RELAX: Reinforcement Learning Enabled 2D-LiDAR Autonomous System for Parsimonious UAVs

Unmanned Aerial Vehicles (UAVs) have become increasingly prominence in recent years, finding applications in surveillance, package delivery, among many others. Despite considerable efforts in developing algorithms that enable UAVs to navigate through complex unknown environments autonomously, they often require expensive hardware and sensors, such as RGB-D cameras and 3D-LiDAR, leading to a persistent trade-off between performance and cost. To this end, we propose RELAX, a novel end-to-end autonomous framework that is exceptionally cost-efficient, requiring only a single 2D-LiDAR to enable UAVs operating in unknown environments. Specifically, RELAX comprises three components: a pre-processing map constructor; an offline mission planner; and a reinforcement learning (RL)-based online re-planner. Experiments demonstrate that RELAX offers more robust dynamic navigation compared to existing algorithms, while only costing a fraction of the others. The code will be made public upon acceptance

Information-rich Task Allocation and Motion Planning for Heterogeneous Sensor Platforms

This paper introduces a novel stratified planning algorithm for teams of heterogeneous mobile sensors that maximizes information collection while minimizing resource costs. The main contribution of this work is the scalable unification of effective algorithms for de- centralized informative motion planning and decentralized high-level task allocation. We present the Information-rich Rapidly-exploring Random Tree (IRRT) algorithm, which is amenable to very general and realistic mobile sensor constraint characterizations, as well as review the Consensus-Based Bundle Algorithm (CBBA), offering several enhancements to the existing algorithms to embed information collection at each phase of the planning process. The proposed framework is validated with simulation results for networks of mobile sensors performing multi-target localization missions.United States. Air Force. Office of Scientific Research (Grant FA9550-08-1-0086)United States. Air Force. Office of Scientific Research. Multidisciplinary University Research Initiative (FA9550-08-1-0356

Factored Monte-Carlo tree search for coordinating UAVs in disaster response

The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out surveys is a major challenge for emergency responders. In particular, UAVs have to fly over kilometre-scale areas while trying to discover casualties as quickly as possible. However, an increase in the availability of real-time data about a disaster from sources such as crowd reports or satellites presents a valuable source of information to drive the planning of UAV flight paths over a space in order to discover people who are in danger. Nevertheless challenges remain when planning over the very large action spaces that result. To this end, we introduce the survivor discovery problem and present as our solution, the first example of a factored coordinated Monte Carlo tree search algorithm to perform decentralised path planning for multiple coordinated UAVs. Our evaluation against standard benchmarks show that our algorithm, Co-MCTS, is able to find more casualties faster than standard approaches by 10% or more on simulations with real-world data from the 2010 Haiti earthquake

UAV Path Planning and Obstacle Avoidance Based on Fuzzy Logic and Kinodynamic RRT Methods

Path Planning is one of the important problems to be explored in unmanned aerial vehicle (UAV) to find the optimal path between starting position and destination. The aim of path planning technique is not only to find the shortest path but also to provide the collision-free path for the UAV in unknown environment. Although there have been significant advances on the methods of path planning where the map of environment is known in advance, there are still some challenges to be addressed for dynamic autonomous navigation for the UAV in unknown environment. This thesis research proposes a new path planning method named Fuzzy Kinodynamic RRT for unmanned aerial vehicle flying in the unknown environment. This method generates a global path based on RRT [1] (Rapidly-exploring random tree) and utilizes fuzzy logic system to avoid obstacles in real time. A set of heuristics fuzzy rules are designed to lead the UAV away from unmodeled obstacles and to guide the UAV towards the goal. The rules are also tested in different scenarios, and they are all working efficiently both in simple and complicated cases. The UAV starts to fly along the path generated by RRT, and the fuzzy logic system is then activated when it comes across the obstacle. When the sensor detects no collision within a specific distance, the fuzzy system is turned off and the UAV flies back to the previous path towards the final destination. The simulations of the developed algorithm have been carried out in various scenarios, with the sensor to detect the obstacles. The numerical simulations show the satisfactory results in various scenarios for path planning that considerably reduces the risk of colliding with other stationary and moving obstacles. A more robust and efficient fuzzy logic controller which embeds the path planning is finally proposed and the simulation shows the satisfactory results in complicated environments

Visual flight rules-based collision avoidance systems for UAV flying in civil aerospace

The operation of Unmanned Aerial Vehicles (UAVs) in civil airspace is restricted by the aviation authorities, which require full compliance with regulations that apply for manned aircraft. This paper proposes control algorithms for a collision avoidance system that can be used as an advisory system or a guidance system for UAVs that are flying in civil airspace under visual flight rules. A decision-making system for collision avoidance is developed based on the rules of the air. The proposed architecture of the decision-making system is engineered to be implementable in both manned aircraft and UAVs to perform different tasks ranging from collision detection to a safe avoidance manoeuvre initiation. Avoidance manoeuvres that are compliant with the rules of the air are proposed based on pilot suggestions for a subset of possible collision scenarios. The proposed avoidance manoeuvres are parameterized using a geometric approach. An optimal collision avoidance algorithm is developed for real-time local trajectory planning. Essentially, a finite-horizon optimal control problem is periodically solved in real-time hence updating the aircraft trajectory to avoid obstacles and track a predefined trajectory. The optimal control problem is formulated in output space, and parameterized by using B-splines. Then the optimal designed outputs are mapped into control inputs of the system by using the inverse dynamics of a fixed wing aircraft

Information-based path planning for source term estimation using an unmanned aerial vehicle: algorithms and experiments

Department of Mechanical Enginering (Mechanical Engineering)Searching and estimating source information such as the location and release rate, called a source term, have many applications across environmental, medical, and security domains. For autonomous source search and estimation in a turbulent environment, this thesis presents two information-theoretic search strategies. Firstly, Gaussian mixture model (GMM) based infotaxis, termed as GMM-Infotaxis, is presented. The GMM is used to determine the action candidates for the next best informative sampling position in a continuous domain by appropriately clustering possible source locations obtained from the particle filter, compared with Infotaxis using discrete action candidates. This facilitates the better trade-off between exploitation and exploration for search, resulting in more efficient search and better estimation performance. However, GMM-Infotaxis has limitations in complex environments with many obstacles such as urban area, as this approach only predicts one step ahead action and the obstacles prevent efficient search. To address this problem, Infotaxis combined with the Rapidly-exploring Random Trees (RRT) is proposed and termed as RRT-Infotaxis. By introducing new utility function which is designed to maximize entropy reduction and minimize searching path at the same time, RRT-Infotaxis has advantage of searching efficient path in obstacle-rich environments. With proposed utility function, this approach is designed not only to avoid obstacles but also to sample the next best sampling positions considering several steps ahead in a continuous domain. Numerical simulations for both strategies, GMM-Infotaxis and RRT-Infotaxis, are implemented to prove the enhanced performance compared to the conventional Infotaxis. Numerical simulations show that in an open space the performance of GMM-Infotaxis is better than the conventional Infotaxis and in various urban environments RRT-Infotaxis outperforms both original Infotaxis and GMM-Infotaxis. Besides, real outdoor flight experiments using a multirotor UAV in an open space for GMM-Infotaxis are conducted. It shows the superior performance of the GMM-Infotaxis compared with the original Infotaxis method.ope

Path planning for unmanned aerial vehicles using visibility line-based methods

This thesis concerns the development of path planning algorithms for unmanned aerial vehicles (UAVs) to avoid obstacles in two- (2D) and three-dimensional (3D) urban environments based on the visibility graph (VG) method. As VG uses all nodes (vertices) in the environments, it is computationally expensive. The proposed 2D path planning algorithms, on the contrary, select a relatively smaller number of vertices using the so-called base line (BL), thus they are computationally efficient. The computational efficiency of the proposed algorithms is further improved by limiting the BL’s length, which results in an even smaller number of vertices. Simulation results have proven that the proposed 2D path planning algorithms are much faster in comparison with the VG and hence are suitable for real time path planning applications. While vertices can be explicitly defined in 2D environments using VG, it is difficult to determine them in 3D as they are infinite in number at each obstacle’s border edge. This issue is tackled by using the so-called plane rotation approach in the proposed 3D path planning algorithms where the vertices are the intersection points between a plane rotated by certain angles and obstacles edges. In order to ensure that the 3D path planning algorithms are computationally efficient, the proposed 2D path planning algorithms are applied into them. In addition, a software package using Matlab for 2D and 3D path planning has also been developed. The package is designed to be easy to use as well as user-friendly with step-by-step instructions

Optimisation-based verification process of obstacle avoidance systems for unmanned vehicles

This thesis deals with safety verification analysis of collision avoidance systems for unmanned vehicles. The safety of the vehicle is dependent on collision avoidance algorithms and associated control laws, and it must be proven that the collision avoidance algorithms and controllers are functioning correctly in all nominal conditions, various failure conditions and in the presence of possible variations in the vehicle and operational environment. The current widely used exhaustive search based approaches are not suitable for safety analysis of autonomous vehicles due to the large number of possible variations and the complexity of algorithms and the systems. To address this topic, a new optimisation-based verification method is developed to verify the safety of collision avoidance systems. The proposed verification method formulates the worst case analysis problem arising the verification of collision avoidance systems into an optimisation problem and employs optimisation algorithms to automatically search the worst cases. Minimum distance to the obstacle during the collision avoidance manoeuvre is defined as the objective function of the optimisation problem, and realistic simulation consisting of the detailed vehicle dynamics, the operational environment, the collision avoidance algorithm and low level control laws is embedded in the optimisation process. This enables the verification process to take into account the parameters variations in the vehicle, the change of the environment, the uncertainties in sensors, and in particular the mismatching between model used for developing the collision avoidance algorithms and the real vehicle. It is shown that the resultant simulation based optimisation problem is non-convex and there might be many local optima. To illustrate and investigate the proposed optimisation based verification process, the potential field method and decision making collision avoidance method are chosen as an obstacle avoidance candidate technique for verification study. Five benchmark case studies are investigated in this thesis: static obstacle avoidance system of a simple unicycle robot, moving obstacle avoidance system for a Pioneer 3DX robot, and a 6 Degrees of Freedom fixed wing Unmanned Aerial Vehicle with static and moving collision avoidance algorithms. It is proven that although a local optimisation method for nonlinear optimisation is quite efficient, it is not able to find the most dangerous situation. Results in this thesis show that, among all the global optimisation methods that have been investigated, the DIviding RECTangle method provides most promising performance for verification of collision avoidance functions in terms of guaranteed capability in searching worst scenarios