-time Control Architecture (CIRCA), which combines an AI planner with a separate real-time system such that plans are developed, scheduled, and executed with real-time guarantees. We present flight simulation tests that demonstrate how our probabilistic model may improve CIRCA performance.