Taking inspiration from biological systems to enhance navigational autonomy of robots flying in confined environments.
Flying in such environments implies a number of challenges that are not found in high-altitude, GPS-based, unmanned aerial vehicles (UAVs). These include small size and slow speed for maneuverability, light weight to stay airborne, low-consumption electronics, and smart sensing and control. We believe that neuromorphic vision chips and bio-inspired control strategies are very promising methods to solve this challenge.
The MC2 is based on a microCeline, a 5-gram living room flyer produced by DIDEL equipped with a 4mm geared motor (a) and two magnet-in-a-coil actuators (b) controlling the rudder and the elevator (b). When fitted with the required electronics for autonomous navigation, the total weight reaches 10 grams. The custom electronics consists of a microcontroller board (c) featuring a PIC18LF4620 running at 32MHz, a Bluetooth radio module (for parameter monitoring), and two camera modules, which comprise a CMOS linear camera (TSL3301) and a MEMS rate gyros (ADXRS150) each. One of those camera modules (d) is oriented forward with its rate gyro measuring yaw rotations, and will mainly by used for obstacle avoidance. The second camera module (c) is oriented downwards, looking longitudinally at the ground, while its rate gyro measures rotation about the pitch axis. Each of the cameras have 102 gray-level pixels spanning a total field of view of 120°. In order to measure its airspeed, the MC2 is also equipped with an anemometer (e) consisting of a free propeller and a hall-effect sensor. This anemometer is placed in a region that is not blown by the main propeller (a). The 65mAh Lithium-polymer battery (f) ensures an autonomy of approximately 10 minutes.
The synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, where few systematic techniques exist. Given the importance and pervasiveness of analog networks, there is a founded interest in the development of automatic techniques capable of handling both problems. Evolutionary methods appear as one of the most promising approaches for the fulfillment of this objective.
Analog Genetic Encoding (AGE) is a new way to represent and evolve analog networks. The genetic representation of Analog Genetic Encoding is inspired by the working of biological genetic regulatory networks (GRNs). Like genetic regulatory networks, Analog Genetic Encoding uses an implicit representation of the interaction between the devices that form the network. This results in a genome that is compact and very tolerant of genome reorganizations, thus permitting the application of genetic operators that go beyond the simple operators of mutation and crossover that are typically used in genetic algorithms. In particular, Analog Genetic Encoding permits the application of operators of duplication, deletion, and transpositions of fragments of genome, which are recognized as fundamental for the evolution and complexification of biological organisms. The resulting evolutionary system displays state-of-the-art performance in the evolutionary synthesis and reverse engineering of analog networks.
ECAgents is a transdisciplinary European research project. Its goal is to provide a better understanding of the role of communication in collections of embodied and situated agents (real and simulated robots). This project involves people from different fields such as computer science, robotics, biology, physics and mathematics.
Our work focuses on the prerequisites for communication, which need to be in place before embodied agents can start bootstraping communication systems of any complexity. These prerequisites depend on the complexity of the agents, the complexity of the environment, and the complexity of the agent's set tasks.
We use artificial evolution to search for the emergence of communication and neural networks as the underlying agent control mechanism.
The dynamic of the environment. For instance, an agent should look for food, but food placement as well as food caracteritics, such as colour, are dynamic. Some potential food sources in the world are "good", but others are "bad". The agents need to explore and report their discovery to other agents in order to maximise the group fitness.
The genetic relatedness and the level of selection. Does a homogenous group perform better then a heterogenous one? Is it better to do individual or group selection? Can we find a general principle out of different experiments involving different tasks and environment dynamics? This work is done in collaboration with the EvoAnts projects project.
The neural network architecture. To which point can we go without a hidden layer? Is it necessary to balance sensor modality weight (preprocess vision because of its several pixels)? Is memory (recurent neurons) mandatory? And if yes, how many, with which connections?
The communication medium structure. Is one medium sufficient (like vision)? Is it necessary to have differents channels of differents properties (like sound and vision)? Is local communication mandatory?
Our agents are S-bots, which were created as part of the Swarmbot project. Both simulated and real one are used.
We are exploring and analysing the bootstraping of communication with several starting conditions, neural architectures and evolutionary conditions using virtual s-bots in Enki.
We are porting some of our experiments to the real s-bot robots.
We are also exploring what are the mechanisms to provide a smooth path for evolution of signaling.
To conduct our experiments, we have developed a fast 2D physics-based simulator and an evolutionary framework. Both are open-source.
The s-bots have a diameter of 12 cm and a height of 15cm and possess 2 Lilon batteries, which give it about an hour of autonomy. A 400 MHz custom Xscale CPU board with 64 MB of RAM and 32 MB of flash memory is used for processing, as well as 12 distributed PIC microcontroller for low-level handling.
