Local reconstruction of differential-drive mobile robots position and orientation is made possible with an accurate odometry calibration. Starting from the encoders readings, and assuming an absolute measurement available, a deterministic filter to localize the vehicle while estimating a proper set of odometric parameters is proposed. A stability analysis guarantees that the position and orient...

In this paper we propose a robotics framework for studying emergence and collective adaptation. We describe two sets of experiments, where a pool of heterogeneous Khepera robots, participate in adapting the collective behavior, without being aware of it but just in virtue of the design of their internal structure. The innovative aspect in our approach rests on a system integrating communication...

Pyro, which stands for Python Robotics, is a Python-based robotics programming environment that enables students to explore topics in robotics. Programming robot behaviors in Pyro is akin to programming in a high-level general purpose programming language; Pyro provides abstractions for low-level robot-specific features much like the abstractions provided in high-level programming languages. Co...

Is it more e cient to use one or several robots? Will the performance of a group of robots working in a collaborative task be enhanced if the robots can communicate with one another? What learning abilities should the robot(s) be provided with for adapting to a continuously changing environment? We address these three issues in a speci c task, namely learning the topography of an environment wh...

A cellular hardware implementation of a spiking neural network with run-time reconfigurable connectivity is presented. It is implemented on a compact custom FPGA board which provides a powerful reconfigurable hardware platform for hardware and software design. Complementing the system, a CPU synthesized on the FPGA takes care of interfacing the network with the external world. The FPGA board an...

Two computationally simple forms of dead reckoning were implemented in a physical robot (Khepera). The precision of homing was compared between both strategies for various environments in which obstacles were distributed in four different ways: regular, at random, clumped, and with walls. Counter intuitively, the computational more complex strategy did not perform better in general. Both strate...

Reinforcement learning (RL) offers a set of various algorithms for in-situation behavior synthesis [1]. The Qlearning [2] technique is certainly the most used of the RL methods. Multilayer perceptron implementations of the Q-learning have been proposed early [3], due to the interest of the restricted memory need and the generalization capability [4]. Self-organizing map implementation of the Q-...

Nolfi in [1] presented an experimental comparison of weight evolution in five different neural network architectures (feed-forward vs. recurrent, modular vs. non-modular) for the control of a Khepera robot, which had to pick up objects with its gripper arm and place them outside an arena. Best results were achieved with a so-called emergent modular architecture. This paper extends Nolfi’s exper...

In this paper, we use a massive modular architecture for the generation of complex behaviours in complex robots within the evolutionary robotics framework. We define two different ways of introducing modularity in neural controllers using evolutionary techniques, which we call strategic and tactical modularity, and show at what modular levels each one acts and how can they be combined for the g...