In this work, we present a novel method for automating persistent surveillance missions involving multiple vehicles. Automata-based techniques were used to generate collision-free motion plans for a team of vehicles to satisfy a temporal logic specification. Vector fields were created for use with a differential flatness-based controller, allowing vehicle flight and deployment to be fully automated according to the motion plans. The use of charging platforms with the vehicles allows for truly persistent missions. Experiments were performed with two quadrotors over 50 runs to validate the theoretical results.
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. Our method 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. Building on our previous work [1], the focus of this paper is on the on-line part of the overall method.
In this paper, we propose a sampling-based motion planning algorithm that finds an infinite path satisfying a Linear Temporal Logic (LTL) formula over a set of properties satisfied by some regions in a given environment. The algorithm has three 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 guarantees the low complexity of the overall method. Third, it is probabilistically complete. Examples illustrating the usefulness and the performance of the method are included.
Membrane computing is an interdisciplinary research field focused on new computational models, also known as P systems, inspired by the compartmental model of the cell and the membrane transport mechanisms. Numerical P systems are a type of P systems introduced by Gh. Păun in 2006 for possible applications in economics. Recently, an extension of numerical P systems, enzymatic numerical P systems, has been defined in the context of robot control. This paper presents a new approach to modeling and implementing autonomous mobile robot behaviors and proposes a new odometry module implemented with enzymatic numerical P systems for robot localization. The advantages of modeling robot behaviors with enzymatic membrane controllers and the experimental results obtained on real and simulated robots are also discussed.
The main result of the paper is the proof that Enzymatic Numerical P Sytems with deterministic, but parallel, execution model are universal, even when the production functions used are polynomials of degree 1. This extends previous known results and provides the optimal case in terms of polynomial degree.
Membrane controllers have been developed using Numerical P Systems and their extension, Enzymatic Numerical P Systems, for controlling mobile robots like e-puck and Khepera III. In this paper we prove that membrane controllers can be easily adapted for other types of robotic platforms. Therefore, obstacle avoidance and follower behaviors were adapted for Koala robots. The membrane controllers for Koala robots have been tested on real and simulated platforms. Experimental results and performance analysis are presented.
This paper presents an original approach to the control of mobile robots using natural computing based solutions, which fall beyond traditional use of Artificial Intelligence and bio-inspired methods in robot control. The idea is to use the modeling power of P systems, which are based on the structure and functioning of living cells. Results obtained so far are presented in a synthetic way and directions for further developments are given.
A multi-agent control architecture for swarm robotics applications which includes an innovative human-swarm interface is proposed. The architecture is designed to allow an operator to monitor and guide a robotic swarm to accomplish its missions. The system is composed of three types of agents, a graphical user interface agent, and a pair of a local and a social agent for each robot in the swarm. The local agent implements low level robot-specific functionalities like movement, obstacle avoidance and localization. The control algorithm is implemented in the social agent and is based on an adapted distributed version of the Particle Swarm Optimization technique. An original method, Gravity Points Method, for representing goals which are used by the human-swarm interface is also proposed. Experimental results using simulated e-puck robots are presented and directions for further developments are given.
A cognitive collaborative multi-agent control architecture that addresses real-world control problems for swarms of mobile robots is proposed. The swarm's emergent behaviour is obtained by using a distributed Particle Swarm Optimization inspired algorithm. A swarm-user interface is also presented and offers a way for a human operator to interact with and guide the robotic swarm without limiting its emergent intelligence. The architecture is designed as a multi-agent system developed using JADE Framework. Three types of agents were defined: local behaviour agent, social behaviour agent and graphical user interface (GUI) agent. Each robot is associated with a pair of a local and a social behaviour agents which implement the reactive component and the interaction between the robots. The swarm forms a hierarchical structure composed of subswarms and neighbourhoods based on tasks and goals defined by the user or the swarm itself. The GUI agent is used as a link between the human expert and the swarm. The architecture was tested on a swarm of e-puck robots.
Ecology is a challenging application area for mobile service robots. They should be able to automate tasks that are too tedious or dangerous for humans to execute, such as collecting waste material. Such a robot must make use of several senses, of which the most important and difficult to implement is vision. This paper presents the cognitive vision system of ReMaster One, an autonomous service robot that is able to recognize and sort waste in an indoor environment. A first prototype has been built and tested with success.
