Mobile micro-robots ready to use: Alice

Distributed Autonomous Robotic Systems 7

We discuss system identification of self-organizing, swarm robotic systems using a "gray-box" approach, based on probabilistic macroscopic models. Using a well known case study concerned with the autonomous inspection of a regular structure by a swarm of miniature robots, we show how to achieve highly accurate predictive models by combining previously developed probabilistic modeling and calibration methods, with parameter optimization based on experimental data (80 experiments involving 5-20 real robots). Key properties of the optimization process are outlined with the help of a simple scenario and a model that can be solved analytically. Concepts are then validated numerically for the more complex, non-linear inspection scenario.

Section: Identification Of a Non-linear Swarm-robotic Systemmentioning

confidence: 99%

System Identification of Self-Organizing Robotic Swarms

Distributed Autonomous Robotic Systems 7

“…The Alice II robot [4] is endowed with a PIC micro controller (368 bytes RAM, 8Kb FLASH), has a length of 22mm, and a maximal speed of 4cm/s (Figure 1, right). Four IR modules can Figure 1: Left: Close-up on an Alice robot equipped with radio module and camera in the real setup.…”

Section: Turbine Mock-up Environment and Robotic Platformmentioning

confidence: 99%

Distributed boundary coverage with a team of networked miniature robots using a robust market-based algorithm

Amstutz

2008

Ann Math Artif Intell

We study distributed boundary coverage of known environments using a team of miniature robots. Distributed boundary coverage is an instance of the multi-robot task-allocation problem and has applications in inspection, cleaning, and painting among others. The proposed algorithm is robust to sensor and actuator noise, failure of individual robots, and communication loss. We use a market-based algorithm with known lower bounds on the performance to allocate the environmental objects of interest among the team of robots. The coverage time for systems subject to sensor and actuator noise is significantly shortened by on-line task re-allocation. The complexity and convergence properties of the algorithm are formally analyzed. The system performance is systematically analyzed at two different microscopic modeling levels, using agent-based, discrete-event and module-based, realistic simulators. Finally, results obtained in simulation are validated using a team of Alice miniature robots involved in a distributed inspection case study.

“…Stator blades can be distinguished from rotor blades as their curvature is concave whereas the curvature of a rotor blade is convex when looking at the edge following the round tip (considering coverage of the boundary in clockwise direction), compare Figure 2. The Alice robot (Figure 1, [5]) has a size of 2cm×2cm×2cm, a differential wheel drive with reaching speed of up to 4 cm s , and four infrared distance sensors for obstacle detection (up to 3cm), and 4Bit/s local communication up to 6cm. It is endowed with a PIC micro controller with 368bytes of RAM.…”

Section: Methodsmentioning

confidence: 99%

“…In [7] instead we use no planning, but coordination is fully decentralized and reactive, enabling execution by minimalist robots with crude sensors and limited localization capabilities. While [17] extends the algorithm of [10] to work in dynamic environments with distributed path re-planning, we raise the level of coordination of our minimalist approach: we implement and compare two algorithms on the Alice platform [5] that rely on orthogonal paradigms, deliberative planning with minimal reactive parts [2] and self-organization [3]. Whereas in the deliberative approach robots plan their trajectories based on an algorithm leading to provably complete coverage, in the self-organized approach robots follow simple heuristics that govern their behavior upon interaction with other robots or the environment.…”

Section: Introductionmentioning

confidence: 99%

Comparing Coordination Schemes for Miniature Robotic Swarms: A Case Study in Boundary Coverage of Regular Structures

Rutishauser

Experimental Robotics

We consider boundary coverage of a regular structure by a swarm of miniature robots, and compare a suite of three fully distributed coordination algorithms experimentally. All algorithms rely on boundary coverage by reactive control, whereas coordination of the robots high-level behavior is fundamentally different: random, self-organized, and deliberative with reactive elements.The self-organized coordination algorithm was designed using macroscopic probabilistic models that lead to analytical expressions for the algorithm's mean performance. We contrast this approach with a provably complete, near optimal coverage algorithm, which is due to its assumption (noise-less sensors and actuators) infeasible on a real miniature robotic platform, but is considered to yield best-possible policies for an individual robot.Experimental results with swarms of up to 30 robots show that selforganization significantly improves coverage performance with increasing swarm size. We also observe that enforcing a provably complete policy on a miniature robot with limited hardware capabilities is highly sub-optimal as there is a trade-off between coverage throughput and time spent for localization and navigation.