Efficient exploration of large-scale environments remains a critical challenge in robotics, with applications ranging from environmental monitoring to search and rescue operations. This article proposes Frontier Shepherding (FroShe), a bio-inspired multi-robot framework for large-scale exploration. The framework heuristically models frontier exploration based on the shepherding behavior of herding dogs, where frontiers are treated as a swarm of sheep reacting to robots modeled as shepherding dogs. FroShe is robust across varying environment sizes and obstacle densities, requiring minimal parameter tuning for deployment across multiple agents. Simulation results demonstrate that the proposed method performs consistently, regardless of environment complexity, and outperforms state-of-the-art exploration strategies by an average of 20% with three UAVs. The approach was further validated in real-world experiments using single-and dual-drone deployments in a forest-like environment.

Quadcopter attitude control involves two tasks: smooth attitude tracking and aggressive stabilization from arbitrary states. Although both can be formulated as tracking problems, their distinct state spaces and control strategies complicate a unified reward function. We propose a multitask deep reinforcement learning framework that leverages parallel simulation with IsaacGym and a Graph Convolutional Network (GCN) policy to address both tasks effectively. Our multitask Soft Actor-Critic (SAC) approach achieves faster, more reliable learning and higher sample efficiency than single-task methods. We validate its real-world applicability by deploying the learned policy—a compact two-layer network with 24 neurons per layer—on a Pixhawk flight controller, achieving 400 Hz control without extra computational resources. We provide our code at https://github.com/ robot-perception-group/GraphMTSAC_UAV/.

Multimodal Unmanned Aerial Vehicles (UAVs), capable of operating in different locomotion modes, offer greater versatility and optimized energy usage. This paper presents a novel trimodal UAV that integrates ground locomotion, hovering, and fixed-wing flight using a shared actuator system. The design features a quadcopter frame with modular components, including passive wheels for ground mobility and fixed wings for forward flight. A control system for ground locomotion was implemented within the ArduPilot framework, enabling autonomous waypoint navigation across all modes. The prototype was extensively tested, with a comprehensive energy efficiency evaluation conducted through wind tunnel experiments and flight trials. In forward flight, the vehicle’s range increased, although its endurance decreased. Ground mode saw significant gains in both. Wing incidence tuning in hover improved endurance and range but reduced controllability. Additionally, the vehicle was shown to be capable of climbing inclined surfaces, such as walls.

The use of Micro Aerial Vehicles (MAVs) for inspection and surveillance missions has proved to be extremely useful, however, their usability is negatively impacted by the large power requirements and the limited operating time. This work describes the design and development of a novel hybrid aerial-ground vehicle, enabling multi-modal mobility and long operating time, suitable for long-endurance inspection and monitoring applications. The design consists of a MAV with two tiltable axles and four independent passive wheels, allowing it to fly, approach, land and move on flat and inclined surfaces, while using the same set of actuators for all modes of locomotion. In comparison to existing multi-modal designs with passive wheels, the proposed design enables a higher ground locomotion efficiency, provides a higher payload capacity, and presents one of the lowest mass increases due to the ground actuation mechanism. The vehicle's performance is evaluated through a series of real experiments, demonstrating its flying, ground locomotion and wall-climbing capabilities, and the energy consumption for all modes of locomotion is evaluated.

Employing small size aerial robots, acting as mobile airborne sensors, to work alongside ground robots can be extremely useful in many different robotic missions. Due to the strict constraints in terms of size, weight, 3D coverage, processing power and power consumption. There are not many technological possibilities for performing independent self-localization for such tiny robots. This paper describes a cooperative audio-visual localization system to robustly estimate the position of a small aerial robot from a ground robot. Experimental results with a 40-gram quadrotor assess the performance of the system and demonstrate the reliability gained through fusion of sound measurements with visual information.

Teams of fixed wing micro-aerial vehicles (MAVs) could provide a wide area coverage and relay data in wireless ad-hoc networks. In such applications fixed wing MAVs have to be able to regulate an inter-robot distance. Fixed wing MAVs have reduced maneuverability, that is, they cannot perform sharp turns or hover on the spot. This kinematic property presents the main challenge to design a formation algorithm that will regulate inter-MAV distance and cover the desired area. In this paper we present a distributed control strategy that is based on attraction and repulsion between MAVs and relies only on local information. We show in simulation and in field experiments with a team of fixed wing MAVs that using our strategy MAVs can cover an area by creating an equilateral triangular lattice and regulate communication link quality between neighboring MAVs.

Localization is one of the key challenges that needs to be considered beforehand to design truly autonomous MAV teams. In this paper, we present a cooperative method to address the localization problem for a team of MAVs, where individuals obtain their position through perceiving a sound-emitting beacon MAV that is flying relative to a reference point in the environment. For this purpose, an on-board audio-based localization system is proposed that allows individuals to measure the relative bearing to the beacon robot and furthermore to localize themselves and the beacon robot simultaneously, without the need for a communication network. Our method is based on coherence testing among signals of a small on-board microphone array, to obtain the relative bearing measurements, and an estimator, to fuse these measurements with sensory information about the motion of the robot throughout time, to estimate robustly the MAV positions. The proposed method is evaluated both in simulation and in real world experiments.

In search and rescue missions, Micro Air Vehicles (MAV's) can assist rescuers to faster locate victims inside a large search area and to coordinate their efforts. Acoustic signals play an important role in outdoor rescue operations. Emergency whistles, as found on most aircraft life vests, are commonly carried by people engaging in outdoor activities, and are also used by rescue teams, as they allow to signal reliably over long distances and far beyond visibility. For a MAV involved in such missions, the ability to locate the source of a distress sound signal, such as an emergency whistle blown by a person in need of help, is therefore significantly important and would allow the localization of victims and rescuers during night time, through foliage and in adverse conditions such as dust, fog and smoke. In this paper we present a sound source localization system for a MAV to locate narrowband sound sources on the ground, such as the sound of a whistle or personal alarm siren. We propose a method based on a particle filter to combine information from the cross correlation between signals of four spatially separated microphones mounted on the MAV, the dynamics of the aerial platform, and the doppler shift in frequency of the sound due to the motion of the MAV. Furthermore, we evaluate our proposed method in a real world experiment where a flying micro air vehicle is used to locate and track the position of a narrowband sound source on the ground.