Occlusion-based cooperative transport with a swarm of miniature mobile robots

This paper proposes a strategy for transporting a large object to a goal using a large number of mobile robots that are significantly smaller than the object. The robots only push the object at positions where the direct line of sight to the goal is occluded by the object. This strategy is fully decentralized and requires neither explicit communication nor specific manipulation mechanisms. We prove that it can transport any convex object in a planar environment. We implement this strategy on the e-puck robotic platform and present systematic experiments with a group of 20 e-pucks transporting three objects of different shapes. The objects were successfully transported to the goal in 43 out of 45 trials. When using a mobile goal, teleoperated by a human, the object could be navigated through an environment with obstacles. We also tested the strategy in a 3-D environment using physics-based computer simulation. Due to its simplicity, the transport strategy is particularly suited for implementation on microscale robotic systems

Goal Based Human Swarm Interaction for Collaborative Transport

Human-swarm interaction is an important milestone for the introduction of swarm-intelligence based solutions into real application scenarios. One of the main hurdles towards this goal is the creation of suitable interfaces for humans to convey the correct intent to multiple robots. As the size of the swarm increases, the complexity of dealing with explicit commands for individual robots becomes intractable. This brings a great challenge for the developer or the operator to drive robots to finish even the most basic tasks. In our work, we consider a different approach that humans specify only the desired goal rather than issuing individual commands necessary to obtain this task. We explore this approach in a collaborative transport scenario, where the user chooses the target position of an object, and a group of robots moves it by adapting themselves to the environment. The main outcome of this thesis is the design of integration of a collaborative transport behavior of swarm robots and an augmented reality human interface. We implemented an augmented reality (AR) application in which a virtual object is displayed overlapped on a detected target object. Users can manipulate the virtual object to generate the goal configuration for the object. The designed centralized controller translate the goal position to the robots and synchronize the state transitions. The whole system is tested on Khepera IV robots through the integration of Vicon system and ARGoS simulator

Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning

Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. While it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building and building a communication link. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols.Comment: 13 pages, 4 figures, version 2, accepted at ANTS 201

A wearable general-purpose solution for Human-Swarm Interaction

Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. Controlling the motion and behavior of these swarms presents unique challenges for human operators, who cannot yet effectively convey their high-level intentions to a group of robots in application. This work proposes a new human-swarm interface based on novel wearable gesture-control and haptic-feedback devices. This work seeks to combine a wearable gesture recognition device that can detect high-level intentions, a portable device that can detect Cartesian information and finger movements, and a wearable advanced haptic device that can provide real-time feedback. This project is the first to envisage a wearable Human-Swarm Interaction (HSI) interface that separates the input and feedback components of the classical control loop (input, output, feedback), as well as being the first of its kind suitable for both indoor and outdoor environments

Cooperation in Swarms of Robots without Communication

Swarm robotics aims to use a large group of relatively simple robots to solve tasks that can hardly be achieved by a single robot in the group. Compared to single robot systems with increased capability, a swarm robotic system may have advantages in robustness, flexibility and scalability. However, designing cooperative behaviors for a swarm robotic system is a challenging problem, especially when the robots may not have communication capabilities and thus only know local information. For a swarm of miniature mobile robots that cannot communicate explicitly, this thesis studies fully decentralized solutions of two problems. For the problem of cooperative transport, the thesis presents a strategy to push an object that is large enough to occlude the robots' perception of the goal of the transportation. For the problem of pattern formation, the thesis investigates algorithms based on the Brazil nut effect that can organize the swarm of robots into an annular formation. These problems are studied using physics-based computer simulations as well as experimental implementations based on the e-puck robotic platform. The simplicity of the solutions make them suitable for applications that require the individual robots to be as simple as possible. Example application scenarios could be micro robot swarms working in the human body

Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges

Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction. As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans. Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process. Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation's recommendations to select an appropriate level of autonomy at a certain point of time. However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are. We explore open challenges that hamper the process of developing effective flexible autonomy. We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling Conference, Canberra, Australi

Scalable Control Strategies and a Customizable Swarm Robotic Platform for Boundary Coverage and Collective Transport Tasks

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

Classification and Management of Computational Resources of Robotic Swarms and the Overcoming of their Constraints

Swarm robotics is a relatively new and multidisciplinary research field with many potential applications (e.g., collective exploration or precision agriculture). Nevertheless, it has not been able to transition from the academic environment to the real world. While there are many potential reasons, one reason is that many robots are designed to be relatively simple, which often results in reduced communication and computation capabilities. However, the investigation of such limitations has largely been overlooked. This thesis looks into one such constraint, the computational constraint of swarm robots (i.e., swarm robotics platform). To achieve this, this work first proposes a computational index that quantifies computational resources. Based on the computational index, a quantitative study of 5273 devices shows that swarm robots provide fewer resources than many other robots or devices. In the next step, an operating system with a novel dual-execution model is proposed, and it has been shown that it outperforms the two other robotic system software. Moreover, results show that the choice of system software determines the computational overhead and, therefore, how many resources are available to robotic software. As communication can be a key aspect of a robot's behaviour, this work demonstrates the modelling, implementing, and studying of an optical communication system with a novel dynamic detector. Its detector improves the quality of service by orders of magnitude (i.e., makes the communication more reliable). In addition, this work investigates general communication properties, such as scalability or the effects of mobility, and provides recommendations for the use of such optical communication systems for swarm robotics. Finally, an approach is shown by which computational constraints of individual robots can be overcome by distributing data and processing across multiple robots