Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon the recently introduced formulation of probabilistic belief-dependent constraints. We present an anytime approach employing the Monte Carlo Tree Search (MCTS) method in continuous domains. Unlike previous approaches, our method assures safety anytime with respect to the currently expanded search tree without relying on the convergence of the search. We prove convergence in probability with an exponential rate of a version of our algorithms and study proposed techniques via extensive simulations. Even with a tiny number of tree queries, the best action found by our approach is much safer than the baseline. Moreover, our approach constantly finds better than the baseline action in terms of objective. This is because we revise the values and statistics maintained in the search tree and remove from them the contribution of the pruned actions.

In this paper we propose MA-DV2F: Multi-Agent Dynamic Velocity Vector Field. It is a framework for simultaneously controlling a group of vehicles in challenging environments. DV2F is generated for each vehicle independently and provides a map of reference orientation and speed that a vehicle must attain at any point on the navigation grid such that it safely reaches its target. The field is dynamically updated depending on the speed and proximity of the ego-vehicle to other agents. This dynamic adaptation of the velocity vector field allows prevention of imminent collisions. Experimental results show that MA-DV2F outperforms concurrent methods in terms of safety, computational efficiency and accuracy in reaching the target when scaling to a large number of vehicles. Project page for this work can be found here: https://yininghase.github.io/MA-DV2F/

To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive Planner (EHMPP), which employs an energy-saving optimization strategy. EHMPP focuses on refining the design of the motion planner to be seamlessly integrated with the existing automatic driving algorithms, without additional hardware. It has been validated through simulation experiments on the Prescan, CarSim, and Matlab platforms, demonstrating that it can increase passive recovery energy by 11.74\% and effectively track motor speed and acceleration at optimal power. To sum up, EHMPP not only aids in trajectory planning but also significantly boosts energy efficiency in autonomous EVs.

In this paper, how to efficiently find the optimal path in complex warehouse layout and make real-time decision is a key problem. This paper proposes a new method of Proximal Policy Optimization (PPO) and Dijkstra's algorithm, Proximal policy-Dijkstra (PP-D). PP-D method realizes efficient strategy learning and real-time decision making through PPO, and uses Dijkstra algorithm to plan the global optimal path, thus ensuring high navigation accuracy and significantly improving the efficiency of path planning. Specifically, PPO enables robots to quickly adapt and optimize action strategies in dynamic environments through its stable policy updating mechanism. Dijkstra's algorithm ensures global optimal path planning in static environment. Finally, through the comparison experiment and analysis of the proposed framework with the traditional algorithm, the results show that the PP-D method has significant advantages in improving the accuracy of navigation prediction and enhancing the robustness of the system. Especially in complex warehouse layout, PP-D method can find the optimal path more accurately and reduce collision and stagnation. This proves the reliability and effectiveness of the robot in the study of complex warehouse layout navigation algorithm.

In this paper, we present a novel RRT*-based strategy for generating kinodynamically feasible paths that satisfy temporal logic specifications. Our approach integrates a robustness metric for Linear Temporal Logics (LTL) with the system's motion constraints, ensuring that the resulting trajectories are both optimal and executable. We introduce a cost function that recursively computes the robustness of temporal logic specifications while penalizing time and control effort, striking a balance between path feasibility and logical correctness. We validate our approach with simulations and real-world experiments in complex environments, demonstrating its effectiveness in producing robust and practical motion plans. This work represents a significant step towards expanding the applicability of motion planning algorithms to more complex, real-world scenarios.

Dose prediction is an area of ongoing research that facilitates radiotherapy planning. Most commercial models utilise imaging data and intense computing resources. This study aimed to predict the dose-volume of rectum and bladder from volumes of target, at-risk structure organs and their overlap regions using machine learning. Dose-volume information of 94 patients with prostate cancer planned for 6000cGy in 20 fractions was exported from the treatment planning system as text files and mined to create a training dataset. Several statistical modelling, machine learning methods, and a new fuzzy rule-based prediction (FRBP) model were explored and validated on an independent dataset of 39 patients. The median absolute error was 2.0%-3.7% for bladder and 1.7-2.4% for rectum in the 4000-6420cGy range. For 5300cGy, 5600cGy and 6000cGy, the median difference was less than 2.5% for rectum and 3.8% for bladder. The FRBP model produced errors of 1.2%, 1.3%, 0.9% and 1.6%, 1.2%, 0.1% for the rectum and bladder respectively at these dose levels. These findings indicate feasibility of obtaining accurate predictions of the clinically important dose-volume parameters for rectum and bladder using just the volumes of these structures.

