We have multiple papers accepted by ICRA 2020, congratulations to those lab members! See below for details:
Multi-Robot Path Deconfliction through Prioritization by Path Prospects
Wu, Wenying | University of Cambridge
Bhattacharya, Subhrajit | Lehigh University
Prorok, Amanda | University of Cambridge
Abstract: This work deals with the problem of planning conflict-free paths for mobile robots in cluttered environments. Since centralized, coupled planning algorithms are computationally intractable for large numbers of robots, we consider decoupled planning, in which robots plan their paths sequentially in order of priority. Choosing how to prioritize the robots is a key consideration. State-of-the-art prioritization heuristics, however, do not model the coupling between a robot’s mobility and its environment. This is particularly relevant when prioritizing between robots with different degrees of mobility. In this paper, we propose a prioritization rule that can be computed online by each robot independently, and that provides consistent, conflict-free path plans. Our innovation is to formalize a robot’s path prospects to reach its goal from its current location. To this end, we consider the number of homology classes of trajectories, which capture distinct prospects of paths for each robot. This measure is used as a prioritization rule, whenever any robots enter negotiation to deconflict path plans. We perform simulations with heterogeneous robot teams and compare our method to five benchmarks. Our method achieves the highest success rate, and strikes a good balance between makespan and flowtime objectives.
Dense R-Robust Formations on Lattices
Guerrero-Bonilla, Luis | KTH Royal Institute of Technology
Saldaña, David | Lehigh University
Kumar, Vijay | University of Pennsylvania
Abstract: Robot networks are susceptible to fail under the presence of malicious or defective robots. Resilient networks in the literature require high connectivity and large communication ranges, leading to high energy consumption in the communication network. This paper presents robot formations with guaranteed resiliency that use smaller communication ranges than previous results in the literature. The formations can be built on triangular and square lattices in the plane, and cubic lattices in the three-dimensional space. We support our theoretical framework with simulations.
Estimation with Fast Feature Selection in Robot Visual Navigation
Mousavi, Hossein K. | Lehigh University
Motee, Nader | Lehigh University
Abstract: We consider the robot localization problem with sparse visual feature selection. The underlying key property is that contributions of trackable features (landmarks) appear linearly in the information matrix of the corresponding estimation problem. We utilize standard models for motion and vision system using a camera to formulate the feature selection problem over moving finite-time horizons. We propose a scalable randomized sampling algorithm to select more informative features to obtain a certain estimation quality. We provide probabilistic performance guarantees for our method. The time-complexity of our feature selection algorithm is linear in the number of candidate features, which is practically plausible and outperforms existing greedy methods that scale quadratically with the number of candidate features. Our numerical simulations confirm that not only the execution time of our proposed method is comparably less than that of the greedy method, but also the resulting estimation quality is very close to the greedy method.
Self-Reconfiguration in Response to Faults in Modular Aerial Systems
Gandhi, Neeraj | University of Pennsylvania
Saldaña, David | Lehigh University
Kumar, Vijay | University of Pennsylvania
Phan, Linh Thi Xuan | University of Pennsylvania
Abstract: We present a self-reconfiguration technique bywhich a modular flying platform can mitigate the impact of rotor failures. In this technique, the system adapts its configuration in response to rotor failures to be able to continue its mission while efficiently utilizing resources. A mixed integer linear program determines an optimal module-to-position allocation in the structure based on rotor faults and desired trajectories. We further propose an efficient dynamic programming algorithm that minimizes the number of disassembly and reassembly steps needed for reconfiguration. Evaluation results show that our technique can substantially increase the robustness of the system while utilizing resources efficiently, and that it can scale well with the number of modules.