This paper proposes a generalized algorithm that enables mobile agents to meet in a bounded region based only on bearing information of other agents within their vicinity. Each agent repeatedly employs a stop-and-go strategy consisting of the following three actions: (1) Estimate the bearing of agents in its vicinity, (2) compute a target point based on the estimates, and (3) move to that target point. The motivation and case study example is a modular robot, the Distributed Flight Array, which we employ to validate the proposed algorithm.

This work addresses communication challenges associated with computationally limited distributed embedded systems. Methods for overcoming these challenges are implemented and tested on a robotics testbed called the Distributed Flight Array (DFA). The DFA consists of distributed computational units that can be assembled at random into a wired and/or wireless mesh network. Coordinate control of the DFA such as driving and flying, which is discussed in previous work, demonstrates successful implementation of various communication tasks in a resource constrained platform.

This paper discusses distributed average consensus in the context of a distributed embedded system with multiple agents connected through a communication network. Adversities such as switching of network topologies, agents joining or leaving the network, and communication link creation or failure may arise in these systems. To address these difficulties, we propose an asynchronous implementation of a distributed average consensus algorithm that has the following properties: (1) unbiased average, (2) homogeneous implementation, (3) robustness to network adversities, (4) dynamic consensus, and (5) well-defined tuning parameters. We demonstrate an application of the implementation on a specific distributed embedded system, the Distributed Flight Array, where we solve two average consensus problems to estimate altitude and tilt of the vehicle from multiple distance measurements.

This paper describes a generalized method for computing the altitude and attitude of a rigid body with respect to an inertial frame using a set of distance measurements obtained from a sensor network. In the case where all sensors are centrally measurable, a linear-optimal estimate is obtained. This method is used as a way for estimating altitude and attitude of the Distributed Flight Array, a modular multi-propeller flying vehicle where each module in the array obtains its own distance measurement and coordinates with its immediate neighbour(s) actions for flight. To account for communication bandwidth constraints, a scalable, distributed scheme is presented where each module shares local information. In the limit of sharing information, each module asymptotically computes the linear-optimal altitude and attitude estimate.

Designing a controller that is scalable, robust, and that can adapt to an arbitrary configuration is one of the major challenges of modular robotics. This paper takes one step forward in addressing this challenge by presenting a methodology for controlling any flight-feasible configuration of a modular flying vehicle, in this case the Distributed Flight Array (DFA). In this work we present a well-structured, parameterized controller and describe a method for optimizing its parameters in order to achieve the best possible performance subject to the system's physical constraints. We then show how the configuration space of the DFA can be parameterized by only a few variables and propose a straightforward approach for mapping this configuration space to its control parameter space.

This paper introduces the Distributed Flight Array which is being developed at ETH Zurich. This multi-propeller platform consists of autonomous single-propeller modules that are able to drive, dock with their peers, and fly in a coordinated fashion. These modules are organized as distributed computational units with minimal sensory input. This is a complex system that is rich in dynamics with plenty of room to explore various distributed estimation and control strategies. Experimental demonstrations in docking, driving, and flight have proven its feasibility.

There is a clear trend toward the miniaturization of medical devices for minimally invasive medical procedures, ranging from diagnosis and targeted drug delivery to complex surgical interventions. Current research focuses on increasing the functionality of commercially successful capsule endoscope technology by developing active locomotion and telemetry. The size of such a capsule must not be larger than what a person can swallow without difficulty. One approach to increase functionality while still working within this size constraint is to build a modular robotic system in which the modules are swallowed one at a time, and the final assembly is performed inside the gastrointestinal (GI) tract. This paper addresses a fundamental challenge that must be be met for the success of such swallowable modular robots-their self-assembly. We propose to use magnets in a specific configuration on the mating faces of the modules. Our results show that high success rates can be achieved and snake-type robots can be self-assembled with compliant magnetic joints allowing them to adapt to highly irregular paths, such as the small intestine.