A Bio-inspired Autonomous Navigation Controller for Differential Mobile Robots Based on Crowd Dynamics

This article extends ideas from crowd dynamics to a navigationcontroller for mobile robots. Each mobile robot is consideredas an agent, associated to a comfort zone with a certain radius, whichcreates a repulsive force when this comfort zone is violated by its environmentor by another agent, therefore avoiding collisions. Meanwhile,attractive forces drive the agents from their instantaneous position toa goal position. The resulting navigation controller is tested by simulationsand experiments. It is found that simulations capture the globaldynamic behavior that is shown in experiments, showing robustness ofthe proposed navigation controller.This article extends ideas from crowd dynamics to a navigation controller for mobile robots. Each mobile robot is considered as an agent, associated to a comfort zone with a certain radius, which creates a repulsive force when this comfort zone is violated by its environment or by another agent, therefore avoiding collisions. Meanwhile, attractive forces drive the agents from their instantaneous position to a goal position. The resulting navigation controller is tested by simulations and experiments. It is found that simulations capture the global dynamic behavior that is shown in experiments, showing robustness of the proposed navigation controller

Aerial collective systems

Deployment of multiple flying robots has attracted the interest of several research groups in the recent times both because such a feat represents many interesting scientific challenges and because aerial collective systems have a huge potential in terms of applications. By working together, multiple robots can perform a given task quicker or more efficiently than a single system. Furthermore, multiple robots can share computing, sensing and communication payloads thus leading to lighter robots that could be safer than a larger system, easier to transport and even disposable in some cases. Deploying a fleet of unmanned aerial vehicles instead of a single aircraft allows rapid coverage of a relatively larger area or volume. Collaborating airborne agents can help each other by relaying communication or by providing navigation means to their neighbours. Flying in formation provides an effective way of decongesting the airspace. Aerial swarms also have an enormous artistic potential because they allow creating physical 3D structures that can dynamically change their shape over time. However, the challenges to actually build and control aerial swarms are numerous. First of all, a flying platform is often more complicated to engineer than a terrestrial robot because of the inherent weight constraints and the absence of mechanical link with any inertial frame that could provide mechanical stability and state reference. In the first section of this chapter, we therefore review this challenges and provide pointers to state-of-the-art methods to solve them. Then as soon as flying robots need to interact with each other, all sorts of problems arise such as wireless communication from and to rapidly moving objects and relative positioning. The aim of section 3 is therefore to review possible approaches to technically enable coordination among flying systems. Finally, section 4 tackles the challenge of designing individual controllers that enable a coherent behavior at the level of the swarm. This challenge is made even more difficult with flying robots because of their 3D nature and their motion constraints that are often related to the specific architectures of the underlying physical platforms. In this third section is complementary to the rest of this book as it focusses only on methods that have been designed for aerial collective systems

Aerial Human-Comfortable Collision-free Navigation in Dense Environments

With current overuse of the road transportation system and planned increase in traffic, inno- vative solutions that overcome environmental and financial cost of the current system should be assessed. A promising idea is the use of the third dimension for personal transportation. Therefore, the European project myCopter, funded under the 7th framework, aimed at en- abling the technologies for Personal Aerial Transportation Systems as breakthrough in 21st century transportation systems. This project was the starting point of this thesis. When multiple vehicles share a common part of the sky, the biggest challenge is the man- agement of the risk of collision. While optimal collision-free navigation strategies have been proposed for autonomous robots, trajectories and accelerations for Personal Aerial Vehicles (PAVs) should also take into account human comfort for their passengers, which has rarely been the focus of these studies. Comfort of the trajectories is a key factor in order for this new transportation mean to be accepted and adopted by everyday users. Existing strategies used to maximize human-comfort of trajectories are based on path planning strategies, which compute beforehand the whole trajectory, implementing comfort as an optimization criteria. Personal Aerial Transportation Systems will have a high density of vehicles, where the time to react to potential threats might decrease to a few seconds only. This might be insufficient to compute a new trajectory each time using these path planning strategies. Therefore, in this thesis, a reactive decentralized strategy is proposed, maximizing the comfort of the trajectories for humans traveling in a Personal Aerial Vehicle. To prove the feasibility of collision avoidance strategies, it is not sufficient anymore to validate them only in simulation, but, in addition, real-time tests in a realistic outdoor environment should be performed. Nowadays, single drones can be effectively controlled by a single operator on the ground. The challenge relies instead on an efficient management of a whole swarm of drone. In this thesis, a framework to perform outdoor drone experiment was developed in order to validate the proposed collision avoidance strategy. On the one hand, an autopilot framework was developed, tailored for multi-drone experiments, allowing fast and easy deployment and maintenance of a swarm of drones. On the other hand, a ground control interface is proposed in order to monitor, control and maintain safety in a flight with a swarm of drones. Using the autopilot framework together with the ground control interface, the proposed collision avoidance strategy was validated using 10 quadrotors flying autonomously outdoor in a challenging scenario

