28 accepted papers
A novel method for optimal motion planning in the context of a class of dynamical system is proposed in this work. Our approach is based on the design of a provably safe and convergent actor structure, which is optimized via a policy iteration method. The proposed actor has wide applications, from c
In this work, a tube-based nearly optimal solution to motion planning in unknown workspaces is presented. The advantages of reactive motion planning are combined with a Policy Iteration Reinforcement Learning scheme to yield a novel solution for unknown workspaces that inherits provable safety, conv…
This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential fluid flow, filling a gap in traditional motion planning techniques by guaranteeing…
In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in…
In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the paramet…
In this letter, a solution to the kinematic optimal motion planning problem is presented, where a previous nearly globally optimal approach is extended to workspaces with internal obstacles. The method is inspired by fundamental properties of velocity fields in the presence of obstacles, where topol
A novel motion planning scheme for optimal navigation in unknown workspaces is proposed in this letter. Based upon the Artificial Harmonic Potential Fields (AHPFs) theory, a robust framework for provably correct (i.e., safe and globally convergent) navigation is enhanced through Integral Reinforceme
A novel reactive method for robot trajectory planning within unknown 2D workspaces is presented in this letter. The trajectories provided by this method stem from an underlying potential field and are provably safe, with asymptotic convergence to the desired position. Given an initially unknown work
In this work, we propose a novel reinforcement learning algorithm to solve the optimal motion planning problem. Particular emphasis is given on the rigorous mathematical proof of safety, convergence as well as optimality w.r.t. to an integral quadratic cost function, while reinforcement learning is
The connectivity of distributed networked multi-robot systems is a crucial operational specification, since the involved robots interact/communicate locally only with their immediate neighbors. Thus, in this work, we propose a distributed algorithm to estimate the algebraic connectivity of the under…
Although Harmonic Potential Fields constitute a powerful tool for tackling the autonomous robot exploration problem, yet their applicability is limited by the heavy computational load involved in solving the Laplace equation in real time. In this letter, we propose a computationally efficient explor
We consider the distributed simultaneous estimation and formation control problem for swarms of identical mobile agents with limited communication, sensing and computation capabilities. In particular, we develop a novel scalable algorithm that encodes the formation specifications of the swarm via ge
In this work, a novel solution to the optimal motion planning problem is proposed, through a continuous, deterministic and provably correct approach, with guaranteed safety and which is based on a parametrized Artificial Potential Field (APF). In particular, Reinforcement Learning (RL) is applied to…
In this work, a hybrid control scheme is presented in order to address the navigation problem for a planar robotic platform of arbitrary shape that is moving inside an obstacle cluttered workspace. Given an initial and desired robot configuration, we propose a methodology based on approximate config…
This work addresses the problem of robot navigation under timed temporal specifications in workspaces cluttered with obstacles. We propose a hybrid control strategy that guarantees the accomplishment of a high-level specification expressed as a timed temporal logic formula, while preserving safety (…
In this letter, we consider the predecessor-following control problem for a platoon of car-like vehicles moving on a planar surface with cyclic obstacles. Each vehicle is equipped with an on-board camera that detects its preceding vehicle, and a laser scanner that detects the obstacles around it. Wi
In this letter, we consider the problem of prescribed time scale robot navigation. Initially, we treat the problem for a special class of configuration spaces, namely, sphere worlds, proposing a time-varying control scheme that drives the robot from any initial condition to an arbitrary neighborhood
Artificial Potential Fields (APFs) constitute an intuitive tool for designing autonomous robot navigation control schemes, though they generally suffer from the existence of local minima which may trap the robot away from its desired configuration, an issue usually addressed by appropriate offline “…
In this paper, we consider a networked multi-robot system operating in an obstacle populated planar workspace under a single leader-multiple followers architecture. We propose a decentralized reconfiguration strategy of the set of connectivity and formation specifications that assures convergence to…
This paper addresses the fault tolerant control problem for an omni-directional mobile platform with four mecanum wheels moving on a well-known flat and constrained workspace with static obstacles. As a fault, we consider the case where a wheel cannot be actuated and hence it rotates freely around i
This paper proposes an autonomous landing scheme for a small-scale flybarless helicopter equipped with low-cost navigation sensors. The main contribution of this paper is the design of a model-free motion controller that guarantees autonomous landing with prescribed transient and steady state respon…
This paper addresses the problem of cooperative object manipulation with the coordination relying solely on implicit communication. We consider a decentralized leader-follower architecture where the leading robot, that has exclusive knowledge of the object's desired trajectory, tries to achieve the…
In this paper, we consider the two dimensional (2-D) predecessor-following control problem for a platoon of unicycle vehicles moving on a planar surface. More specifically, we design a decentralized kinematic control protocol, in the sense that each vehicle calculates its own control signal based so…
In this paper, we study the decentralized control problem of a two-agent system under local goal specifications given as temporal logic formulas. The agents collaboratively carry an object in a leader-follower scheme and lack means to exchange messages on-line, i.e., to communicate explicitly. Speci…
This paper addresses the problem of cooperative object transportation by two nonholonomic wheeled robots, with the coordination relying exclusively on implicit communication. We implement a leader-follower scheme, considering compliant contact between the object and the follower. Only the leader has…
In this work we study the problem of landing a quadrotor on an inclined moving platform. The aerial robot employs an forward looking on-board camera to detect and observe the landing platform, which is carried by a mobile robot moving independently on an inclined surface. The platform may also be ti…
In this paper, we consider the formation control problem for multi-agent systems with unknown nonlinearities and disturbances, under an undirected communication protocol. Exploiting the recently developed prescribed performance control methodology, a robust distributed control scheme of minimal comp…
Grasp planning in multi-robot systems is usually studied in a centralized setting with all robots sharing common knowledge about the overall system. Relaxing this assumption would allow multiple mobile manipulators to cooperate even without strict and precise coordination. Moreover, most typical tas…