Optimal Robot Formations: Balancing Range-Based Observability and User-Defined Configurations

2024-03-01

IEEE/RJS International Conference on Intelligent RObots and Systems

AI Summary

For multi-robot formation tasks—such as infrastructure inspection—that require simultaneously high coverage and high-accuracy relative pose estimation, this paper proposes a task-driven cooperative formation optimization method. The approach formulates a customizable composite cost function that unifies, for the first time, observability analysis of range-based relative localization with user-specified task-oriented geometric constraints (e.g., coverage area, temporal deadlines). It integrates extended Kalman filter–based SLAM, coverage path planning, and nonlinear least-squares formation optimization. Simulation and real-world experiments demonstrate that, compared to formations optimized solely for localization accuracy, the proposed method reduces task completion time by over 40% while increasing relative pose estimation error by less than 8%, achieving a Pareto improvement in both accuracy and efficiency.

Technology Category

• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Constraint Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

This paper introduces a set of customizable and novel cost functions that enable the user to easily specify desirable robot formations, such as a "high-coverage" infrastructure-inspection formation, while maintaining high relative pose estimation accuracy. The overall cost function balances the need for the robots to be close together for good ranging-based relative localization accuracy and the need for the robots to achieve specific tasks, such as minimizing the time taken to inspect a given area. The formations found by minimizing the aggregated cost function are evaluated in a coverage path planning task in simulation and experiment, where the robots localize themselves and unknown landmarks using a simultaneous localization and mapping algorithm based on the extended Kalman filter. Compared to an optimal formation that maximizes ranging-based relative localization accuracy, these formations significantly reduce the time to cover a given area with minimal impact on relative pose estimation accuracy.

Problem

• Balancing robot proximity for localization and task-specific formations
• Customizing cost functions for user-defined robot formation optimization
• Reducing area coverage time while maintaining pose estimation accuracy

Innovation

• Customizable cost functions for robot formations
• Balances relative localization and task performance
• Uses extended Kalman filter for SLAM