Collision-free Guidance Research Articles

Urban Air Mobility Guidance with Panel Method: Experimental Evaluation Under Wind Disturbances

In this paper, a nature-inspired guidance algorithm based on the panel method is proposed. The panel method is a numerical tool borrowed from the aerodynamics domain to calculate the potential field of a fluid flow around arbitrarily shaped objects. The proposed algorithm has little computational load and generates guidance vectors in real time that can guide multiple vehicles through smooth and collision-free paths. Panel-method-based guidance is a promising candidate for air mobility applications in urban environments where multiple aerial vehicles are expected to operate simultaneously without colliding with architectural structures and other vehicles in the airspace. In this study, the effectiveness and feasibility of the proposed guidance method is evaluated through a test campaign conducted in Toulouse, France, using multiple quadrotors in a scaled urban environment. Furthermore, the robustness of the guidance method under wind disturbances is tested in both indoor and outdoor experiments. Experimental results suggest that the panel-method-based guidance algorithm is an effective and robust tool for real-time, collision-free guidance of multiple aerial vehicles in complex urban environments.

Helicopter short-term collision avoidance with sampling-based model predictive control

AbstractAutomatic helicopter flight in uncertain surroundings remains a challenging task due to sudden changes in environment, requiring fast response to guarantee safe and collision-free guidance. Increasing numbers of small unmanned aerial vehicles, which are not covered by air traffic control, pose a potential threat to rotorcraft operating in lower airspace. To provide collision avoidance in this scenario, the capability of reacting immediately to appearing obstacles and guiding the rotorcraft along feasible evasive trajectories is required. This paper presents an approach to short-term collision avoidance based on model predictive techniques. The proposed method, originally developed for automotive applications, finds optimal control inputs by predicting a set of trajectories utilizing a model resembling the helicopter dynamics. Compared to model predictive control, no iterative optimization is adopted, resulting in deterministic execution time. The proposed method is evaluated by closed-loop simulations with a non-linear helicopter model. Additional hardware-in-the-loop simulations are conducted to examine the real-time capability of the approach.

Safety-Critical Receding-Horizon Planning and Formation Control of Autonomous Surface Vehicles via Collaborative Neurodynamic Optimization.

This article addresses the safety-critical receding-horizon planning and formation control of autonomous surface vehicles (ASVs) in the presence of model uncertainties, environmental disturbances, as well as stationary and moving obstacles. A three-level formation control architecture is proposed with a safety-critical formation trajectory generation module at its high level, a collision-free guidance module at its middle level, and an anti-disturbance control module at its low level. Specifically, a safety-critical formation trajectory generator is designed by leveraging collaborative neurodynamic optimization to plan safe formation trajectories to track a given trajectory and avoid stationary obstacles in a receding-horizon manner. Based on control barrier functions, a collision-free line-of-sight guidance law is developed to generate safe guidance commands to avoid collision with moving obstacles and other vehicles. An anti-disturbance control law is customized with a finite-time convergent observer for a vehicle to follow the guidance command signals. Simulation and hardware-in-the-loop experimental results are elaborated to validate the efficacy of the proposed method for the receding-horizon planning and formation control of ASVs.

Robust collision-free guidance and control for fully actuated multirotor aerial vehicles

Robust collision-free guidance and control for fully actuated multirotor aerial vehicles

Robust Collision-Free Guidance and Control for Underactuated Multirotor Aerial Vehicles

This paper is concerned with the robust collision-free guidance and control of underactuated multirotor aerial vehicles in the presence of moving obstacles capable of accelerating, linear velocity and rotor thrust constraints, and matched model uncertainties and disturbances. We address this problem by using a hierarchical flight control architecture composed of a supervisory outer-loop guidance module and an inner-loop stabilizing control one. The inner loop is designed using a typical hierarchical control scheme that nests the attitude control loop inside the position one. The effectiveness of this scheme relies on proper time-scale separation (TSS) between the closed-loop (faster) rotational and (slower) translational dynamics, which is not straightforward to enforce in practice. However, by combining an integral sliding mode attitude control law, which guarantees instantaneous tracking of the attitude commands, with a smooth and robust position control one, we enforce, by construction, the satisfaction of the TSS, thus avoiding the loss of robustness and use of a dull trial-and-error tweak of gains. On the other hand, the outer-loop guidance is built upon the continuous-control-obstacles method, which is incremented to respect the velocity and actuator constraints and avoid multiple moving obstacles that can accelerate. The overall method is evaluated using a numerical Monte Carlo simulation and is shown to be effective in providing satisfactory tracking performance, collision-free guidance, and the satisfaction of linear velocity and actuator constraints.

Centroidal Voronoi Tessellation and Model Predictive Control–Based Macro-Micro Trajectory Optimization of Microsatellite Swarm

Probabilistic swarm guidance enables autonomous microsatellites to generate their individual trajectories independently so that the entire swarm converges to the desired distribution shape. However, it is essential to avoid crowding for reducing the possibility of collisions between microsatellites. To determine the collision-free guidance trajectory of each microsatellite from the current position to the target space, a collision avoidance algorithm is necessary. A synthesis method is proposed that generate the collision avoidance trajectories. The idea is that the trajectory planning is divided into macro-planning and micro-planning; macro-planning guides where the microsatellites move step by step from the initial cube to the target cube by probabilistic swarm guidance with Centroidal Voronoi tessellation, while the micro-planning is to generate the optimal path for each step and finally reach the specified position in the target cube by model predictive control. Simulation results are presented for the collision-free guidance trajectory of microsatellites to verify the benefits of this planning scheme.

