NMPC and genetic algorithm based approach for trajectory tracking and collision avoidance of UAVs

Research on unmanned aircraft is improving constantly the autonomous flight capabilities of these vehicles in order to provide performance needed to employ them in even more complex tasks. UAV Path Planning (PP) system plans the best path to per- form the mission and then it uploads this path on the Flight Management System (FMS) providing reference to the aircraft navigation. Tracking the path is the way to link kine- matic references related to the desired aircraft positions with its dynamic behaviours, to generate the right command sequence. This paper presents a Nonlinear Model Predictive Control (NMPC) system that tracks the reference path provided by PP and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control sys- tem solves on-line (i.e., at each sampling time) a finite horizon (state horizon) open loop optimal control problem with a Genetic Algorithm. This algorithm finds the command sequence that minimises the tracking error with respect to the reference path, driving the aircraft far from sensed obstacles and towards the desired trajectory

From computer-aided to intelligent machining: Recent advances in computer numerical control machining research

The aim of this paper is to provide an introduction and overview of recent advances in the key technologies and the supporting computerized systems, and to indicate the trend of research and development in the area of computational numerical control machining. Three main themes of recent research in CNC machining are simulation, optimization and automation, which form the key aspects of intelligent manufacturing in the digital and knowledge based manufacturing era. As the information and knowledge carrier, feature is the efficacious way to achieve intelligent manufacturing. From the regular shaped feature to freeform surface feature, the feature technology has been used in manufacturing of complex parts, such as aircraft structural parts. The authors’ latest research in intelligent machining is presented through a new concept of multi-perspective dynamic feature (MpDF), for future discussion and communication with readers of this special issue. The MpDF concept has been implemented and tested in real examples from the aerospace industry, and has the potential to make promising impact on the future research in the new paradigm of intelligent machining. The authors of this paper are the guest editors of this special issue on computational numerical control machining. The guest editors have extensive and complementary experiences in both academia and industry, gained in China, USA and UK

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

CMAC Trained Optimum Mid course Guidance for Tactical Flight Vehicle

This paper discusses design and validation of neural network based mid-course guidance law of a surface to air flight vehicle. In present study, initially different optimal trajectories have been generated off-line of different pursuer-evader engagements by ensuring minimum flight time, maximum terminal velocity and favorable handing over conditions for seeker based terminal guidance. These optimal trajectories have been evolved by nonlinear programming based direct method of optimisation. The kinematic information of both pursuer and evader, generated based on these trajectories have been used to train cerebellar model articulate controller (CMAC) neural network. Later for a given engagement scenario an on-line near optimal mid-course guidance law has been evolved based on output of trained network. Training has been carried out by CMAC type supervisory neural network. The tested engagement condition is within input/output training space of neural network. Seeker based homing guidance has been used for terminal phase. Complete methodology has been validated along pitch plane of pursuer-evader engagement. During mid-course phase, the guidance demand has been tracked by attitude hold autopilot and during terminal phase, the guidance demanded lateral acceleration has been tracked by acceleration autopilot. System robustness has been studied in presence of plant parameter variations and sensor noise under Monte Carlo Platform

A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots

This paper contributes towards the development and comparison of Divergent-Component-of-Motion (DCM) based control architectures for humanoid robot locomotion. More precisely, we present and compare several DCM based implementations of a three layer control architecture. From top to bottom, these three layers are here called: trajectory optimization, simplified model control, and whole-body QP control. All layers use the DCM concept to generate references for the layer below. For the simplified model control layer, we present and compare both instantaneous and Receding Horizon Control controllers. For the whole-body QP control layer, we present and compare controllers for position and velocity control robots. Experimental results are carried out on the one-meter tall iCub humanoid robot. We show which implementation of the above control architecture allows the robot to achieve a walking velocity of 0.41 meters per second.Comment: Submitted to Humanoids201

A Case based Online Trajectory Planning Method of Autonomous Unmanned Combat Aerial Vehicles with Weapon Release Constraints

As a challenging and highly complex problem, the trajectory planning for unmanned combat aerial vehicle (UCAV) focuses on optimising flight trajectory under such constraints as kinematics and complicated battlefield environment. An online case-based trajectory planning strategy is proposed in this study to achieve rapid control variables solution of UCAV flight trajectory for the of delivery airborne guided bombs. Firstly, with an analysis of the ballistic model of airborne guided bombs, the trajectory planning model of UCAVs is established with launch acceptable region (LAR) as a terminal constraint. Secondly, a case-based planning strategy is presented, which involves four cases depending on the situation of UCAVs at the current moment. Finally, the feasibility and efficiency of the proposed planning strategy is validated by numerical simulations, and the results show that the presented strategy is suitable for UCAV performing airborne guided delivery missions in dynamic environments

An evolutionary computation approach to three- dimensional path planning for unmanned aerial vehicles with tactical and kinematic constraints

This paper presents a novel evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles (UAVs) with tactical and kinematic constraints. A genetic algorithm (GA) is modified and extended for path planning. Two GAs are seeded at the initial and final positions with a common objective to minimise their distance apart under given UAV constraints. This is accomplished by the synchronous optimisation of subsequent control vectors. The proposed evolutionary computation approach is called synchronous genetic algorithm (SGA). The sequence of control vectors generated by the SGA constitutes to a near-optimal path plan. The resulting path plan exhibits no discontinuity when transitioning from curve to straight trajectories. Experiments and results show that the paths generated by the SGA are within 2% of the optimal solution. Such a path planner when implemented on a hardware accelerator, such as field programmable gate array chips, can be used in the UAV as on-board replanner, as well as in ground station systems for assisting in high precision planning and modelling of mission scenarios