Most real-world Multi-Robot Task Allocation (MRTA) problems require fast and efficient decision-making, which is often achieved using heuristics-aided methods such as genetic algorithms, auction-based methods, and bipartite graph matching methods. These methods often assume a form that lends better explainability compared to an end-to-end (learnt) neural network based policy for MRTA. However, deriving suitable heuristics can be tedious, risky and in some cases impractical if problems are too complex. This raises the question: can these heuristics be learned? To this end, this paper particularly develops a Graph Reinforcement Learning (GRL) framework to learn the heuristics or incentives for a bipartite graph matching approach to MRTA. Specifically a Capsule Attention policy model is used to learn how to weight task/robot pairings (edges) in the bipartite graph that connects the set of tasks to the set of robots. The original capsule attention network architecture is fundamentally modified by adding encoding of robots’ state graph, and two Multihead Attention based decoders whose output are used to construct a LogNormal distribution matrix from which positive bigraph weights can be drawn. The performance of this new bigraph matching approach augmented with a GRL-derived incentive is found to be at par with the original bigraph matching approach that used expert-specified heuristics, with the former offering notable robustness benefits. During training, the learned incentive policy is found to get initially closer to the expert-specified incentive and then slightly deviate from its trend.

Efficient multi-robot task allocation (MRTA) is fundamental to various time-sensitive applications such as disaster response, warehouse operations, and construction. This paper tackles a particular class of these problems that we call MRTA-collective transport or MRTA-CT - here tasks present varying workloads and deadlines, and robots are subject to flight range, communication range, and payload constraints. For large instances of these problems involving 100s-1000's of tasks and 10s-100s of robots, traditional non-learning solvers are often time-inefficient, and emerging learning-based policies do not scale well to larger-sized problems without costly retraining. To address this gap, we use a recently proposed encoder-decoder graph neural network involving Capsule networks and multi-head attention mechanism, and innovatively add topological descriptors (TD) as new features to improve transferability to unseen problems of similar and larger size. Persistent homology is used to derive the TD, and proximal policy optimization is used to train our TD-augmented graph neural network. The resulting policy model compares favorably to state-of-the-art non-learning baselines while being much faster. The benefit of using TD is readily evident when scaling to test problems of size larger than those used in training.

Urban Air Mobility (UAM) promises a new dimension to decongested, safe, and fast travel in urban and suburban hubs. These UAM aircraft are conceived to operate from small airports called vertiports each comprising multiple take-offllanding and battery-recharging spots. Since they might be situated in dense urban areas and need to handle many aircraft landings and take-offs each hour, managing this schedule in real-time becomes challenging for a traditional air-traffic controller but instead calls for an automated solution. This paper provides a novel approach to this problem of Urban Air Mobility - Vertiport Schedule Management (UAM-VSM), which leverages graph reinforcement learning to generate decision-support policies. Here the designated physical spots within the vertiport's airspace and the vehicles being managed are represented as two separate graphs, with feature extraction performed through a graph convolutional network (GCN). Extracted features are passed onto perceptron layers to decide actions such as continue to hover or cruise, continue idling or take-off, or land on an allocated vertiport spot. Performance is measured based on delays, safety (no. of collisions) and battery consumption. Through realistic simulations in AirSim applied to scaled down multi-rotor vehicles, our results demonstrate the suitability of using graph reinforcement learning to solve the UAM-VSM problem and its superiority to basic reinforcement learning (with graph embed dings) or random choice baselines.

This paper presents a novel graph reinforcement learning (RL) architecture to solve multi-robot task allocation (MRTA) problems that involve tasks with deadlines and workload, and robot constraints such as work capacity. While drawing motivation from recent graph learning methods that learn to solve combinatorial optimization (CO) problems such as multi-Traveling Salesman and Vehicle Routing Problems using RL, this paper seeks to provide better performance (compared to non-learning methods) and important scalability (compared to existing learning architectures) for the stated class of MRTA problems. The proposed neural architecture, called Capsule Attention-based Mechanism or CapAM acts as the policy network, and includes three main components: 1) an encoder: a Capsule Network based node embedding model to represent each task as a learnable feature vector; 2) a decoder: an attention-based model to facilitate a sequential output; and 3) context: that encodes the states of the mission and the robots. To train the CapAM model, the policy-gradient method based on REINFORCE is used. When evaluated over unseen scenar-ios, CapAM demonstrates better task completion performance and > 10 times faster decision-making compared to standard non-learning based online MRTA methods. CapAM's advantage in generalizability, and scalability to test problems of size larger than those used in training, are also successfully demonstrated in comparison to a popular approach for learning to solve CO problems, namely the purely attention mechanism.

Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various dynamic Internet of Things (IoT) and robotics applications. However, single radio technology can hardly deliver optimal performance across all critical quality of service (QoS) dimensions under the typically varying environmental conditions or under varying distance between communicating nodes. Using a single wireless technology therefore falls short of meeting the demands of varying workloads or changing environmental conditions. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS , an Adaptive Radio and Transmission Power Selection system, which makes available at runtime multiple wireless technologies (e.g., WiFi and ZigBee) and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. The principal components of ARTPoS include new empirical models of power consumption and packet reception ratio (the latter can also be refined online) and online optimization schemes. We have implemented our system and evaluate it on the physical testbed consisting of our new embedded platforms with heterogeneous radios. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability, compared to standard baselines.

Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various Internet of Things (IoT) applications. However, none of the existing technologies delivers optimal performance across all critical quality of service (QoS) dimensions under varying environmental conditions. Using a single wireless technology therefore cannot meet the demands of varying workloads or changing environmental conditions. This problem is exacerbated with the increasing interest in placing embedded devices on the user's body or other mobile objects in mobile IoT applications. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS, an adaptive radio and transmission power selection system, which makes available multiple wireless technologies at runtime and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability.

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No abstract available.