Human Heuristics for a Team of Mobile Robots

2011

Recently there has been increased interest in the use of cognitive architectures for mobile robots. In this research, a cognitive agent (developed using Soar) has been developed to perform an indoor search and rescue mission using a mobile robot. For this application, sensor processing systems such as image processing, fuzzy logic, and image matching are used to generate information about the environment. The cognitive agent described in this paper is capable of using this information about the environment to explore a building while detecting and recording the locations of common types of intersections, making decisions about where to go based on these intersections, and detecting objects of interest and recording their locations. Results are given for a test in a simple environment.

2000

Teams with up to 12 real robots were given the mission to maintain the energy stocked in their nest by collecting food-items. To achieve this mission efficiently, we implemented a simple and decentralised task allocation mechanism based on individual activation-thresholds, i.e. the energy level of the nest under which a given robot decides to go collect food-items. The experiments show that such a mechanism --- already studied among social insects --- results in an efficient dynamical task allocation even under the noisy conditions prevailing in real experiments. Experiments with different team sizes were carried out to investigate the effect of team size on performance and risk of mission failure. 2000 Elsevier Science B.V. All rights reserved.

2016

With recent advances in robotics technologies and autonomous systems, the idea of human-robot teams is gaining ever-increasing attention. In this context, our research focuses on developing an intelligent robot that can autonomously perform non-trivial, but specific tasks conveyed through natural language. Toward this goal, a consortium of researchers develop and integrate various types of intelligence into mobile robot platforms, including cognitive abilities to reason about high-level missions, perception to classify regions and detect relevant objects in an environment, and linguistic abilities to associate instructions with the robot’s world model and to communicate with human teammates in a natural way. This paper describes the resulting system with integrated intelligence and reports on the latest assessment.

Human Factors: The Journal of the Human Factors and Ergonomics Society, 2010

Objective: The number of robots an operator can supervise increases with the robots’ level of autonomy. The reported study investigates multirobot foraging to identify aspects of the task most suitable for automation. Background: Many envisioned applications of robotics involve multirobot teams. One of the simplest of these applications is foraging, in which robots are operated independently to explore and discover targets. Depending on levels of autonomy and task, operators have been found able to manage 3 to 12 robots. Method: The foraging task can be functionally subdivided into visiting new regions and identifying targets. In the reported experiment, full-task foraging performance was compared with exploration and perceptual search performance for 4-, 8-, and 12-robot teams in a between-groups repeated measures design. Results: Operators in the full-task condition could not successfully manage 12 robots, finding only half as many victims as perceptual search operators. Explorati...

Proceedings of the 11th Intl. FLAIRS Conference, …

This work presents an application of the agent tech-nology to the simtdation in the robotic domain. A group of robots cooperate with each other to simulate a large surface which as a labyrinth structure. A robot is represented as a specialist agent which as for task to recognize ...

2007

Robot foraging, a frequently used test application for collective robotics, consists in a group of robots retrieving a set of opportunely defined objects to a target location. A commonly observed experimental result is that the retrieving efficiency of the group of robots, measured for example as the number of units retrieved by a robot in a given time interval, tends to decrease with increasing group sizes. In this paper we describe a biology inspired method for tuning the number of foraging robots in order to improve the group efficiency.

2010

Abstract As the number of applications for human-robot teaming continue to rise, there is an increasing need for planning technologies that can guide robots in such teaming scenarios. In this article, we focus on adapting planning technology to Urban Search And Rescue (USAR) with a human-robot team. We start by showing that several aspects of state-of-the-art planning technology, including temporal planning, partial satisfaction planning, and replanning, can be gainfully adapted to this scenario.

International Joint Conference on Artificial Intelligence, 2007

When animals (including humans) first explore a new environment, what they remember is fragmen- tary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more pow- erful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information,

IEEE Access

Numerous applications, such as material handling, manufacturing, security, and automated transportation systems, use mobile robots. Autonomous navigation remains one of the primary challenges of the mobile robot industry; many new control algorithms have been recently developed that aim to overcome this challenge. These algorithms are primarily related by their adoption of new strategies for avoiding obstacles and minimizing the travel time to a target along an optimal path. In this paper, we introduce four different navigation systems for an autonomous mobile robot (PowerBot) and compare them. The four systems are based on a fuzzy logic controller (FLC). The FLC of one system is tuned by an inexperienced human (naive), while the three other FLCs are optimized through a genetic algorithm (GA), particle swarm optimization (PSO), and a human expert. We hope the comparison answers the question of which is the best controller. In other words, ''who can win?,'' the naive, the GA, the PSO, or the expert, in fine tuning the membership functions of the navigation and obstacle avoidance behavior of the mobile robot? To answer this question, we used four different techniques for optimization (the naive FLC, GA, PSO, and FLC-expert) and used many criteria for comparison, whereas other research papers have dealt with two techniques at a time.

RoboCup 2016: Robot World Cup XX

We consider the problem of exploring a known structured environment to find an object with a mobile robot. We proposed a novel heuristic-based strategy for reducing the traveled distance by first obtaining an exploration order of the rooms in the environment and then, searching for the object in each room by positioning the robot through a set of viewpoints. For the exploration order we proposed a heuristic based on the distance from the robot to the room, the probability of finding the object therein and the room area; integrated in a O(n 2) complexity greedy algorithm that selects the next room. The experimental results show an advantage of the proposed heuristic over other methods in terms of expected traveled distance, except for full search which has a complexity of O(n!). For the exploration within each room, we integrate the localization of horizontal flat surfaces with the generation of poses. With the set of poses, a similar heuristic establishes the exploration order that guides the robot path inside the room. The evaluation of the set of poses shows an average coverage of the flat surfaces of more than 90% when it is configured with an overlap of 40%. Experiments were performed with a real robot using three objects in a six-room environment. The success rate for the robot finding the object is 86.6%.