Robust Decentralized Supervisory Control in a Leader-Follower Configuration with Obstacle Avoidance

In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.

Autonomous Landing of Fixed Wing Unmanned Aerial Vehicle with Reactive Collision Avoidance

In this paper, a multi-agent formation control strategy with collision avoidance and obstacle avoidance in complex environments is proposed. Under a virtual leader structure, the multi-agent formation is realized through path planning and formation control. In path planning, a transitional region based on prediction horizon is used to replan reference trajectories and avoid obstacles in advance. The virtual-leader alternation strategy and feasible state searching strategy are adopted to avoid one side obstacles and crowded obstacles, respectively. Then the path planning optimization problem is formulated by mixed-integer quadratic programming (MIQP) to calculate trajectories collision-free with obstacles. In formation control, a distributed model predictive control (DMPC) algorithm is developed to form and maintain formation. In DMPC optimization problem, the non-convex obstacle avoidance constraints are replaced with deviation constraints and linear constraints to design terminal invariant set and reduce online computation burden. Under the whole formation control strategy, the tasks of obstacle avoidance and collision avoidance are both achieved and the agents maintain the formation as much as possible. Finally, the example with different obstacle situations is discussed to illustrate the effectiveness of the proposed strategy.

Collision avoidance is more and more of importance due to the growing amount of space debris posing a threat not only on satellites in orbit but also on upcoming missions. To avoid a collision between space debris and functioning satellites or even between two functioning satellites collision avoidance manoeuvres can be induced if one of the satellites has a working propulsion system. An alternative method of collision avoidance operations is presented in this study. By changing the satellites attitude, it is possible to obtain an increase or decrease in the semi-major axis in relation to its nominal orbital decay. The change in the semi major axis together with the time until closest approach provokes a change in the relative geometry of the object orbits resulting in a decreasing collision risk. This method enables collision avoidance manoeuvres for satellites in Low Earth Orbits featuring active attitude control and drag-susceptible geometries. Additionally, the probability of a collision can be reduced by changing the satellite attitude so that the minimum effective area is perpendicular to the relative velocity vector at the time of closest approach. Both methods can be applied to satellites without propulsion, which otherwise would be defenceless. A test run for verification of collision avoidance by means of drag-minimization was performed in June 2018 with the Technology Experiment Carrier (TET-1) satellite of the FireBIRD constellation. The experiment planning and results are presented along with the initial flight dynamics analysis and representative examples for collision avoidance scenarios.

In response to safety concerns pertaining to multi-UAV formation flights, a novel obstacle avoidance method based on an Improved Adaptive Artificial Potential field (IAAPF) is presented. This approach enhances UAV obstacle avoidance capabilities by utilizing segmented attraction potential fields refined with adaptive factors and augmented with virtual forces for inter-UAV collision avoidance. To further enhance the control and stability of multi-UAV formations, a Sliding Mode Control (SMC) method is integrated into the IAAPF-based obstacle avoidance framework. Renowned for its robustness and ability to handle system uncertainties and disturbances, the SMC method is combined with a feedback control system that utilizes inner and outer loops. The outer loop generates the desired path based on the leader’s state and control commands, while the inner loop tracks these trajectories and adjusts the follower UAVs’ motions. This design ensures that obstacle feedback is accounted for before the desired state information is received, enabling effective obstacle avoidance while maintaining formation integrity. Integrating leader-follower formation control techniques with SMC-based multi-UAV obstacle avoidance strategies ensures the effective convergence of the formation velocity and spacing to predetermined values, meeting the cooperative obstacle avoidance requirements of multi-UAV formations. Simulation results validate the efficacy of the proposed method in reaching otherwise unreachable destinations within obstacle-rich environments, while ensuring robust collision avoidance among UAVs.

Most ship collisions are caused by human error, making them unpreventable as long as humans operate a ship. Autonomous navigation technology is expected to prevent these accidents. Collision avoidance is a key issue in the realization of autonomous ships. To date, rule- and artificial intelligence (AI)-based collision avoidance algorithms have been studied. However, most of them assumed a simple environment in which other ships maintain their speed and course. During actual navigation in congested waters, safe and efficient collision avoidance are achieved through cooperative maneuvers between ships that obey the COLREGs. To realize cooperative maneuvers of ships, multi-agent deep reinforcement learning was applied to learn collision avoidance maneuvers considering the motions of other ships and COLREGs. Then, the developed multi-agent AI was evaluated with typical one-on-one encounters. The multi-agent AI performed cooperative collision avoidance maneuvers in head-on and crossing situations. Furthermore, the influence of reward setting regarding the degree of compliance with COLREGs on the cooperative collision avoidance was investigated. These results demonstrated that the multi-agent AI was promising for realizing cooperative collision avoidance maneuvers similar to those performed by humans.

Unmanned Aerial Vehicle (UAV) has received wide interest in recent years because of their promising potentials for several applications that include border surveillance, intrusion, detection, emergent item delivery, wildlife monitoring, rescue operations etc. The growing number of entrances of drones into airspace has emerged a significant interest in the collision and obstacle avoidance in autonomous operation. One of major challenges for safer deployment of UAVs is collision avoidance in order to make UAVs autonomous and self-sustainable. In today’s technological era, it is possible for UAVs to avoid obstacles and prevent collisions while navigation by implementing cutting edge computer vision algorithms and visual tracking systems. But it is challenging due to the limited payload in UAV that only permits vision based sensors such as camera, inertial measurement unit such for control and obstacle detection. This paper aims to summarize existing solutions of collision avoidance strategies used in the navigation of UAV. Collision avoidance strategies based on geometry, optimization, sense and avoid, vision-based and force field based methods are investigated in this study.

