A Holistic Approach to Energy Harvesting for Indoor Robots

Service robotics is a fast expanding market. Inside households, domestic robots can now accomplish numerous tasks, such as floor cleaning, surveillance, or remote presence. Their sales have considerably increased over the past years. Whereas 1.05 million domestic service robots were reportedly sold in 2009, at least 2.7 million units were sold in 2013. Consequently, this growth gives rise to an increase of the energy needs to power such a large and growing fleet of robots. However, the unique properties of mobile robots open some new fields of research. We must find technologies that are suitable for decreasing the energy requirements and thus further advance towards a sustainable development. This thesis tackles two fundamental goals based on a holistic approach of the global problem. The first goal is to reduce the energy needs by identifying key technologies in making energy-efficient robots. The second goal is to leverage innovative indoor energy sources to increase the ratio of renewable energies scavenged from the environment. To achieve our first goal, new energy-wise metrics are applied to real robotic hardware. This gives us the means to assess the impact of some technologies on the overall energy balance. First, we analysed seven robotic vacuum cleaners from a representative sample of the market that encompasses a wide variety of technologies. Simultaneous Localisation and Mapping (SLAM) was identified as a key technology to reduce energy needs when carrying out such tasks. Even if the instantaneous power is slightly increased, the completion time of the task is greatly reduced. We also analysed the needed sensors to achieve SLAM, as they are largely diversified. This work tested three sensors using three different technologies. We identified several important metrics. As of our second goal, potential energy sources are compared to the needs of an indoor robot. The sunshine coming through a building's apertures is identified as a promising source of renewable power. Numerical simulations showed how a mobile robot is mandatory to take full advantage of this previously unseen situation, as well as the influence of the geometric parameters on the yearly energy income under ideal sunny conditions. When considering a real system, the major difficulty to overcome is the tracking of the sunbeam along the day. The proposed algorithm uses a hybrid method. A high-level cognitive approach is responsible for the initial placement. Following realignments during the day are performed by a low-level reactive behaviour. A solar harvesting module was developed for our research robot. The tests conducted inside a controlled environment demonstrate the feasibility of this concept and the good performances of the aforementioned algorithm. Based on a realistic scenario and weather conditions, we computed that between 1 and 14 days of recharge could be necessary for a single cleaning task. In the future, our innovative technology could greatly lower the energy needs of service robots. However, it is not completely possible to abandon the recharge station due to occasional bad weather. The acceptance of this technology inside the user's home ecosystem remains to be studied.

Synthesis, modeling, and experimental validation of distributed robotic search

James Pugh

The work of this thesis centers around the research subject of distributed robotic search. Within the field of distributed robotic systems, a task of particular interest is attempting to locate one or more targets in a possibly unknown environment. While numerous studies have proposed and analyzed methods to accomplish this, there is currently a lack of a strong foundation of tools and techniques that can be used to facilitate the development and evaluation of different approaches to distributed robotic search. In this work, we aim to provide such a foundation through tools, methods, models, and analysis of experimentation. An often overlooked aspect of the distributed robotic research process is the development and analysis of tools and modules to be used with robotic systems. These may include plug-ins for realistic robotic simulators, software/hardware systems to track multiple mobile robots in real-time, extension boards for robotic platforms, and the robots themselves. Along with other tools developed and used for this work, we focus particularly on the development, characterization, and validation of a fast, accurate on-board system for relative positioning and communication between robots, a capability which is critical for effective distributed robotic search. Designing individual robot controllers to generate a specific group behavior is a difficult and often counter-intuitive process. A possible alternative to hand-crafting distributed search controllers is automatic synthesis using machine-learning techniques. We explore the effectiveness of using a noise-resistant version of the Particle Swarm Optimization algorithm to optimize the weights of an embedded Artificial Neural Network, allowing the robot to learn obstacle avoidance behavior (a common benchmark for robotic learning techniques); we find that this technique appears to offer superior performance as compared to the canonical approach of using Genetic Algorithms for this type of learning. A method for faster learning using distributed evaluation in a robot team is tested and is found to offer comparable performance using only a fraction of the original learning time. This technique can be used for fast, effective learning and adaptation in a distributed robotic system performing search. The process of designing and analyzing algorithms for distributed robotic systems can be greatly facilitated if models are available to describe the dynamics of the algorithm at an abstract level. Inspired by previous examples in the distributed robotic field, we work to design a model of robotic search that captures the system at different levels of abstraction, ranging from accurately recreating the details of individual robots to describing the entire system as an indivisible whole. To capture the entire search process, we model both the exploration phase, where robots cover an environment in an effort to detect traces of targets, and the localization phase, where robots use target emission sensing to navigate towards the target. The utility of our models is demonstrated by using them to develop an effective technique for the declaration phase of search, where robots decide that a target has been accurately localized and announce its position. In distributed robotics research, it is important that techniques developed with abstracted simulations and models are ultimately validated using real robotic platforms in order to verify their correctness. In that spirit, we run systematic sound search experiments using teams of up to ten real robots. These experiments utilize the tools developed throughout the research process, demonstrate the utility of our learning technique for fast search adaptation, and serve to validate our models of distributed robotic localization. In addition, they allow us to analyze and better understand the subtle dynamics of the search process, providing information which should be useful for any future work on distributed robotic search.

