Mapping and exploration of a Martian terrain with an aerial vehicle has become an emerging research direction, since the successful flight demonstration of the Mars helicopter Ingenuity. Although the autonomy and navigation capability of the state of the art Mars helicopter has proven to be efficient in an open environment, the next area of interest for exploration on Mars are caves or ancient lava tube like environments, especially towards the never-ending search of life on other planets. This article presents an autonomous exploration mission based on a modified frontier approach along with a risk aware planning and integrated collision avoidance scheme with a special focus on energy aspects of a custom designed Mars Coaxial Quadrotor (MCQ) in a Martian simulated lava tube. One of the biggest novelties of the article stems from addressing the exploration capability, while rapidly exploring in local areas and intelligently global re-positioning of the MCQ when reaching dead ends in order to efficiently use the battery based consumed energy, while increasing the volume of the exploration. The proposed novel algorithm for the Martian exploration is able to select the next way point of interest, such that the MCQ keeps its heading towards the local exploration direction where it will find maximum information about the surroundings. The proposed three layer cost based global re-position point selection assists in rapidly redirecting the MCQ to previously partially seen areas that could lead to more unexplored part of the lava tube. The Martian fully simulated mission presented in this article takes into consideration the fidelity of physics of Mars condition in terms of thin atmosphere, low surface pressure and low gravity of the planet, while proves the efficiency of the proposed scheme in exploring an area that is particularly challenging due to the subterranean-like environment. The proposed exploration-planning framework is also validated in simulation by comparing it against the graph based exploration planner. Intensive simulations with true Mars conditions are carried out in order to validate and benchmark our approach in a utmost realistic Mars lava tube exploration scenario using a Mars Coaxial Quadrotor.

Mars has been a prime candidate for planetary explo-ration of the solar system because of the science discoveries that support chances of future habitation on this planet. The Mars exploration landers and rovers have laid the foundation of our understanding of the planet's atmosphere and terrain. However, the rovers have presented limitations in terms of their pace, travers ability, and exploration capabilities from the ground and thus, one of the main field of interest for future robotic mission to Mars is to enhance the autonomy of this exploration vehicles. Martian caves and lava tubes like terrains, which consists of uneven ground, poor visibility and confined space, makes it impossible for wheel based rovers to navigate through these areas. In order to address these limitations and advance the exploration capability in a Martian terrain, this article presents the design and control of a novel coaxial quadrotor Micro Aerial Vehicle (MAV). As it will be presented, the key contributions on the design and control architecture of the proposed Mars coaxial quadrotor, are introducing an alternative and more enhanced, from a control point of view concept, when compared in terms of autonomy to Ingenuity. Based on the presented design, the article will introduce the mathematical modelling and automatic control framework of the vehicle that will consist of a linearised model of a co-axial quadrotor and a corresponding Model Pre-dictive Controller (MPC) for the trajectory tracking. Among the many models, proposed for the aerial flight on Mars, a reliable control architecture lacks in the related state of the art. The MPC based closed loop responses of the proposed MAV will be verified in different conditions during the flight with additional disturbances, induced to replicate a real flight scenario. For the model validation purpose, the Mars coaxial quadrotor is sim-ulated inside a Martian environment with related atmospheric conditions in the Gazebo simulator, which will use the proposed MPC controller for following an a priory defined trajectory. In order to further validate the proposed control architecture and prove the efficacy of the suggested design, the introduced Mars coaxial quadrotor and the MPC scheme will be compared to a PID-type controller, similar to the Ingenuity helicopter's control architecture for the position and the heading.

Autonomous navigation capability is a crucial part for deploying robots in an unknown environment. In this article a reactive local planner for autonomous and safe navigation in subterranean environment is presented. The proposed planning framework navigates the MAV forward in a tunnel such that the MAV gains more information about the environment while avoiding obstacles. The proposed planning architecture works solely based on the information of local surrounding of the MAV thus, making navigation simple yet fast. One of the biggest novelties of the article comes from solving the combined problem of autonomous navigation and obstacle avoidance. The proposed algorithm for selecting the next way point of interest also accounts in the safety margin for traversing to such way point. The approach presented in this article is also different from classical map based global planning algorithms because it favours the next way point away from obstacles in way point selection process and thus providing a safe path for incremental forward navigation. The approach is validated by simulating a MAV equipped with the proposed reactive local planner in order for the MAV to navigate in a subterranean cave environment.

Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present a complete framework for deploying Micro Aerial Vehicles (MAVs) in autonomous exploration missions in unknown subterranean areas. The main motive of exploration algorithms is to depict the next best frontier for the MAV such that new ground can be covered in a fast, safe yet efficient manner. The proposed framework uses a novel frontier selection method that also contributes to the safe navigation of autonomous MAVs in obstructed areas such as subterranean caves, mines, and urban areas. The framework presented in this work bifurcates the exploration problem in local and global exploration. The proposed exploration framework is also adaptable according to computational resources available onboard the MAV which means the trade-off between the speed of exploration and the quality of the map can be made. Such capability allows the proposed framework to be deployed in subterranean exploration and mapping as well as in fast search and rescue scenarios. The performance of the proposed framework is evaluated in detailed simulation studies with comparisons made against a high-level exploration-planning framework developed for the DARPA Sub-T challenge as it will be presented in this article.

Exploration and safe navigation in previously unknown GPS-denied obstructed areas are major challenges for autonomous robots when deployed in subterranean environments. In response, this work establishes an Exploration-Planning framework developed for the real-world deployment of Micro Aerial Vehicles (MAVs) in subterranean exploration missions. The fundamental task for an autonomous MAV to navigate in an unknown area, is to decide where to look while navigating such that the MAV will acquire more information about the surrounding. The work presented in this article focuses around 3D exploration of large-scale caves or multi-branched tunnel like structures, while still prioritizing the Look-Ahead and Move-Forward approach for fast navigation in previously unknown areas. In order to achieve such exploration behaviour, the proposed work utilizes a two-layer navigation approach. The first layer deals with computing traversable frontiers to generate the look ahead poses in the constrained field of view, aligned with the MAV’s heading vector that leads to rapid continuous exploration. The proposed frontier distribution based switching goal selection approach allows the MAV to explore various terrains, while still regulating the MAV’s heading vector. The second layer of the proposed scheme deals with global cost based navigation of the MAV to the potential junction in a multi-branched tunnel system leading to a continuous exploration of partially seen areas. The proposed framework is a combination of a novel frontier goal selection approach, risk-aware expandable grid based path planning, nonlinear model predictive control and artificial potential fields based on local reactive navigation, obstacle avoidance, and control for the autonomous deployment of MAVs in extreme environments.

Subterranean exploration and mapping for search and rescue robotics have become an emerging research direction since the DARPA organized Subterranean Challenge. As part of development efforts within the team CoSTAR (Collaborative SubTerranean Autonomous Robots) in the Sub-T challenge, this work establishes a novel traversable graph-based exploration strategy that utilizes frontiers for local navigation and a fast collision risk-aware graph building for global navigation. The exploration strategy extracts frontiers in an unknown area that contribute to safe navigation while maximizing information gain for the robot. The exploration problem is further bifurcated into local and global exploration for faster decision-making at junctions with the goal of rapidly exploring the area. The local exploration guarantees collision-free straight-line paths to informative frontiers for rapid forward navigation, while global re-positioning utilizes a traversable graph subject to geometrical collision checks within the occupancy map. The pathfinding in a graph is addressed using a heuristic, which combines risk margins and travel costs to assist in short yet safe paths to the global frontier in case of a dead end in local exploration. The presented exploration strategy is developed with the goal of making exploration algorithms platform agnostic in order to be able to use it with aerial, as well as ground robots. The proposed method is also evaluated against different state-of-the-art exploration planners in simulated fixed-time budget-based exploration missions on an Unmanned Aerial Vehicle (UAV) in order to benchmark the capabilities and highlight the novelty.