@ARTICLE{milojevic2024,
	abbrev_source_title = {IEEE Trans. Robot.},
	copyright = {In Copyright - Non-Commercial Use Permitted},
	year = {2024},
	type = {Journal Article},
	journal = {IEEE Transactions on Robotics},
	author = {Milojevic, Dejan and Zardini, Gioele and Elser, Miriam and Censi, Andrea and Frazzoli, Emilio},
	size = {20 p.},
	abstract = {This paper discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage of resources such as costs, energy, computational requirements, and weight. We emphasize the interplay between perception and motion planning in decision-making, leveraging False Negative Rate (FNR) and False Positive Rate (FPR) to evaluate sensor and algorithm performance under various factors such as geometric relationships, object properties, sensor resolution, and environmental conditions. We introduce the concept of occupancy queries to quantify the perception requirements for sampling-based motion planners, and propose an Integer Linear Programming (ILP) approach for efficient sensor and algorithm selection and placement. This forms the basis for a co-design optimization that includes the robot body, motion planner, perception pipeline, and computing unit. A case study on developing an Autonomous Vehicle (AV) for urban scenarios provides actionable information for designers, and shows that complex tasks escalate resource demands, with task performance affecting choices of the autonomy stack. The study demonstrates that resource prioritization influences sensor choice: cameras are preferred for cost-effective and lightweight designs, while lidar sensors are chosen for better energy and computational efficiency.},
	issn = {1552-3098},
	keywords = {Mobile robot; Co-design; Sensor selection},
	language = {en},
	DOI = {10.3929/ethz-b-000672201},
	title = {Resource-Efficient Task-Driven Co-Design of Perception and Decision Making in Autonomous Robots}
}