Mobile robots are cyber-physical systems where the cyberspace and the physical world are strongly coupled. Attacks against mobile robots can transcend cyber defenses and escalate into disastrous consequences in the physical world. In this paper, we focus on the detection of active attacks that are capable of directly influencing robot mission operation. Through leveraging physical dynamics of m...

A method for evolving behavior-based robot controllers using genetic programming is presented. Due to their hierarchical nature, genetic programs are useful representing high-level knowledge for robot controllers. One drawback is the difficulty of incorporating sensory inputs. To overcome the gap between symbolic representation and direct sensor values, the elements of the function set in genet...

RALF SALOMON Department of Computer Science, University of Zurich Winterthurerstrasse 190, 8057 Zurich, Switzerland FAX: +41-1-635 68 09; Email: [email protected] ABSTRACT : This paper proposes a new self-organizing control architecture for mobile robots. It consists of a controller and a value system. The controller uses visual sensors to determine the motor commands, whereas the value syst...

Techniques developed in the fields of evolutionary computation, adaptive systems, agents, and artificial neural networks can be used in entertainment robotics in order to provide easy access to the robot technology. We have developed a number of user-guided approaches based on the techniques from these research fields. These techniques include user-guided behaviour-based systems, user-guided ev...

This paper proposes a new self-organizing, biologically-inspired control architecture for mobile robots consisting of a controller and a value system. The controller uses activity patterns of visual sensors to determine the motor commands, whereas the value system receives stimuli from proprioceptive sensors. This design decision is justified by the following arguments: (1) the feedback of prop...

The goal of this work is to augment reinforcement learning techniques for autonomous robot navigation with a state space encoding more representative of the actual state of the robot in its environment, than available from direct sensor readings. A second goal is to demonstrate the approach in a real-world setting, using the microrobot Khepera (K-Team, Lausanne, Switzerland). The choice of stat...

In this paper we investigate real-time adaptive extensions of our fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. The main idea is to introduce active battery level sensors and recharge zones to improve robot behavior for reaching survivability in environment exploration. In order to achieve this goal, we propose an impro...

This paper presents a predictive model of sensor readings for mobile robot. The model predicts sensor readings for given time horizon based on current sensor readings and velocities of wheels assumed for this horizon. Similar models for such anticipation have been proposed in the literature. The novelty of the model presented in the paper comes from the fact that its structure takes into accoun...

Within the context of learning sequences of basic tasks to build a complex behavior a method is proposed to coordinate a hierarchical set of tasks Each one pos sesses a set of sub tasks lower in the hierarchy which must be coordinated to respect a binary perceptive con straint For each task the coordination is achieved by a reinforcement learning inspired algorithm based on an heuristic which d...

The utilization of a multi-objective approach for evolving artificial neural networks that act as the controllers for phototaxis and radio frequency (RF) localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment is discussed in this chapter. It explains the comparison performances among the elitism without archive and elitism with archive used in the evoluti...