Contact-based navigation for an autonomous flying robot

Autonomous navigation in obstacle-dense indoor environments is very challenging for flying robots due to the high risk of collisions, which may lead to mechanical damage of the platform and eventual failure of the mission. While conventional approaches in autonomous navigation favor obstacle avoidance strategies, recent work showed that collision-robust flying robots could hit obstacles without breaking and even self-recover after a crash to the ground. This approach is particularly interesting for autonomous navigation in complex environments where collisions are unavoidable, or for reducing the sensing and control complexity involved in obstacle avoidance. This paper aims at showing that collision-robust platforms can go a step further and exploit contacts with the environment to achieve useful navigation tasks based on the sense of touch. This approach is typically useful when weight restrictions prevent the use of heavier sensors, or as a low-level detection mechanism supplementing other sensing modalities. In this paper, a solution based on force and inertial sensors used to detect obstacles all around the robot is presented. Eight miniature force sensors, weighting 0.9g each, are integrated in the structure of a collision-robust flying platform without affecting its robustness. A proof-of-concept experiment demonstrates the use of contact sensing for exploring autonomously a room in 3D, showing significant advantages compared to a previous strategy. To our knowledge this is the first fully autonomous flying robot using touch sensors as only exteroceptive sensors.

Miniature Cameras for Ultra-Light Flying Robots

Adam Klaptocz

The Laboratory of Intelligent Systems (LIS) at the Swiss Federal Technical Institute at Lausanne (EPFL) is working on a project to create an autonomous flying robot that uses ‘vision’ (i.e. optical flow) to maneuver through small spaces such as corridors in a building. The navigation algorithm uses data from a linear photodiode array and a gyroscope to determine the distance of the robot from an obstacle. This project involves the design of a light-weight vision system to be used on the flying robot. The vision system is made up of two identical modules, each one containing a linear photodiode sensor array, a focusing lens with associated plastic casing, a gyroscope, and the PCB on which the system is mounted. The first module will be pointed ahead of the robot and will be used for lateral steering, and the second module will be pointed straight down and be used to control elevation. The main design constraint is the weight of the system, which has to be below 2g for the robot to fly.

2005

Self-Assembly Strategies in a Group of Autonomous Mobile Robots

Roderich Gross

Robots are said to be capable of self-assembly when they can autonomously form physical connections with each other. By examining different ways in which a system can use self-assembly (i.e., different strategies), we demonstrate and quantify the performance costs and benefits of (i) acting as a physically larger self-assembled entity, (ii) letting the system choose when and if to self-assemble, (iii) coordinating the sensing and actuation of the connected robots so that they respond to the environment as a single collective entity. Our analysis is primarily based on real world experiments in a hill crossing task. The configuration of the hill is not known by the robots in advance—the hill can be present or absent, and can vary in steepness and orientation. In some configurations, the robots can overcome the hill more quickly by navigating individually, while other configurations require the robots to self-assemble to overcome the hill. We demonstrate the applicability of our self-assembly strategies to two other tasks—hole crossing and robot rescue—for which we present further proof-of-concept experiments with real robots.

2010

Energy-Efficient Indoor Search by Swarms of Flying Robots without Global Information

Timothy Stirling

Swarms of robots can quickly search large environments through parallelisation, are robust due to redundancy, and can simplify complex tasks like navigation compared to a single robot. Flying swarms can rapidly cover rough terrain and have elevated sensing, aiding tasks such as search or surveillance. However, flying robots have severe limitations in sensing, processing and energy, which raise three open challenges to realise indoor aerial swarms. The first is goal-directed autonomous flight, which normally requires absolute positioning from GPS or external tracking systems, unavailable in unknown indoor environments. On-board sensing approaches using illumination-dependent cameras or laser scanners that can fail in homogeneous environments require processing on ground stations using long-range communication due to high computational demands. Although optic-flow based approaches can be less computationally demanding, they require significant illumination and contrast, and need additional sensors for goal-directed flight. The second challenge is cooperative search and navigation, which typically requires either absolute positioning, localisation using a priori environment maps or the computationally expensive online creation of maps. Thirdly, collective systems frequently suffer inefficiencies due to interference and unnecessary locomotion, resulting in wasted energy. Prior research to improve coordination usually required maps, centralised processing or GPS and considered only ground robots, but flying robots have significantly different energy characteristics and demands. Alternative strategies are therefore required that do not rely on absolute positioning or localisation, environment maps, long-range communication or centralised processing, referred to as Global Information. This thesis introduces a holistic approach to achieve autonomous flight and navigation by embedding a network of robots in the environment with local sensing, processing and communication. A new methodology is presented for goal-directed autonomous flight using relative positioning in reference to nearby static robots. This allows flying robots to directly perceive their motion and fly across the network through the environment. Comprehensive validation tests demonstrated a quadrotor successfully exploring a confined indoor environment. Efficient navigation is achieved using communication messages propagated across the robot network to retrieve the shortest paths. Search is accomplished with a systematic strategy to dynamically redeploy the robot network to unexplored areas. Extensive realistic simulation analysis showed that swarms of 20–30 robots could search a variety of large indoor environments. To mitigate the short flight endurance, different swarm deployment strategies are systematically compared that reduce robot interference and decrease unnecessary locomotion by exploiting gained environment information. The strategies either control the dispatching rate, robot activation or exploit the network to recruit robots when and where necessary. These comparisons facilitate the selection of the best strategy according to the robot capabilities and application requirements. In summary, this thesis resolves three major challenges in aerial swarm robotics and contributes new methodologies to significantly progress the realisation of energy-efficient indoor search by swarms of flying robots. This research should enable many applications such as locating toxic gas leaks, crowd monitoring, and searching for suspicious objects or injured victims.

EPFL2011

Energy-Efficient Indoor Search by Swarms of Flying Robots without Global Information

Timothy Stirling

EPFL2011