Obstacle avoidance path planning for uncrewed aerial vehicles (UAVs), or drones, is rarely addressed in most flight path planning schemes, despite obstacles being a realistic condition. Obstacle avoidance can also be energy-intensive, making it a critical factor in efficient point-to-point drone flights. To address these gaps, we propose EcoFlight, an energy-efficient pathfinding algorithm that determines the lowest-energy route in 3D space with obstacles. The algorithm models energy consumption based on the drone propulsion system and flight dynamics. We conduct extensive evaluations, comparing EcoFlight with direct-flight and shortest-distance schemes. The simulation results across various obstacle densities show that EcoFlight consistently finds paths with lower energy consumption than comparable algorithms, particularly in high-density environments. We also demonstrate that a suitable flying speed can further enhance energy savings.

--Navigation of UA Vs in unknown environments with obstacles is essential for applications in disaster response and infrastructure monitoring. However, existing obstacle avoidance algorithms such as Artificial Potential Field (APF) are unable to generalize across environments with different obstacle configurations. Furthermore, the precise location of the final target may not be available in applications such search and rescue, in which case approaches such as RF source seeking can be used to align towards the target location. This paper proposes a real-time trajectory planning method, which involves real time adaptation of APF through a sampling-based approach. The proposed approach utilizes only the bearing angle of the target without its precise location, and adjusts the potential field parameters according to the environment with new obstacle configurations in real time. The main contributions of the article are i) RF source seeking algorithm to provide a bearing angle estimate using RF signal calculations based on antenna placement, and ii) modified APF for adaptable collision avoidance in changing environments, which are evaluated separately in the simulation software Gazebo, using ROS2 for communication. Simulation results show that the RF source-seeking algorithm achieves high accuracy, with an average angular error of just 1.48 degrees, and with this estimate, the proposed navigation algorithm improves the success rate of reaching the target by 46% and reduces the trajectory length by 1.2% compared to standard potential fields. The increasing use of drones in various applications has been facilitated by advancements in sensor technology, enabling better localization and obstacle detection methods. These technologies allow drones to effectively navigate through complex environments, avoiding obstacles in real time. The demand for autonomous drone navigation is growing in sectors like search and rescue [1], inspection of unknown areas [2], and other critical applications requiring drones to operate in unfamiliar and potentially hazardous environments. In these scenarios, drones must autonomously identify and locate targets, update environmental maps in real time, detect obstacles, and plan safe trajectories. The variability of these environments, such as changes in obstacle sizes, distances, and spatial constraints, poses a significant challenge to creating a unified navigation system that can adapt to such differing conditions.

In autonomous navigation, trajectory replanning, refinement, and control command generation are essential for effective motion planning. This paper presents a resilient approach to trajectory replanning addressing scenarios where the initial planner's solution becomes infeasible. The proposed method incorporates a hybrid A* algorithm to generate feasible trajectories when the primary planner fails and applies a soft constraints-based smoothing technique to refine these trajectories, ensuring continuity, obstacle avoidance, and kinematic feasibility. Obstacle constraints are modelled using a dynamic Voronoi map to improve navigation through narrow passages. This approach enhances the consistency of trajectory planning, speeds up convergence, and meets real-time computational requirements. In environments with around 30\% or higher obstacle density, the ratio of free space before and after placing new obstacles, the Resilient Timed Elastic Band (RTEB) planner achieves approximately 20\% reduction in traverse distance, traverse time, and control effort compared to the Timed Elastic Band (TEB) planner and Nonlinear Model Predictive Control (NMPC) planner. These improvements demonstrate the RTEB planner's potential for application in field robotics, particularly in agricultural and industrial environments, where navigating unstructured terrain is crucial for ensuring efficiency and operational resilience.

Search and rescue environments exhibit challenging 3D geometry (e.g., confined spaces, rubble, and breakdown), which necessitates agile and maneuverable aerial robotic systems. Because these systems are size, weight, and power (SWaP) constrained, rapid navigation is essential for maximizing environment coverage. Onboard autonomy must be robust to prevent collisions, which may endanger rescuers and victims. Prior works have developed high-speed navigation solutions for autonomous aerial systems, but few have considered safety for search and rescue applications. These works have also not demonstrated their approaches in diverse environments. We bridge this gap in the state of the art by developing a reactive planner using forward-arc motion primitives, which leverages a history of RGB-D observations to safely maneuver in close proximity to obstacles. At every planning round, a safe stopping action is scheduled, which is executed if no feasible motion plan is found at the next planning round. The approach is evaluated in thousands of simulations and deployed in diverse environments, including caves and forests. The results demonstrate a 24% increase in success rate compared to state-of-the-art approaches.

Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV navigation face challenges in generalization, particularly when applied to unseen complex environments. To address these limitations, we propose a Dual-Transformer Encoder-based Proximal Policy Optimization (DTPPO) method. DTPPO enhances multi-UAV collaboration through a Spatial Transformer, which models inter-agent dynamics, and a Temporal Transformer, which captures temporal dependencies to improve generalization across diverse environments. This architecture allows UAVs to navigate new, unseen environments without retraining. Extensive simulations demonstrate that DTPPO outperforms current MADRL methods in terms of transferability, obstacle avoidance, and navigation efficiency across environments with varying obstacle densities. The results confirm DTPPO's effectiveness as a robust solution for multi-UAV navigation in both known and unseen scenarios.

Recent advances have improved autonomous navigation and mapping under payload constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones due to their need for heavy sensors and high computational resources. To address these challenges, we introduce ExploreBug, a novel hybrid frontier range bug algorithm designed to handle limited sensing capabilities for a swarm of nano-drones. This system includes three primary components: a mapping subsystem, an exploration subsystem, and a navigation subsystem. Additionally, an intra-swarm collision avoidance system is integrated to prevent collisions between drones. We validate the efficacy of our approach through extensive simulations and real-world exploration experiments involving up to seven drones in simulations and three in real-world settings, across various obstacle configurations and with a maximum navigation speed of 0.75 m/s. Our tests demonstrate that the algorithm efficiently completes exploration tasks, even with minimal sensing, across different swarm sizes and obstacle densities. Furthermore, our frontier allocation heuristic ensures an equal distribution of explored areas and paths traveled by each drone in the swarm. We publicly release the source code of the proposed system to foster further developments in mapping and exploration using autonomous nano drones.

This paper introduces an end-to-end trajectory planning algorithm tailored for multi-UAV systems that generates collision-free trajectories in environments populated with both static and dynamic obstacles, leveraging point cloud data. Our approach consists of a 2-fork neural network fed with sensing and localization data, able to communicate intermediate learned features among the agents. One network branch crafts an initial collision-free trajectory estimate, while the other devises a neural collision constraint for subsequent optimization, ensuring trajectory continuity and adherence to physicalactuation limits. Extensive simulations in challenging cluttered environments, involving up to 25 robots and 25% obstacle density, show a collision avoidance success rate in the range of 100 -- 85%. Finally, we introduce a saliency map computation method acting on the point cloud data, offering qualitative insights into our methodology.

Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.

A wide range of real-world applications can be formulated as Multi-Agent Path Finding (MAPF) problem, where the goal is to find collision-free paths for multiple agents with individual start and goal locations. State-of-the-art MAPF solvers are mainly centralized and depend on global information, which limits their scalability and flexibility regarding changes or new maps that would require expensive replanning. Multi-agent reinforcement learning (MARL) offers an alternative way by learning decentralized policies that can generalize over a variety of maps. While there exist some prior works that attempt to connect both areas, the proposed techniques are heavily engineered and very complex due to the integration of many mechanisms that limit generality and are expensive to use. We argue that much simpler and general approaches are needed to bring the areas of MARL and MAPF closer together with significantly lower costs. In this paper, we propose Confidence-based Auto-Curriculum for Team Update Stability (CACTUS) as a lightweight MARL approach to MAPF. CACTUS defines a simple reverse curriculum scheme, where the goal of each agent is randomly placed within an allocation radius around the agent's start location. The allocation radius increases gradually as all agents improve, which is assessed by a confidence-based measure. We evaluate CACTUS in various maps of different sizes, obstacle densities, and numbers of agents. Our experiments demonstrate better performance and generalization capabilities than state-of-the-art MARL approaches with less than 600,000 trainable parameters, which is less than 5% of the neural network size of current MARL approaches to MAPF.

In this paper, we present a method of multi-robot motion planning by biasing centralized, sampling-based tree search with decentralized, data-driven steer and distance heuristics. Over a range of robot and obstacle densities, we evaluate the plain Rapidly-expanding Random Trees (RRT), and variants of our method for double integrator dynamics. We show that whereas plain RRT fails in every instance to plan for $4$ robots, our method can plan for up to 16 robots, corresponding to searching through a very large 65-dimensional space, which validates the effectiveness of data-driven heuristics at combating exponential search space growth. We also find that the heuristic information is complementary; using both heuristics produces search trees with lower failure rates, nodes, and path costs when compared to using each in isolation. These results illustrate the effective decomposition of high-dimensional joint-space motion planning problems into local problems.