Fig. 1. Left: Four conditions tested in our experiments.
Fig. 1. Right: Comparison of mean performance with and without communication in the four cases.
Fig. 2. Left: Evolved food signalling strategy.
Fig. Left: The rigid connection can be used to form chains and pass very big obstacles and large gaps.
Fig. Right: Swarm-bot robot configuration to pass a large gap.
Eyebots are autonomous flying robots with powerful sensing and communication abilities for search, monitoring, and pathfinding in built environments. Eyebots operate in swarm formation, as honeybees do, to efficiently explore built environments, locate predefined targets, and guide other robots or humans (figure 1).
Eyebots are part of the Swarmanoid, a European research project aimed at developing an heterogeneous swarm of wheeled, climbing, and flying robots that can carry out tasks normally assigned to humanoid robots. Within the Swarmanoid, Eyebots serve the role of eyes and guide other robots with simpler sensing abilities.
Eyebots can also be deployed on their own in built environments to locate humans who may need help, suspicious objects, or traces of dangerous chemicals. Their programmability, combined with individual learning and swarm intelligence, makes them rapidly adaptable to several types of situations that may pose a danger for humans.
Eyebots are currently under development at LIS, EPFL. We will post additional information on this site as soon as technical documents will be available for public disclosure.
Gliding flight is powerful -to overcome obstacles and travel from A to B.
Fatigue is a major source of stress and accidents in today's world, but there are no objective ways of monitoring and preventing the build-up of fatigue.
Sleep and wake periods are major factors, but not the only ones, that contribute to regulate the onset of fatigue. In this project, we start by developing a non-intrusive, wearable device for monitoring sleep and wake phases.
Since body signals related to sleep and wake are different from person to person, our device incorporates learning technologies adapted from our work on autonomous robotics. This allows the device to self-tune to the user.
The output of the sleep/wake device will then be incorporated into a model of fatigue that takes into account also other body signals and can adapt to the style and physiology of the user.
A version of the sleep/wake device will be tested within the framework of Solar Impulse, where the pilot has to be alert during the entire flight, which can take up to five days and nights. Our device can be used to predict the pilots fatigue and to calculate his optimal break times, always taking into account the mission status.
We show that the co-evolution of active vision and feature selection can greatly reduce the computational complexity required to produce a given visual performance. Active vision is the sequential and interactive process of selecting and analyzing parts of a visual scene. Feature selection instead is the development of sensitivity to relevant features in the visual scene to which the system selectively responds. Each of these processes has been investigated and adopted in machine vision. However, the combination of active vision and feature selection is still largely unexplored.
In our experiments behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons are evolved while they freely interact with their environments. We describe the application of this methodology in three sets of experiments, namely, shape discrimination, car driving, and robot navigation. We show that these systems develop sensitivity to a number of oriented, retinotopic, visual-feature-oriented edges, corners, height, and a behavioral repertoire. This sensitivity is used to locate, bring, and keep these features in particular regions of the vision system, resembling strategies observed in simple insects.
In this project we went one step further and investigated the ontogenetic development of receptive fields in an evolutionary mobile robot with active vision. In contrast to the previous work where synaptic weights for both receptive field and behavior were genetically encoded and evolved on the same time scale, here the synaptic weights for receptive fields develop during the life of the individual. In these experiments, behavioral abilities and receptive fields develop on two different temporal scales, phylogenetic and ontogenetic respectively. The evolutionary experiments are carried out in physics-based simulation and the evolved controllers are tested on the physical robot in an outdoor environment.
Such a neural architecture with visual plasticity for a freely moving behavioral system also allows us to explore the role of active body movement in the formation of the visual system. More specifically we study the development of visual receptive fields and behavior of robots under active and passive movement conditions. We show that the receptive fields and behavior of robots developed under active condition significantly differ from those developed under passive condition. A set of analyses suggest that the coherence of receptive fields developed in active condition plays an important role in the performance of the robot.
The omnidirectional camera is a relatively new optic device that provides a 360 degrees field of view, and it has been widely used in many practical applications including surveillance systems and robot navigation. However, in most applications visual systems uniformly process the entire image, which would be computationally expensive when detailed information is required. In other cases the focus is determined for particular uses by the designers or users. In other words, the system is not allowed to freely interact with the environment and selectively choose visual features.
Contrarily, all vertebrates and several insects -- even those with a very large field of view -- share the steerable eyes with a foveal region, which means that they have been forced to choose necessary information from a vast visual field at any given time so as to survive. Such a sequential and interactive process of selecting and analyzing behaviorally-relevant parts of a visual scene is called active vision.
