In this paper, we present algorithms to identify environmental hotspots using mobile sensors. We examine two approaches: one involving a single robot and another using multiple robots coordinated through a decentralized robot system. We introduce an adaptive algorithm that does not require precise knowledge of Gaussian Processes (GPs) hyperparameters, making the modeling process more flexible. The robots operate for a pre-defined time in the environment. The multi-robot system uses Voronoi partitioning to divide tasks and a Monte Carlo Tree Search for optimal path planning. Our tests on synthetic and a real-world dataset of Chlorophyll density from a Pacific Ocean sub-region suggest that accurate estimation of GP hyperparameters may not be essential for hotspot detection, potentially simplifying environmental monitoring tasks.

A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand. Active prostheses utilize real-valued, continuous sensor data to recognize patient target poses, or goals, and proactively move the artificial limb. Previous studies have examined how well the data collected in stationary poses, without considering the time steps, can help discriminate the goals. In this case study paper, we focus on using time series data from surface electromyography electrodes and kinematic sensors to sequentially recognize patients' goals. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system. Results from data collected in a virtual reality setting with ten subjects demonstrate the effectiveness of our proposed goal recognition approach, which achieves significantly better precision and recall than the state-of-the-art machine learning techniques and is less confident when wrong, which is beneficial when approximating smoother movements of prostheses.

Unmanned Aerial Vehicles (UAVs) have gained significant prominence in recent years for areas including surveillance, search, rescue, and package delivery. One key aspect in UAV operations shared across all these tasks is the autonomous path planning, which enables UAV to navigate through complex, unknown, and dynamic environments while avoiding obstacles without human control. Despite countless efforts having been devoted to this subject, new challenges are constantly arisen due to the persistent trade-off between performance and cost. And new studies are more urgently needed to develop autonomous system for UAVs with parsimonious sensor setup, which is a major need for wider adoptions. To this end, we propose an end-to-end autonomous framework to enable UAVs with only one single 2D-LiDAR sensor to operate in unknown dynamic environments. More specifically, we break our approach into three stages: a pre-processing Map Constructor; an offline Mission Planner; and an online reinforcement learning (RL)-based Dynamic Obstacle Handler. Experiments show that our approach provides robust and reliable dynamic path planning and obstacle avoidance with only 1/10 of the cost in sensor configuration. The code will be made public upon acceptance.

Automated Guided Vehicles (AGVs) are widely adopted in various industries due to their efficiency and adaptability. However, safely deploying AGVs in dynamic environments remains a significant challenge. This paper introduces an online trajectory optimization framework, the Fast Safe Rectangular Corridor (FSRC), designed for AGVs in obstacle-rich settings. The primary challenge is efficiently planning trajectories that prioritize safety and collision avoidance. To tackle this challenge, the FSRC algorithm constructs convex regions, represented as rectangular corridors, to address obstacle avoidance constraints within an optimal control problem. This conversion from non-convex to box constraints improves the collision avoidance efficiency and quality. Additionally, the Modified Visibility Graph algorithm speeds up path planning, and a boundary discretization strategy expedites FSRC construction. The framework also includes a dynamic obstacle avoidance strategy for real-time adaptability. Our framework's effectiveness and superiority have been demonstrated in experiments, particularly in computational efficiency (see Fig. \ref{fig:case1} and \ref{fig:case23}). Compared to state-of-the-art frameworks, our trajectory planning framework significantly enhances computational efficiency, ranging from 1 to 2 orders of magnitude (see Table \ref{tab:res}). Notably, the FSRC algorithm outperforms other safe convex corridor-based methods, substantially improving computational efficiency by 1 to 2 orders of magnitude (see Table \ref{tab:FRSC}).

AMR is widely used in factories to replace manual labor to reduce costs and improve efficiency. However, it is often difficult for logistics robots to plan the optimal trajectory and unreasonable trajectory planning can lead to low transport efficiency and high energy consumption. In this paper, we propose a method to directly calculate the optimal trajectory for short distance on the basis of the Dubins set, which completes the calculation of the Dubins path. Additionally, as an improvement of Dubins path, we smooth the Dubins path based on clothoid, which makes the curvature varies linearly. AMR can adjust the steering wheels while following this trajectory. The experiments show that the Dubins path can be calculated quickly and well smoothed.

