Publication

Congratulations to the following lab members for their successful submissions to ICRA and ACC 2023!

1. Diego S. D’Antonio, Subhrajit Bhattacharya, and David Saldaña. Forming and Controlling Hitches in Midair Using Aerial Robots, ICRA 2023.
2. Disha Kamale, Sofie Haesaert, Cristian-Ioan Vasile. Cautious Planning with Incremental Symbolic Perception: Designing Verified Reactive Driving Maneuvers, ICRA 2023.
3. Gustavo A. Cardona, Kevin Leahy, Cristian-Ioan Vasile. Temporal Logic Swarm Control with Splitting and Merging, ICRA 2023.
4. Jinda Cui, Jeff Trinkle, Jiawei Xu, David Saldaña. Toward Fine Contact Interactions: Learning to Control Normal Contact Force with Limited Information, ICRA 2023.
5.  Jiawei Xu*, David Saldaña. Finding Optimal Modular Robots for Aerial Tasks, ICRA 2023.
6. Guangyi Liu, Disha Kamale, Cristian-Ioan Vasile, and Nader Motee. Symbolic Perception Risk in Autonomous Driving, ACC 2023.
7. Guangyi Liu, Vivek Pandey, Christoforos Somarakis, and Nader Motee. Risk of Cascading Failures in Multi-agent Rendezvous with Communication Time Delay, ACC 2023.
8. Guangyi Liu, Vivek Pandey, Christoforos Somarakis, and Nader Motee. Cascading Waves of Fluctuation in Time-delay Multi-agent Rendezvous, ACC 2023.
9. Mingyu Cai, Makai Mann, Zachary Serlin, Kevin Leahy, Cristian-Ioan Vasile. Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications, ACC 2023.
10. Danyang Li, Mingyu Cai, Cristian-Ioan Vasile, Roberto Tron. Learning Signal Temporal Logic through Neural Network for Interpretable Classification, ACC 2023.
11. Gustavo A. Cardona, Disha Kamale, Cristian-Ioan Vasile. Mixed Integer Linear Programming Approach for Control Synthesis with Weighted Signal Temporal Logic, HSCC 2023.
12. Invited Session: Guangyi Liu, Christoforos Somarakis, Nader Motee. Risk-Aware Design and Control, ACC 2023.
13. Workshop: Guangyi Liu and Nader Motee. Principles of Risk Quantification in Networked Control Systems, ACC 2023.

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A new paper from AIR lab is available on IJRR:

Reactive sampling-based path planning with temporal logic specifications

Cristian Ioan Vasile | Lehigh University
Xiao Li | Boston University
Calin Belta | Boston University

Abstract: We develop a sampling-based motion planning algorithm that combines long-term temporal logic goals with short-term reactive requirements. The mission specification has two parts: (1) a global specification given as a linear temporal logic (LTL) formula over a set of static service requests that occur at the regions of a known environment, and (2) a local specification that requires servicing a set of dynamic requests that can be sensed locally during the execution. The proposed computational framework consists of two main ingredients: (a) an off-line sampling-based algorithm for the construction of a global transition system that contains a path satisfying the LTL formula; and (b) an on-line sampling-based algorithm to generate paths that service the local requests, while making sure that the satisfaction of the global specification is not affected. The off-line algorithm has four main features. First, it is incremental, in the sense that the procedure for finding a satisfying path at each iteration scales only with the number of new samples generated at that iteration. Second, the underlying graph is sparse, which implies low complexity for the overall method. Third, it is probabilistically complete. Fourth, under some mild assumptions, it has the best possible complexity bound. The on-line algorithm leverages ideas from LTL monitoring and potential functions to ensure progress towards the satisfaction of the global specification while servicing locally sensed requests. Examples and experimental trials illustrating the usefulness and the performance of the framework are included.
 
 

DOI: https://doi.org/10.1177/0278364920918919