Hierarchical Collaboration Approach for Tracking of Flying Target by UAVs

This paper investigates a hierarchical target tracking method based on the collaboration of unmanned aerial vehicles (UAVs) at different altitudes. To enhance the target tracking range, the high-altitude UAVs monitor the wide area, and transmit their surveillance information to low-altitude UAVs which directly detect and collect information about the target's movements. The contributions of this paper are threefold: First, to track the flying target in a dynamic environment, a modified Lyapunov guidance vector field (LGVF) method is used to plan velocities for UAVs, where a time-varying vertical component is incorporated into the traditional LGVF function to satisfy the constraints of cluster communication. Secondly, a three-dimensional local collision-free guidance vector field (TLCGVF) method is proposed for UAVs to plan collision-free paths on line. To simultaneously track the target and avoid obstacles, the vector field by LGVF is used as the original vector field of TLCGVF. Thirdly, the rolling optimization strategy is used to adjust the reactive parameters of TLCGVF to enhance the path quality. The simulation results confirm the feasibility of the above approach.

Extended-State-Observer-Based Collision-Free Guidance Law for Target Tracking of Autonomous Surface Vehicles with Unknown Target Dynamics

This paper is concerned with the target tracking problem of an autonomous surface vehicle in the presence of a maneuvering target. The velocity information of target is totally unknown to the follower vehicle, and only the relative distance and angle between the target and follower are obtained. First, a reduced-order extended state observer is used to estimate the unknown relative dynamics due to the unavailable velocity of the target. Based on the reduced-order extended state observer, an antidisturbance guidance law for target tracking is designed. The input-to-state stability of the closed-loop target tracking guidance system is analyzed via cascade theory. Furthermore, the above result is extended to the case that collisions between the target and leader are avoided during tracking, and a collision-free target tracking guidance law is developed. The main feature of the proposed guidance law is twofold. First, the target tracking can be achieved without using the velocity information of the target. Second, collision avoidance can be achieved during target tracking. Simulation results show the effectiveness of the proposed antidisturbance guidance law for tracking a maneuvering target with the arbitrary bounded velocity.

Guidance Control for Fleet of Small Aerial Robots with Communication Restriction

Abstract In this paper, collision-free guidance control of multiple small aerial robots is designed. Collision avoidance should be considered in the operation of a fleet of aerial robots. A guidance control system using a distributed model predictive control (DMPC) is proposed to realize the collision avoidance. A constraint of the relative position vector between each robot is considered in the design for efficient avoidance. Small multi-rotor helicopter is considered as controlled object, and the guidance control system is designed by using the translational model of the helicopter. The robots exchange information about current state and optimal input sequence each other for calculating the predictive trajectory of others. Based on the calculated trajectories, each helicopter solves its local optimization problem. Here, sharing the velocity information is difficult because calculation processing capability of the small sensor and communication capability among the robots are strictly restricted. Therefore, a dynamic compensator for velocity compensation is introduced. By introducing the dynamic compensator, collision avoidance using only exchange on the position and the input sequence information is accomplished without exchanging velocity information. The effectiveness of the proposed control system is verified by numerical simulations and flight experiment.

Shared Control Design of a Walking-Assistant Robot

This brief presents a control design for a walking-assistant robot in a complex indoor environment, such that it can assist a walking-impaired person to walk and avoid unexpected obstacles. In this design, the robot motion is a resultant of autonomous navigation and compliant motion control. The compliance motion controller allows the robot to possess passive behavior following the motion intent of the user, while the autonomous guidance gives safe navigation of the robot without colliding with any obstacles. A shared-control approach is suggested to combine the passive compliant behavior and safe guidance of the robot. When a user exerts force to the robot, the mobile platform responds to adjust the speed in compliance with the user movement. On the other hand, the autonomous navigation controller is designed to provide collision-free guidance. Using the developed shared controller, outputs of the compliance motion controller and autonomous navigation controller are fused to generate appropriate motion for the robot. In this manner, passive behavior allows the walking-assistant robot to adapt to a user’s motion intent and move in compliance with user. Meanwhile, the active guidance adjusts the linear velocity and the direction of the robot in real time in response to the environmental data received from the on-board laser scanner. The developed algorithms have been implemented on a self-constructed walking-assistant robot. Experimental results validate the proposed design and demonstrate that the robot can actively avoid unexpected obstacles while move passively following the user to the destination.

Collision-free guidance control of multiple small UAVs based on distributed model predictive control

Collision-free guidance control of multiple small UAVs based on distributed model predictive control

Collision-Free Guidance Control of Small Unmanned Helicopter Using Nonlinear Model Predictive Control

In this study, our aim is to realize collision-free guidance control for a small unmanned helicopter. The simultaneous flight of multiple small unmanned helicopters has recently attracted considera...

1C24 Collision-free Guidance Control of Multiple UAVs with Restricted Communication Networks(The 12th International Conference on Motion and Vibration Control)

1C24 Collision-free Guidance Control of Multiple UAVs with Restricted Communication Networks(The 12th International Conference on Motion and Vibration Control)