The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The current implementation of the BTMGs for a specific task may not be robust to the changes in the environment and may not generalize well to different variations of tasks. We propose to extend the BTMG policy representation with a module that predicts BTMG parameters for a new task variation. To achieve this, we propose a model that combines a Gaussian process and a weighted support vector machine classifier. This model predicts the performance measure and the feasibility of the predicted policy with BTMG parameters and task variations as inputs. Using the outputs of the model, we then construct a surrogate reward function that is utilized within an optimizer to maximize the performance of a task over BTMG parameters for a fixed task variation. To demonstrate the effectiveness of our proposed approach, we conducted experimental evaluations on push and obstacle avoidance tasks in simulation and with a real KUKA iiwa robot. Furthermore, we compared the performance of our approach with four baseline methods.

The proliferation of unmanned vehicles offers many opportunities for solving environmental sampling tasks with applications in resource monitoring and precision agriculture. Informative path planning (IPP) includes a family of methods which offer improvements over traditional surveying techniques for suggesting locations for observation collection. In this work, we present a novel solution to the IPP problem by using a coregionalized Gaussian processes to estimate a dynamic scalar field that varies in space and time. Our method improves previous approaches by using a composite kernel accounting for spatiotemporal correlations and at the same time, can be readily incorporated in existing IPP algorithms. Through extensive simulations, we show that our novel modeling approach leads to more accurate estimations when compared with formerly proposed methods that do not account for the temporal dimension.

Sizing optimization of truss structures is a complex computational problem, and the reinforcement learning (RL) is suitable for dealing with multimodal problems without gradient computations. In this paper, a new efficient optimization algorithm called update Monte Carlo tree search (UMCTS) is developed to obtain the appropriate design for truss structures. UMCTS is an RL-based method that combines the novel update process and Monte Carlo tree search (MCTS) with the upper confidence bound (UCB). Update process means that in each round, the optimal cross-sectional area of each member is determined by search tree, and its initial state is the final state in the previous round. In the UMCTS algorithm, an accelerator for the number of selections for member area and iteration number is introduced to reduce the computation time. Moreover, for each state, the average reward is replaced by the best reward collected on the simulation process to determine the optimal solution. The proposed optimization method is examined on some benchmark problems of planar and spatial trusses with discrete sizing variables to demonstrate the efficiency and validity. It is shown that the computation time for the proposed approach is at least ten times faster than the branch and bound (BB) method. The numerical results indicate that the proposed method stably achieves better solution than other conventional methods.

Autonomous inspection tasks necessitate effective path-planning mechanisms to efficiently gather observations from points of interest (POI). However, localization errors commonly encountered in urban environments can introduce execution uncertainty, posing challenges to the successful completion of such tasks. To tackle these challenges, we present IRIS-under uncertainty (IRIS-U^2), an extension of the incremental random inspection-roadmap search (IRIS) algorithm, that addresses the offline planning problem via an A*-based approach, where the planning process occurs prior the online execution. The key insight behind IRIS-U^2 is transforming the computed localization uncertainty, obtained through Monte Carlo (MC) sampling, into a POI probability. IRIS-U^2 offers insights into the expected performance of the execution task by providing confidence intervals (CI) for the expected coverage, expected path length, and collision probability, which becomes progressively tighter as the number of MC samples increase. The efficacy of IRIS-U^2 is demonstrated through a case study focusing on structural inspections of bridges. Our approach exhibits improved expected coverage, reduced collision probability, and yields increasingly-precise CIs as the number of MC samples grows. Furthermore, we emphasize the potential advantages of computing bounded sub-optimal solutions to reduce computation time while still maintaining the same CI boundaries.

Generating safe and non-conservative behaviors in dense, dynamic environments remains challenging for automated vehicles due to the stochastic nature of traffic participants' behaviors and their implicit interaction with the ego vehicle. This paper presents a novel planning framework, Multipolicy And Risk-aware Contingency planning (MARC), that systematically addresses these challenges by enhancing the multipolicy-based pipelines from both behavior and motion planning aspects. Specifically, MARC realizes a critical scenario set that reflects multiple possible futures conditioned on each semantic-level ego policy. Then, the generated policy-conditioned scenarios are further formulated into a tree-structured representation with a dynamic branchpoint based on the scene-level divergence. Moreover, to generate diverse driving maneuvers, we introduce risk-aware contingency planning, a bi-level optimization algorithm that simultaneously considers multiple future scenarios and user-defined risk tolerance levels. Owing to the more unified combination of behavior and motion planning layers, our framework achieves efficient decision-making and human-like driving maneuvers. Comprehensive experimental results demonstrate superior performance to other strong baselines in various environments.