This paper presents a new design for formation control on a group of autonomous humanoid robots with obstacle and collision avoidance ability that inspired by human walking behavior in a pedestrian crowd. This human walking behavior model is known as Social Force Model (SFM) and has been widely used to predict individual movement in a crowd in many variety of situation. We combine consensus algorithm with virtual structure approach for formation control and SFM for obstacle and collision avoidance. In this research, we induced some factors in SFM into consensus equation and then implemented into a group of humanoid robots. The aim of the integration is to create a group of robots that are capable of carrying out its collective tasks while still able to maintain its safety. The use of perception effect, distinct obstacle and collision avoidance function on the algorithm will differentiate algorithm with the others. The attractive feature of this new design is the various behaviors of robots to go back to their initial formation after they avoid obstacles. In the case of robot is trapped in a crowded situation (singularity condition), robot will still trying to look a new position, until it find a condition that allow him to move forward. We verify the proposed algorithm by some simulations and experiments.

Autonomous surface crafts (ASCs) have strong potential as platforms for repeat transect oceanographic surveys in coastal and estuarine systems. Good spatial and temporal coverage and resolution could be achieved by an ASC capable of weekslong operations at average speed 5 knots, which is the design goal for the Surveying Coastal Ocean Autonomous Profiler (SCOAP) catamaran ASC. This project addresses operational challenges for long duration ASC deployments, to help minimize risk of collisions: site planning, and on-board collision avoidance (CA) software. In anticipation of a Rhode Island Sound (RIS) SCOAP deployment, a site planning method was developed using archived Automatic Identification System (AIS) data. AIS is a real-time, radio-based system for sharing navigation information among vessels. Archived data were used to determine geographic and seasonal patterns of RIS vessel traffic, and associated frequency of potential collision encounters for various hypothetical repeat transect ASC surveys under consideration. Seasonal-mean AIS vessel traffic varied from as low as 0.01 tracks per day or less in southeastern RIS to about 8 tracks per day in shipping lanes near Narragansett Bay. Corresponding numbers of potential collision encounters for a month-long repeat transect ASC survey in these two areas are about 0-2 and 12-22, respectively, with lower/higher values in winter/summer. Crudely estimated non-AIS traffic suggests up to 3-4 times higher total (AIS and non-AIS vessels) potential encounters. The method provides quantitative information to enable site planning that best balances oceanographic sampling goals against associated collision risks. On-board autonomy software capable of performing collision avoidance (CA) maneuvers is essential for long duration ASC operations. To demonstrate and evaluate its CA performance the Mission Oriented Operation Suite Interval Programming Helm autonomy software, to be implemented on SCOAP, was used to simulate repeat transect ASC surveys with traffic vessels. The CA algorithm used avoids collisions without attempting to comply with Coast Guard (CG) Collision Avoidance Regulations (COLREGS). Three categories of ASC CA maneuver were identified, in which nearly all encounters fall: large deflection, course-reversal, and leave/return to station keeping. ASC path disruptions are substantially reduced when traffic vessels perform CA in addition to the ASC; sensitivity of encounter statistics to other ASC and traffic vessel path configurations, and CA algorithm parameters, is modest. Overall the CA algorithm performed reliably, resolving at least 97% of encounters in 10s of seconds, including interactions with multiple traffic vehicles simultaneously. A framework was developed for how ASCs can best approach compliance with COLREGS, the

Determining and visualizing safe motion parameters of a ship navigating in severe weather conditions

Swarming UAV flight offers several advantages over single UAV flight. However, it requires a particular guidance paradigm since the collective behavior emerges from the behavior of each individual. This paper proposes a swarming guidance algorithm developed from the artificial potential field concept. The guidance algorithm is capable of aggregation, swarm collision avoidance, waypoint tracking, obstacle avoidance, and rotational maneuvers. The method is simulated using numerical simulation with a quadcopter UAV model.

Collision risk assessment and automatic obstacle avoidance strategy for teleoperation robots

Deep reinforcement learning with dynamic window approach based collision avoidance path planning for maritime autonomous surface ships

This article presents an obstacle avoidance approach for manipulators based on an obstacle avoidance path planning mechanism. Although the obstacle dealt with in the article is of the cubic type, it can be transformed to the spherical type as shown in the article, which has a simpler analytical description. The obstacle avoidance problem has been formulated in terms of collision avoidance of links rather than points. Link collision avoidance is achieved by continuously controlling the link closest point to the obstacle. For the robot SIR-1 which has an articulated chain, a link can be represented as the line segment defined by the Cartesian position of its two neighboring joints. We implement a simple obstacle avoidance path planning algorithm.

This paper presents a collision-prediction and avoidance algorithm for multivehicle cooperative missions. Using Bézier surfaces, the algorithm avoids time discretization of trajectories and is capable of considering uncertainties in speed profiles. The algorithm runs independently onboard each vehicle and, upon detection of a possible collision, replans its trajectory. Under a few assumptions, the modified trajectory is guaranteed to avoid the predicted collision as well as satisfy mission specific constraints. The deviations in position, velocity, and acceleration caused by the avoidance maneuver are small and respect bounds that can be computed offline. These bounds can be used during the mission-planning phase to guarantee satisfaction of vehicle dynamic constraints and intervehicle safety distances even during collision-avoidance maneuvers.