Omnidirectional bearing-only see-and-avoid for small aerial robots

Motivated by the growing interest in unmanned aerial system’s applications in indoor and outdoor settings and the standardisation of visual sensors as vehicle payload. This work presents a collision avoidance approach based on omnidirectional cameras that does not require the estimation of range between two platforms to resolve a collision encounter. It will achieve a minimum separation between the two vehicles involved by maximising the view-angle given by the omnidirectional sensor. Only visual information is used to achieve avoidance under a bearing-only visual servoing approach. We provide theoretical problem formulation, as well as results from real flight using small quadrotors.

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of cross- entropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.

Quadcopter see and avoid using a fuzzy controller

Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish this task we used the visual information from the front camera. Images are processed off-board and the result send to the Fuzzy Logic controller which then send commands to modify the orientation of the aircraft. Results from flight test are presented with a commercial off-the-shelf platform.

UAS See-and-Avoid using two different approaches of Fuzzy Control

This work presents two UAS See and Avoid approaches using Fuzzy Control. We compare the performance of each controller when a Cross-Entropy method is applied to optimase the parameters for one of the controllers. Each controller receive information from an image processing front-end that detect and track targets in the environment. Visual information is then used under a visual servoing approach to perform autonomous avoidance. Experimental flight trials using a small quadrotor were performed to validate and compare the behaviour of both controllers

See and avoid using on-board computer vision

The integration of unmanned aircraft into civil airspace is a complex issue. One key question is whether unmanned aircraft can operate just as safely as their manned counterparts. The absence of a human pilot in unmanned aircraft automatically points to a deficiency that is the lack of an inherent see-and-avoid capability. To date, regulators have mandated that an “equivalent level of safety” be demonstrated before UAVs are permitted to routinely operate in civil airspace. This chapter proposes techniques, methods, and hardware integrations that describe a “sense-and-avoid” system designed to address the lack of a see-and-avoid capability in UAVs.

Cross-entropy optimization for scaling factors of a fuzzy controller : a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

Design and evaluation of decision and control strategies for autonomous vision-based see and avoid systems

This paper details the design and performance assessment of a unique collision avoidance decision and control strategy for autonomous vision-based See and Avoid systems. The general approach revolves around re-positioning a collision object in the image using image-based visual servoing, without estimating range or time to collision. The decision strategy thus involves determining where to move the collision object, to induce a safe avoidance manuever, and when to cease the avoidance behaviour. These tasks are accomplished by exploiting human navigation models, spiral motion properties, expected image feature uncertainty and the rules of the air. The result is a simple threshold based system that can be tuned and statistically evaluated by extending performance assessment techniques derived for alerting systems. Our results demonstrate how autonomous vision-only See and Avoid systems may be designed under realistic problem constraints, and then evaluated in a manner consistent to aviation expectations.

A Survey of autonomous vision-based See and Avoid for Unmanned Aircraft Systems

This paper provides a comprehensive review of the vision-based See and Avoid problem for unmanned aircraft. The unique problem environment and associated constraints are detailed, followed by an in-depth analysis of visual sensing limitations. In light of such detection and estimation constraints, relevant human, aircraft and robot collision avoidance concepts are then compared from a decision and control perspective. Remarks on system evaluation and certification are also included to provide a holistic review approach. The intention of this work is to clarify common misconceptions, realistically bound feasible design expectations and offer new research directions. It is hoped that this paper will help us to unify design efforts across the aerospace and robotics communities.

Omnidirectional bearing-only see-and-avoid for small aerial robots:

Motivated by the growing interest in unmanned aerial system's applications in indoor and outdoor settings and the standardisation of visual sensors as vehicle payload. This work presents a collision avoidance approach based on omnidirectional cameras that does not require the estimation of range between two platforms to resolve a collision encounter. It will achieve a minimum separation between the two vehicles involved by maximising the view-angle given by the omnidirectional sensor. Only visual information is used to achieve avoidance under a bearing-only visual servoing approach. We provide theoretical problem formulation, as well as results from real flight using small quadrotors

Cross-entropy optimization for scaling factors of a fuzzy controller: a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of crossentropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.

Quadcopter see and avoid using a fuzzy controller

Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish this task we used the visual information from the front camera. Images are processed off-board and the result send to the Fuzzy Logic controller which then send commands to modify the orientation of the aircraft. Results from flight test are presented with a commercial off-the-shelf platform.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand- avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

Using the Cross-Entropy Method for Control Optimization: A Case Study of See-and-Avoid on Unmanned Aerial Vehicles

This paper presents an adaptation of the Cross-Entropy (CE) method to optimize fuzzy logic controllers. The CE is a recently developed optimization method based on a general Monte-Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling. This work shows the application of this optimization method to optimize the inputs gains, the location and size of the different membership functions' sets of each variable, as well as the weight of each rule from the rule's base of a fuzzy logic controller (FLC). The control system approach presented in this work was designed to command the orientation of an unmanned aerial vehicle (UAV) to modify its trajectory for avoiding collisions. An onboard looking forward camera was used to sense the environment of the UAV. The information extracted by the image processing algorithm is the only input of the fuzzy control approach to avoid the collision with a predefined object. Real tests with a quadrotor have been done to corroborate the improved behavior of the optimized controllers at different stages of the optimization process.