Limited energy availability is among the most challenging considerations developers face for heterogeneous systems and is critical for battery-powered devices. For complex systems composed of mechanical and computational units, such as drones and mobile robots, more than half of the power consumption can be due to the computational operations. Critically, these systems are often composed of many components, interacting concurrently to achieve specific functionality. As a result, power prediction and estimation can be a challenging task, especially if different computational units, such as CPU and GPU, should be modeled. In this paper, we focus on limited energy availability for mobile heterogeneous devices powered by a battery and present a coarse-grained computation-oriented energy modeling approach. Our approach predicts the energy consumption of a set of software components, in a specific configuration, executed according to a given scheduling policy. The model, determined numerically from several empirical power samples, describes the energy consumed by a software configuration and can be used for energy-aware planning and optimization from a computational point of view. It can potentially support a complex embedded system in maximizing the level of autonomy while minimizing power consumption and preserving the most appropriate amount of battery charge by finding the right rate of quality of service. Our approach is supported and validated by the design and implementation of a profiling tool. The tool abstracts computational energy behavior and describes the current battery drain as a function of all the admissible configurations.

Computational energy-efficiency is a critical aspect of many modern embedded devices as it impacts the level of autonomy for numerous scenarios. We present a component-based energy modeling approach to abstract per-component energy in a dataflow computational network executed according to a given scheduling policy. The approach is based on a modeling tool and ultimately relies on battery state to support a wider range of energy-optimization strategies for power-critical devices. CCS Concepts• Hardware → Power estimation and optimization; • Computing methodologies → Modeling and simulation; • Computer systems organization → Embedded systems; Multicore architectures.

In this paper, we present the design and evaluation of a vision-based algorithm for autonomous tracking and landing on a moving platform in varying environmental conditions. We use an energy-aware approach, where the design of the algorithm is based on an evaluation of the energy consumption and Quality of Service (QoS) of each computational component. We evaluate our approach with an agricultural use case where a moving platform is tracked using a landing marker and the YOLOv3tiny CNN is used to detect ground-based hazards. We perform all computations onboard using an NVIDIA Jetson Nano and analyse the impact on the flight time by profiling the energy consumption of the marker detection and the CNN. Experiments are conducted in Gazebo simulation using an energy modeling tool to measure the computational energy cost as a function of QoS. We test the energy efficiency and robustness of our system in various dynamic wind disturbances. We show that the marker detection algorithm can be run at the highest QoS with only a marginal energy overhead whereas adapting the QoS level of CNN results in a considerable power saving. The power saving is significant for a system executing on a fixed-wing UAV.

In this paper, we present a case study on the energy estimation of drones and derive a general modeling approach that estimates computational and mechanical energy separately. The mechanical energy model can easily be extended to other drones and is built using a Fourier series from a number of training flights. The computational energy model is more advanced as it handles heterogeneous hardware and incorporates a specification that defines the quality-of-service ranges for software components of the robotic system. The computational model is suitable for any mobile robot and is implemented in a modeling tool. The tool automatically generates an energy model from the specification by performing a set of empirical trials for selected configurations while approximating others. Information about the battery State of Charge is also included in the tool, hence allowing the evaluation of how different software configurations impact the battery. This approach can be used for dynamic mission assessment regarding different planning decisions. We here have demonstrated its ability to model the energy of a specific mission performed at varying levels of quality-of-service using a specific drone.

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In this paper, we present an online planningscheduling approach for battery-powered autonomous aerial robots. The approach consists of simultaneously planning a coverage path and scheduling onboard computational tasks. We further derive a novel variable coverage motion robust to airborne constraints and an empirically motivated energy model. The model includes the energy contribution of the schedule based on an automatic computational energy modeling tool. Our experiments show how an initial flight plan is adjusted online as a function of the available battery, accounting for uncertainty. Our approach remedies possible in-flight failure in case of unexpected battery drops, e.g., due to adverse atmospheric conditions, and increases the overall fault tolerance.

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