In this letter, we present a volumetric mapping system that effectively calculates Occupancy Grid Maps (OGMs) and Euclidean Distance Transforms (EDTs) with parallel computing. Unlike these mappers for high-precision structural reconstruction, our system incrementally constructs global EDT and outputs high-frequency local distance information for online robot motion planning. The proposed system receives multiple types of sensor inputs and constructs OGM without down-sampling. Using GPU programming techniques, the system quickly computes EDT in parallel within local volume. The new observation is continuously integrated into the global EDT using the parallel wavefront algorithm while preserving the historical observations. Experiments with datasets have shown that our proposed approach outperforms existing state-of-the-art robot mapping systems and is particularly suitable for mapping unexplored areas. In its actual implementations on aerial and ground vehicles, the proposed system achieves real-time performance with limited onboard computational resources.

Delivery drones provide a promising sensing platform for smart cities thanks to their city-wide infrastructure and large-scale deployment. However, due to limited battery lifetime and available resources, it is challenging to schedule delivery drones to derive both high sensing and delivery performance, which is a highly complicated optimization problem with several coupled decision variables. Meanwhile, this complex optimization problem involves multiple interconnected decision variables, making it even more complex. In this paper, we first propose a delivery drone-based sensing system and formulate a mixed-integer non-linear programming problem (MINLP) that jointly optimizes the sensing utility and delivery time, considering practical factors including energy capacity and available delivery drones. Then we provide an efficient solution that integrates the strength of deep reinforcement learning (DRL) and heuristic, which decouples the highly complicated optimization search process and replaces the heavy computation with a rapid approximation. Evaluation results compared with the state-of-the-art baselines show that DDL improves the scheduling quality by at least 46% on average. More importantly, our proposed method could effectively improve the computational efficiency, which is up to 98 times higher than the best baseline.

Underwater image enhancement (UIE) is crucial for high-level vision in underwater robotics. While convolutional neural networks (CNNs) have made significant achievements in UIE, the locality of convolution poses a challenge in capturing the global context. In contrast, transformer-based networks, adept at handling long-range dependencies, have shown promise in various vision tasks. Nonetheless, directly applying a transformer to UIE faces critical challenges: 1) it tends to produce results with coarse details due to the negligence of local texture and 2) the varicolored degraded images require the network to be adaptable to different underwater environments. In this article, we propose a novel transformer-based network that can effectively leverage both the global contextual and local detailed information with some key designs (a global–local transformer [GL-Trans] block and a detail-enhanced skip connector [DESC]) while being computationally efficient. Moreover, by introducing a simple but effective learnable environment adaptor, the proposed network is flexible to deal with different underwater environments. Extensive experiments have been conducted and have demonstrated the superiority of our proposed network compared with other state-of-the-art (SOTA) methods both qualitatively and quantitatively.

Electric vehicles (EVs) spend the majority of their operational lifecycle in a state of non-use, which provides sufficient time and flexibility for charging. Utilizing this flexibility with vehicle-to-grid (V2G) technology not only supports grid stability but also offers additional revenue for vehicle owners. However, V2G scheduling based solely on maximizing the individual profits of EVs will lead to a high cost of peak demand charge. Moreover, concerns about range anxiety, battery degradation, and the computational burden of optimization have constrained the widespread of V2G technology.. To address these challenges, a coordinated model predictive control (CMPC)-based scheduling scheme is proposed while considering users’ range anxiety and battery degradation model. A mixed integer linear programming (MILP) is developed to consider range anxiety, battery degradation model, and constant current-constant voltage (CC-CV) charging mode. The CMPC method is proposed to avoid the penalty of peak demand charge by coordinating V2G charging among chargers and to mitigate sharp fluctuations in power by adding a penalty term in the power change rate. Instead of optimizing calculations for each time stage, a control package strategy and an online rolling optimization method are presented to reduce computational burden and consider stochastic charging demand arrivals. Simulations and hardware experiments are performed to demonstrate the effectiveness and feasibility of the proposed scheme.

This article studies the planning problem for a robot residing in partially unknown environments under signal temporal logic (STL) specifications, where most of the existing planning methods using STL rely on a fully known environment. In many practical scenarios, however, robots do not have prior information of all the obstacles. In this article, a novel two-phase planning method, i.e., offline exploration followed by online planning, is proposed to efficiently synthesize paths that satisfy STL tasks. In the offline exploration phase, a rapidly exploring random tree* (RRT*) is grown from task regions under the guidance of timed transducers, which guarantees that the resultant paths satisfy the task specifications. In the online phase, the path with minimum cost in RRT* is determined when an initial configuration is assigned. This path is then set as the reference of the time elastic band algorithm, which modifies the path until it has no collisions with obstacles. It is shown that the online computational burden is reduced, and collisions with unknown obstacles are avoided by using the proposed planning framework. The effectiveness and superiority of the proposed method are demonstrated in simulations and real-world experiments.