13 accepted papers
In this work, we investigate cooperative strategies for an evader drone team of various sizes using multi-agent reinforcement learning in a multi-agent pursuit-evasion scenario. The objective of the evader team is to reach a goal with minimal velocity while not colliding with the pursuer team. The o…
The integration of autonomous systems into civil and military aviation is shifting crew composition from purely human to collaborative human-autonomy teams. Introducing autonomy enables the implementation of automated safety mechanisms. In this letter, we propose a novel safety barrier that adjusts
This paper proposes an optimization-based task and motion planning framework, named “Logic Network Flow”, to integrate signal temporal logic (STL) specifications into efficient mixed-binary linear programmings. In this framework, temporal predicates are encoded as polyhedron constraints on each edge
Humanoid robots offer significant advantages for search and rescue tasks, thanks to their capability to traverse rough terrains and perform transportation tasks. In this study, we present a task and motion planning framework for search and rescue operations using a heterogeneous robot team composed
We study the problem of bipedal robot navigation in complex environments with uncertain and rough terrain. In particular, we consider a scenario in which the robot is expected to reach a desired goal location by traversing an environment with uncertain terrain elevation. Such terrain uncertainties i…
In this work we develop a software-in-the-loop simulator platform for Crazyflie nano quadrotor drone fleets. One of the challenges in maintaining a large fleet of drones is ensuring that the fleet performs its task as expected without collision, and this becomes more challenging as the number of dro…
This paper presents an incremental replanning algorithm, dubbed LTL-D*, for temporal-logic-based task planning in a dynamically changing environment. Unexpected changes in the environment may lead to failures in satisfying a task specification in the form of a Linear Temporal Logic (LTL). In this st…
We consider the problem of tracking a reference trajectory for dynamical systems subject to a priori unknown state-dependent disturbance behavior. We propose a formulation that embeds the uncertain system into a higher dimensional deterministic system that accounts for worst case disturbances. Our m…
In this paper we discuss how to use a barrier function that is subject to kinematic constraints and limited sensing in order to guarantee that fixed wing unmanned aerial vehicles (UAVs) will maintain safe distances from each other at all times despite being subject to sensing constraints. Prior work…
This paper proposes a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field. The algorithm is based on a bio-inspired approach known as Speeding-Up and Slowing-Down (SUSD) for distributed source seeking problems. Our algorithm leverages a Gauss…
In this paper, we propose a graph-based search method to optimally allocate tasks to a team of robots given a global task specification. In particular, we define these agents as discrete transition systems. In order to allocate tasks to the team of robots, we decompose finite linear temporal logic (…
Control barrier functions are mathematical constructs used to guarantee safety for robotic systems. When integrated as constraints in a quadratic programming optimization problem, instantaneous control synthesis with real-time performance demands can be achieved for robotics applications. Prevailing…
In this paper, we study the problem of navigating quadcopters through a sequence of hoops. The specification may be given directly or indirectly via a linear temporal logic (LTL) formula. We approach this problem in three phases. First, we introduce a planner that generates a path through a given se…