Make robots Be Bats: Specializing robotic swarms to the Bat algorithm

Bat algorithm is a powerful nature-inspired swarm intelligence method proposed by Prof. Xin-She Yang in 2010, with remarkable applications in industrial and scientific domains. However, to the best of authors' knowledge, this algorithm has never been applied so far in the context of swarm robotics. With the aim to fill this gap, this paper introduces the first practical implementation of the bat algorithm in swarm robotics. Our implementation is performed at two levels: a physical level, where we design and build a real robotic prototype; and a computational level, where we develop a robotic simulation framework. A very important feature of our implementation is its high specialization: all (physical and logical) components are fully optimized to replicate the most relevant features of the real microbats and the bat algorithm as faithfully as possible. Our implementation has been tested by its application to the problem of finding a target location within unknown static indoor 3D environments. Our experimental results show that the behavioral patterns observed in the real and the simulated robotic swarms are very similar. This makes our robotic swarm implementation an ideal tool to explore the potential and limitations of the bat algorithm for real-world practical applications and their computer simulations.This research has been kindly supported by the Computer Science National Program of the Spanish Research Agency (Agencia Estatal de Investigación) and European Funds, Project #TIN2017-89275-R (AEI/FEDER, UE), the project EVOLFORMAS Ref. #JU12, jointly supported by public body SODERCAN of the Regional Government of Cantabria and the European funds FEDER, the project PDE-GIR of the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions grant agreement #778035, Toho University (Funabashi, Japan), and the University of Cantabria (Santander, Spain). The authors are particularly grateful to the Department of Information Science of Toho University for all the facilities given to carry out this work. Special thanks are also due to the Editors and the three anonymous reviewers for their encouraging and constructive comments and very helpful feedback that allowed us to improve our paper signi cantly

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

Path planning for mobile robots in the real world: handling multiple objectives, hierarchical structures and partial information

Autonomous robots in real-world environments face a number of challenges even to accomplish apparently simple tasks like moving to a given location. We present four realistic scenarios in which robot navigation takes into account partial information, hierarchical structures, and multiple objectives. We start by discussing navigation in indoor environments shared with people, where routes are characterized by effort, risk, and social impact. Next, we improve navigation by computing optimal trajectories and implementing human-friendly local navigation behaviors. Finally, we move to outdoor environments, where robots rely on uncertain traversability estimations and need to account for the risk of getting stuck or having to change route

Formation-Based Odour Source Localisation Using Distributed Terrestrial and Marine Robotic Systems

This thesis tackles the problem of robotic odour source localisation, that is, the use of robots to find the source of a chemical release. As the odour travels away from the source, in the form of a plume carried by the wind or current, small scale turbulence causes it to separate into intermittent patches, suppressing any gradients and making this a particularly challenging search problem. We focus on distributed strategies for odour plume tracing in the air and in the water and look primarily at 2D scenarios, although novel results are also presented for 3D tracing. The common thread to our work is the use of multiple robots in formation, each outfitted with odour and flow sensing devices. By having more than one robot, we can gather observations at different locations, thus helping overcome the difficulties posed by the patchiness of the odour concentration. The flow (wind or current) direction is used to orient the formation and move the robots up-flow, while the measured concentrations are used to centre the robots in the plume and scale the formation to trace its limits. We propose two formation keeping methods. For terrestrial and surface robots equipped with relative or absolute positioning capabilities, we employ a graph-based formation controller using the well-known principle of Laplacian feedback. For underwater vehicles lacking such capabilities, we introduce an original controller for a leader-follower triangular formation using acoustic modems with ranging capabilities. The methods we propose underwent extensive experimental evaluation in high-fidelity simulations and real-world trials. The marine formation controller was implemented in MEDUSA autonomous vehicles and found to maintain a stable formation despite the multi-second ranging period. The airborne plume tracing algorithm was tested using compact Khepera robots in a wind tunnel, yielding low distance overheads and reduced tracing error. A combined approach for marine plume tracing was evaluated in simulation with promising results

Formation-Based Odour Source Localisation Using Distributed Terrestrial and Marine Robotic Systems

This thesis tackles the problem of robotic odour source localisation, that is, the use of robots to find the source of a chemical release. As the odour travels away from the source, in the form of a plume carried by the wind or current, small scale turbulence causes it to separate into intermittent patches, suppressing any gradients and making this a particularly challenging search problem. We focus on distributed strategies for odour plume tracing in the air and in the water and look primarily at 2D scenarios, although novel results are also presented for 3D tracing. The common thread to our work is the use of multiple robots in formation, each outfitted with odour and flow sensing devices. By having more than one robot, we can gather observations at different locations, thus helping overcome the difficulties posed by the patchiness of the odour concentration. The flow (wind or current) direction is used to orient the formation and move the robots up-flow, while the measured concentrations are used to centre the robots in the plume and scale the formation to trace its limits. We propose two formation keeping methods. For terrestrial and surface robots equipped with relative or absolute positioning capabilities, we employ a graph-based formation controller using the well-known principle of Laplacian feedback. For underwater vehicles lacking such capabilities, we introduce an original controller for a leader-follower triangular formation using acoustic modems with ranging capabilities. The methods we propose underwent extensive experimental evaluation in high-fidelity simulations and real-world trials. The marine formation controller was implemented in MEDUSA autonomous vehicles and found to maintain a stable formation despite the multi-second ranging period. The airborne plume tracing algorithm was tested using compact Khepera robots in a wind tunnel, yielding low distance overheads and reduced tracing error. A combined approach for marine plume tracing was evaluated in simulation with promising results

Autonomous Navigation for Mobile Robots in Crowded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen