Mina Kamel

Receding horizon “next-best-view” planner for 3D exploration

This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” scheme: In an online computed random tree it finds the best branch, the quality of which is determined by the amount of unmapped space that can be explored. Only the first edge of this branch is executed at every planning step, while repetition of this procedure leads to complete exploration results. The proposed planner is capable of running online, onboard a robot with limited resources. Its high performance is evaluated in detailed simulation studies as well as in a challenging real world experiment using a rotorcraft micro aerial vehicle. Analysis on the computational complexity of the algorithm is provided and its good scaling properties enable the handling of large scale and complex problem setups.

Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots

This paper presents a new algorithm for three-dimensional coverage path planning for autonomous structural inspection operations using aerial robots. The proposed approach is capable of computing short inspection paths via an alternating two-step optimization algorithm according to which at every iteration it attempts to find a new and improved set of viewpoints that together provide full coverage with decreased path cost. The algorithm supports the integration of multiple sensors with different fields of view, the limitations of which are respected. Both fixed-wing as well as rotorcraft aerial robot configurations are supported and their motion constraints are respected at all optimization steps, while the algorithm operates on both mesh- and occupancy map-based representations of the environment. To thoroughly evaluate this new path planning strategy, a set of large-scale simulation scenarios was considered, followed by multiple real-life experimental test-cases using both vehicle configurations.

Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using Robot Operating System

In this chapter, strategies for Model Predictive Control (MPC) design and implementation for Unmaned Aerial Vehicles (UAVs) are discussed. This chapter is divided into two main sections. In the first section, modelling, controller design and implementation of MPC for multi-rotor systems is presented. In the second section, we show modelling and controller design techniques for fixed-wing UAVs. System identification techniques are used to derive an estimate of the system model, while state of the art solvers are employed to solve the optimization problem online. By the end of this chapter, the reader should be able to implement an MPC to achieve trajectory tracking for both multi-rotor systems and fixed-wing UAVs.

Fast nonlinear Model Predictive Control for unified trajectory optimization and tracking

This paper presents a framework for real-time, full-state feedback, unconstrained, nonlinear model predictive control that combines trajectory optimization and tracking control in a single, unified approach. The proposed method uses an iterative optimal control algorithm, namely Sequential Linear Quadratic (SLQ), in a Model Predictive Control (MPC) setting to solve the underlying nonlinear control problem and simultaneously derive the optimal feedforward and feedback terms. Our customized solver can generate trajectories of multiple seconds within only a few milliseconds. The performance of the approach is validated on two different hardware platforms, an AscTec Firefly hexacopter and the ball balancing robot Rezero. In contrast to similar approaches, we perform experiments that require leveraging the full system dynamics.

Receding horizon path planning for 3D exploration and surface inspection

Within this paper a new path planning algorithm for autonomous robotic exploration and inspection is presented. The proposed method plans online in a receding horizon fashion by sampling possible future configurations in a geometric random tree. The choice of the objective function enables the planning for either the exploration of unknown volume or inspection of a given surface manifold in both known and unknown volume. Application to rotorcraft Micro Aerial Vehicles is presented, although planning for other types of robotic platforms is possible, even in the absence of a boundary value solver and subject to nonholonomic constraints. Furthermore, the method allows the integration of a wide variety of sensor models. The presented analysis of computational complexity and thorough simulations-based evaluation indicate good scaling properties with respect to the scenario complexity. Feasibility and practical applicability are demonstrated in real-life experimental test cases with full on-board computation.

Fast nonlinear model predictive control for multicopter attitude tracking on SO(3)

Exploiting the highly dynamic flight envelope of a multirotor Micro Aerial Vehicle (MAV) is a particularly challenging task that requires special treatment of its attitude control loop. In this paper, we propose a fast nonlinear model predictive control approach based on a geometric formulation of the error to track the MAV attitude on the SO(3) special orthogonal group. The proposed controller is combined with an optimal position tracking control strategy in a cascaded fashion. The overall framework is implemented on-board a hexacopter and verified in both high fidelity simulations as well as extensive experimental studies. As shown, the resulting closed-loop system exploits the dynamics of the platform, is able to track aggressive trajectories, recover from arbitrary attitude configurations and also handle a propeller failure.

Aerial picking and delivery of magnetic objects with MAVs

Autonomous delivery of goods using a Micro Air Vehicle (MAV) is a difficult problem, as it poses high demand on the MAVs control, perception and manipulation capabilities. This problem is especially challenging if the exact shape, location and configuration of the objects are unknown. In this paper, we report our findings during the development and evaluation of a fully integrated system that is energy efficient and enables MAVs to pick up and deliver objects with partly ferrous surface of varying shapes and weights. This is achieved by using a novel combination of an electro-permanent magnetic gripper with a passively compliant structure and integration with detection, control and servo positioning algorithms. The systems ability to grasp stationary and moving objects was tested, as well as its ability to cope with different shapes of the object and external disturbances. We show that such a system can be successfully deployed in scenarios where an object with partly ferrous parts needs to be gripped and placed in a predetermined location.

Collaborative transportation using MAVs via passive force control

This paper shows a strategy based on passive force control for collaborative object transportation using Micro Aerial Vehicles (MAVs), focusing on the transportation of a bulky object by two hexacopters. The goal is to develop a robust approach which does not rely on: (a) communication links between the MAVs, (b) the knowledge of the payload shape and (c) the position of grasping point. The proposed approach is based on the master-slave paradigm, in which the slave agent guarantees compliance to the external force applied by the master to the payload via an admittance controller. The external force acting on the slave is estimated using a non-linear estimator based on the Unscented Kalman Filter (UKF) from the information provided by a Visual-Inertial (VI) navigation system. Experimental results (online video [1]) demonstrate the performance of the force estimator and show the collaborative transportation of a 1.2 m long object.

Full-body multi-objective controller for aerial manipulation

In this paper we present an multi-objective dynamic controller for a micro-aerial vehicle (MAV) equipped with dextrous aerial manipulator. The MAV and the manipulator are considered as a multi-body system where the control input is generated for the MAV and the manipulator joints simultaneously, taking into account dynamic effects. The redundancy of the system is exploited by setting various desired tasks with associated priorities. The proposed controller is compared against classic control approach. Extensive simulation results are presented in Micro Aerial Vehicles Simulator “RotorS”.

Tree cavity inspection using aerial robots

We present an aerial robotic platform for remote tree cavity inspection, based on a hexacopter Micro-Aerial vehicle (MAV) equipped with a dexterous manipulator. The goal is to make the inspection process safer and more efficient and facilitate data collection about tree cavities, which are important for the conservation of biodiversity in forest ecosystems. This work focuses on two key enabling technologies, namely a vision-based cavity detection system and strategies for high level control of the MAV and manipulator. The results of both simulation and real-world experiments are discussed at the end of the paper and demonstrate the effectiveness of our approach.