In this project we explore omnidirectional active vision: coupled with an omnidirectional camera, a square artificial retina can immediately access any visual feature located in any direction, which is impossible for the conventional pan-tilt camera because of the mechanical constraints. It is challenging for the artificial retina to select behaviorally-relevant features in such a broad field of view.
Active vision may be useful to perform landmark-based navigation where landmark relationship requires active scanning of the environment. In this project we explore this hypothesis by evolving the neural system controlling vision and behavior of a mobile robot equipped with a pan/tilt camera so that it can discriminate visual patterns and arrive at the goal zone. The experimental setup employed in this article requires the robot to actively move its gaze direction and integrate information over time in order to accomplish the task. We show that the evolved robot can detect separate features in a sequential manner and discriminate the spatial relationships. An intriguing hypothesis on landmark-based navigation in insects derives from the present results.
The SMAVNET project aims at developing swarms of flying robots that can be deployed in disaster areas to rapidly create communication networks for rescuers. Flying robots are interesting for such applications because they are fast, can easily overcome difficult terrain, and benefit from line-of-sight communication.
To make aerial swarming a reality, robots and controllers need to be made as simple as possible.
From a hardware perspective, robots are designed to be robust, safe, light-weight and low-cost. Furthermore, protocols and human-swarm interfaces are developed to allow non-experts to easily and safely operate large groups of robots.
From a software perspective, controllers allow flying robots to work together. For swarming, robots react to wireless communication with neighboring robots or rescuers (communication-based behaviors). Using communication as a sensor is interesting because most flying robots are generally equipped with off-the-shelf radio modules that are low-cost, light-weight and relatively long-range. Furthermore, this strategy alleviates the need for position which is required for all existing aerial swarm algorithms and typically requires using sensors that depend on the environment (GPS, cameras) or are expensive and heavy (lasers, radars).
Flying Robots were specifically designed for safe, inexpensive and fast prototyping of aerial swarm experiments.
They are light weight (420 g, 80 cm wingspan) and built out of Expanded Polypropylene (EPP) with an electric motor mounted at the back and two control surfaces serving as elevons (combined ailerons and elevator). The robots runs on a LiPo battery and have an autonomy of 30 min. They are equipped with an autopilot for the control of altitude, airspeed and turn rate. Embedded in the autopilot is a micro-controller that runs a minimalist control strategy based on input from only 3 sensors: one gyroscope and two pressure sensors.
Swarm controllers are implemented on a Toradex Colibri PXA270 CPU board running Linux, connected to an off-the-shelf USB WiFi dongle. The output of these controllers, namely a desired turn rate, speed or altitude, is sent as control command to the autopilot.
In order to log flight trajectories, the robot is further equipped with a u-blox LEA-5H GPS module and a ZigBee (XBee PRO) transmitter.
Designing swarm controllers is typically challenging because no obvious relationship exists between the individual robot behaviors and the emergent behavior of the entire swarm. For this reason, we turn to biology for inspiration.
In a first approach, artificial evolution is used for its potential to automatically discover simple and unthought-of robot controllers. Good evolved controllers are then reverse-engineered so as to capture the simple and efficient solutions found through evolution in hand-designed controllers that are easy to understand and can be modeled. Resulting controllers can therefore be adapted to a variety of scenarios in a predictable manner. Furthermore, they can be extended to accommodate entirely new applications. Reverse-engineered controllers demonstrate a variety of behaviors such as exploration, synchronization, area coverage and communication relay.
In a second approach, inspiration is taken from ants that can optimally deploy to search for and maintain pheromone paths leading to food sources in nature. This is analogous to the deployment and maintenance of communication pathways between rescuers using the SMAVNET.
All necessary software and hardware to perform experiments with 10 flying robots was developed in the scope of this project. To the best of our knowledge, this setup is the one with the most flying robots operating outdoors to this day.
For fast deployment of large swarms, input from the swarm operator must be reduced to a minimum during robot calibration, testing and all phases of flight (launch, swarming, landing). Therefore, robot reliability, safety and autonomy must be pushed to a maximum so that operators can easily perform experiments without safety pilots. In our setup, robots auto-calibrate and perform a self-check before being launched by the operator. Robots can be monitored and controlled though a swarm-interface running on a single computer.
The critical issue of operational safety has been addressed by light-weight, low-inertia platform design and by implementing several security features in software. Among other things, we looked at mid-air collision avoidance using local communication links and negotiation of flight altitudes between robots. By providing a risk analysis for ground impact and mid-air collisions to the Swiss Federal Office for Civil Aviation (FOCA), we obtained an official authorization for beyond-line-of-sight swarm operation at our testing site.