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Register a new userDistributed Cohesive Control for Robot Swarms: Maintaining Good Connectivity in the Presence of Exterior Forces
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Added by
Michael Hemmer
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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We present a number of powerful local mechanisms for maintaining a dynamic swarm of robots with limited capabilities and information, in the presence of external forces and permanent node failures. We propose a set of local continuous algorithms that together produce a generalization of a Euclidean Steiner tree. At any stage, the resulting overall shape achieves a good compromise between local thickness, global connectivity, and flexibility to further continuous motion of the terminals. The resulting swarm behavior scales well, is robust against node failures, and performs close to the best known approximation bound for a corresponding centralized static optimization problem.
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Robot swarms to date are not prepared for autonomous navigation such as path planning and obstacle detection in forest floor, unable to achieve low-cost. The development of depth sensing and embedded computing hardware paves the way for swarm of terrestrial robots. The goal of this research is to improve this situation by developing low cost vision system for small ground robots to rapidly perceive terrain. We develop two depth estimation models and evaluate their performance on Raspberry Pi 4 and Jetson Nano in terms of accuracy, runtime and model size of depth estimation models, as well as memory consumption, power draw, temperature, and cost of above two embedded on-board computers. Our research demonstrated that auto-encoder network deployed on Raspberry Pi 4 runs at a power consumption of 3.4 W, memory consumption of about 200 MB, and mean runtime of 13 ms. This can be to meet our requirement for low-cost swarm of robots. Moreover, our analysis also indicated multi-scale deep network performs better for predicting depth map from blurred RGB images caused by camera motion. This paper mainly describes depth estimation models trained on our own dataset recorded in forest, and their performance on embedded on-board computers.
In this paper, we develop a distributed monitoring framework for robot swarms so that the agents can monitor whether the executions of robot swarms satisfy Swarm Signal Temporal Logic (SwarmSTL) formulas. We define generalized moments (GMs) to represent swarm features. A dynamic generalized moments consensus algorithm (GMCA) with Kalman filter (KF) is proposed so that each agent can estimate the GMs. Also, we obtain an upper bound for the error between an agents estimate and the actual GMs. This bound is independent of the motion of the agents. We also propose rules for monitoring SwarmSTL temporal and logical operators. As a result, the agents can monitor whether the swarm satisfies SwarmSTL formulas with a certain confidence level using these rules and the bound of the estimation error. The distributed monitoring framework is applied to a swarm transporting supplies example, where we also show the efficacy of the Kalman filter in the dynamic generalized moments consensus process.
In this paper, we present algorithms for synthesizing controllers to distribute a group (possibly swarms) of homogeneous robots (agents) over heterogeneous tasks which are operated in parallel. We present algorithms as well as analysis for global and local-feedback-based controller for the swarms. Using ergodicity property of irreducible Markov chains, we design a controller for global swarm control. Furthermore, to provide some degree of autonomy to the agents, we augment this global controller by a local feedback-based controller using Language measure theory. We provide analysis of the proposed algorithms to show their correctness. Numerical experiments are shown to illustrate the performance of the proposed algorithms.
Mobile microrobots are envisioned to be useful in a wide range of high-impact applications, many of which requiring cohesive group formation to maintain self-bounded swarms in the absence of confining boundaries. Cohesive group formation relies on a balance between attractive and repulsive interactions between agents. We found that a balance of magnetic dipolar attraction and multipolar repulsion between self-assembled particle chain microrobots enable their self-organization into cohesive clusters. Self-organized microrobotic clusters translate above a solid substrate via a hydrodynamic self-propulsion mechanism. Cluster velocity increases with cluster size, resulting from collective hydrodynamic effects. Clustering is promoted by the strength of cohesive interactions and hindered by heterogeneities of individual microrobots. Scalability of cohesive interactions allows formation of larger groups, whose internal spatiotemporal organization undergoes a transition from solid-like ordering to liquid-like behavior with increasing cluster size. Our work elucidates the dynamics of clustering under cohesive interactions, and presents an approach for addressing operation of microrobots as localized teams.
Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. To apply behaviour adaptation in swarm robotic systems, we propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. We set up foraging experiments with a variety of disturbances: injected faults to proximity sensors, ground sensors, and the actuators of individual robots, with 100 unique combinations for each type. We also investigate disturbances in the operating environment of the swarm, where the swarm has to adapt to drastic changes in the number of resources available in the environment, and to one of the robots behaving disruptively towards the rest of the swarm, with 30 unique conditions for each such perturbation. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within at most 30 evaluations.
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