EPFL2008

Cutting Down the Energy Consumed by Domestic Robots: Insights from Robotic Vacuum Cleaners

Francesco Mondada, Philippe Rétornaz, Florian Christopher Vaussard

The market of domestic service robots, and especially vacuum cleaners, has kept growing during the past decade. According to the International Federation of Robotics, more than 1 million units were sold worldwide in 2010. Currently, there is no in-depth analysis of the energetic impact of the introduction of this technology on the mass market. This topic is of prime importance in our energy-dependant society. This study aims at identifying key technologies leading to the reduction of the energy consumption of a domestic mobile robot, by exploring the design space using technologies issued from the robotic research field, such as the various localization and navigation strategies. This approach is validated through an in-depth analysis of seven vacuum cleaning robots. These results are used to build a global assessment of the influential parameters. The major outcome is the assessment of the positive impact of both the ceiling-based visual localization and the laser-based localization approaches.

Springer Berlin Heidelberg2012

Topological SLAM

Adriana Tapus

This thesis is about topological navigation, more precisely about space representation, perception, localization and mapping. All these elements are needed in order to obtain a robust and reliable framework for navigation. This is essential in order to move in an environment, manipulate objects in it and avoid collisions. The method proposed in this dissertation is suitable for fully autonomous mobile robots, operating in structured indoor and outdoor environments. High robustness is necessary to obtain a distinctive and reliable representation of the environment. This can be obtained by combining the information acquired by several sensors with complementary characteristics. A multimodal perception system that includes the wheel encoders for odometry and two extereoceptive sensors composed of a laser range finder that gives a 360° view of the environment and an omnidirectional camera are used in this work. Significant, robust and stable features are extracted from sensory data. The different features extracted from the extereoceptive sensors (i.e. corners, vertical edges, color patches, etc.) are fused and combined into a single, circular, and distinctive space representation named the fingerprint of a place. This representation is adapted for topological navigation and is the foundation for the whole dissertation. One of the major tasks of a mobile robot is localization. Different topological localization approaches based on the fingerprint concept are presented in this dissertation. Localization on a fingerprint-based representation is reduced to a problem of fingerprint matching. Two of these methods make use of the Bayesian Programming (BP) formalism and two others are based on dynamic programming. They also show how multimodal perception increases the reliability of topological localization for mobile robots. In order to autonomously acquire and create maps, robots have to explore their environment. Several exploration tools for indoor environments are presented: wall following, mid-line following, center of free space of a room, door detection, and environment structure identification. An automatic and incremental topological mapping system based on fingerprints of places and a global localizer using Partially Observable Markov Decision Processes (POMDP) are proposed. The construction of a topological mapping system is combined with localization, both relying on fingerprints of places, in order to perform Simultaneous Localization and Mapping (SLAM). This enables navigation of an autonomous mobile robot in a structured environment without relying on maps given a priori, without using artificial landmarks and by employing a semantic spatial representation that allows a more natural interface between humans and robots. The fingerprint approach, combining the information from all sensors available to the robot, reduces perceptual aliasing and improves the distinctiveness of places. This fingerprint-based approach yields a consistent and distinctive representation of the environment and is extensible in that it permits spatial cognition beyond just pure navigation. All these methodologies have been validated through experiments. Indoor and outdoor experiments have been conducted over a distance exceeding 2 km. The fingerprints of places proved to provide a compact and distinctive methodology for space representation and place recognition – they permit encoding of a huge amount of placerelated information in a single circular sequence of features. The experiments have verified the efficacy and reliability of this approach.