Advances in Autonomous Underwater Vehicles (AUVs) have evolved vastly in short period of time. While advancements in sonar and camera technology with deep learning aid the obstacle detection and path planning to a great extent, achieving the right balance between computational resources , precision and safety maintained remains a challenge. Finding optimal solutions for real-time navigation in cluttered environments becomes pivotal as systems have to process large amounts of data efficiently. In this work, we propose a novel obstacle avoidance method for navigating 3D underwater environments. This approach utilizes a standard multibeam forward-looking sonar to detect and map obstacle in 3D environment. Instead of using computationally expensive 3D sensors, we pivot the 2D sonar to get 3D heuristic data effectively transforming the sensor into a 2.5D sonar for real-time 3D navigation decisions. This approach enhances obstacle detection and navigation by leveraging the simplicity of 2D sonar with the depth perception typically associated with 3D systems. We have further incorporated Control Barrier Function (CBF) as a filter to ensure safety of the AUV. The effectiveness of this algorithm was tested on a six degrees of freedom (DOF) rover in various simulation scenarios. The results demonstrate that the system successfully avoids obstacles and navigates toward predefined goals, showcasing its capability to manage complex underwater environments with precision. This paper highlights the potential of 2.5D sonar for improving AUV navigation and offers insights into future enhancements and applications of this technology in underwater autonomous systems. \url{https://github.com/AIRLabIISc/EROAS}

Path planning in narrow passages is a challenging problem in various applications. Traditional planning algorithms often face challenges in complex environments like mazes and traps, where narrow entrances require special orientation control for successful navigation. In this work, we present a novel approach that combines superquadrics (SQ) representation and Voronoi diagrams to solve the narrow passage problem in both 2D and 3D environment. Our method utilizes the SQ formulation to expand obstacles, eliminating impassable passages, while Voronoi hyperplane ensures maximum clearance path. Additionally, the hyperplane provides a natural reference for robot orientation, aligning its long axis with the passage direction. We validate our framework through a 2D object retrieval task and 3D drone simulation, demonstrating that our approach outperforms classical planners and a cutting-edge drone planner by ensuring passable trajectories with maximum clearance.

This paper presents the development and evaluation of a medical service robot equipped with 3D LiDAR and advanced localization capabilities for use in hospital environments. The robot employs LiDAR-based Simultaneous Localization and Mapping SLAM to navigate autonomously and interact effectively within complex and dynamic healthcare settings. A comparative analysis with established 3D SLAM technology in Autoware version 1.14.0, under a Linux ROS framework, provided a benchmark for evaluating our system performance. The adaptation of Normal Distribution Transform NDT Matching to indoor navigation allowed for precise real-time mapping and enhanced obstacle avoidance capabilities. Empirical validation was conducted through manual maneuvers in various environments, supplemented by ROS simulations to test the system response to simulated challenges. The findings demonstrate that the robot integration of 3D LiDAR and NDT Matching significantly improves navigation accuracy and operational reliability in a healthcare context. This study highlights the robot ability to perform essential tasks with high efficiency and identifies potential areas for further improvement, particularly in sensor performance under diverse environmental conditions. The successful deployment of this technology in a hospital setting illustrates its potential to support medical staff and contribute to patient care, suggesting a promising direction for future research and development in healthcare robotics.

When humans move in a shared space, they choose navigation strategies that preserve their mutual safety. At the same time, each human seeks to minimise the number of modifications to her/his path. In order to achieve this result, humans use unwritten rules and reach a consensus on their decisions about the motion direction by exchanging non-verbal messages. They then implement their choice in a mutually acceptable way. Socially-aware navigation denotes a research effort aimed at replicating this logic inside robots. Existing results focus either on how robots can participate in negotiations with humans, or on how they can move in a socially acceptable way. We propose a holistic approach in which the two aspects are jointly considered. Specifically, we show that by combining opinion dynamics (to reach a consensus) with vortex fields (to generate socially acceptable trajectories), the result outperforms the application of the two techniques in isolation.