Search Results for author: Abraham P. Vinod

Found 8 papers, 2 papers with code

Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning

no code implementations31 Oct 2023 Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano

For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning.

Collision Avoidance Motion Planning +2

Safely: Safe Stochastic Motion Planning Under Constrained Sensing via Duality

no code implementations5 Mar 2022 Michael Hibbard, Abraham P. Vinod, Jesse Quattrociocchi, Ufuk Topcu

We introduce the Safely motion planner, a receding-horizon control framework, that simultaneously synthesizes both a trajectory for the robot to follow as well as a sensor selection strategy that prescribes trajectory-relevant obstacles to measure at each time step while respecting the sensing constraints of the robot.

Motion Planning

Physical-Layer Security via Distributed Beamforming in the Presence of Adversaries with Unknown Locations

no code implementations28 Feb 2021 Yagiz Savas, Abolfazl Hashemi, Abraham P. Vinod, Brian M. Sadler, Ufuk Topcu

In such a setting, we develop a periodic transmission strategy, i. e., a sequence of joint beamforming gain and artificial noise pairs, that prevents the adversaries from decreasing their uncertainty on the information sequence by eavesdropping on the transmission.

On-The-Fly Control of Unknown Systems: From Side Information to Performance Guarantees through Reachability

no code implementations11 Nov 2020 Franck Djeumou, Abraham P. Vinod, Eric Goubault, Sylvie Putot, Ufuk Topcu

Besides, $\texttt{DaTaControl}$ achieves near-optimal control and is suitable for real-time control of such systems.

On-The-Fly Control of Unknown Smooth Systems from Limited Data

no code implementations27 Sep 2020 Franck Djeumou, Abraham P. Vinod, Eric Goubault, Sylvie Putot, Ufuk Topcu

We investigate the problem of data-driven, on-the-fly control of systems with unknown nonlinear dynamics where data from only a single finite-horizon trajectory and possibly side information on the dynamics are available.

Near-Optimal Reactive Synthesis Incorporating Runtime Information

no code implementations31 Jul 2020 Suda Bharadwaj, Abraham P. Vinod, Rayna Dimitrova, Ufuk Topcu

We consider the problem of optimal reactive synthesis - compute a strategy that satisfies a mission specification in a dynamic environment, and optimizes a performance metric.

Management Motion Planning

Stochastic reachability of a target tube: Theory and computation

1 code implementation11 Oct 2018 Abraham P. Vinod, Meeko M. K. Oishi

Of special interest is the stochastic reach set, the set of all initial states from which it is possible to stay in the target tube with a probability above a desired threshold.

Optimization and Control Systems and Control

Probabilistic Occupancy Function and Sets Using Forward Stochastic Reachability for Rigid-Body Dynamic Obstacles

1 code implementation19 Mar 2018 Abraham P. Vinod, Meeko M. K. Oishi

We present theory and algorithms for the computation of probability-weighted "keep-out" sets to assure probabilistically safe navigation in the presence of multiple rigid body obstacles with stochastic dynamics.

Systems and Control Optimization and Control

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