Collision Avoidance Constraints Research Articles

Analytic solution for combined in-plane and out-of-plane spacecraft formation reconfiguration with passive collision avoidance

Passive collision avoidance mechanism are effective for ensuring the safety of spacecraft formations during the reconfiguration process. This paper investigates the combined in-plane and out-of-plane formation reconfiguration, while incorporating passive collision avoidance constraints. We design a low-collision-risk impulsive maneuver sequence using the relative Eccentricity/Inclination (E/I) vectors theory and conduct a comprehensive passive safety analysis. Furthermore, we introduce an analytic, passively collision-free maneuver strategy by deriving constraint-satisfying solutions for the relative E/I vectors reconfiguration path. For the first time, our approach achieves theoretical completeness in passive collision avoidance, significantly enhancing the safety of transition configurations compared with existing analytic solutions. Finally, simulations results show that our approach offers substantial improvements in reconfiguration passive safety while incurring negligible additional fuel consumption in comparison to existing solution.

Trajectory reconstruction method for spacecraft formation considering collision probability constraints

Abstract This paper introduces a trajectory reconstruction algorithm for spacecraft formation, combining collision probability and sequential convex programming algorithms. When reconstructing the trajectory of spacecraft formation, external collision avoidance constraint is added to avoid collision threats from space objects. The constraint accuracy of the external collision avoidance constraints of the BOX method and the collision probability method is analyzed. The simulation results indicate that the collision probability method has higher accuracy compared to the BOX method. It addresses the issue of inaccurate division of collision avoidance zones in the BOX method, which tends to be aggressive in the velocity direction and conservative in other directions. A reference is provided for trajectory reconstruction when formation satellites encounter non-cooperative targets.

Path tracking with switchable stability constraints for vehicle collision avoidance based on extended stability region

There is a certain conflict between vehicle stability and the performance of path-tracking under emergency conditions. In this paper, to make full use of the lateral capacity of vehicles, a path tracking method with switchable stability constraints is proposed. First, using the Lyapunov energy equation and Hurwitz’s theorem, the lateral stability region (SR) and the critical steering angle of steady steering at different longitudinal speeds are calculated. Based on this, the extended stability region (ESR) and the extended critical steering angle of the vehicle under the control of an additional yaw moment are obtained. Furthermore, the driving area during collision avoidance is divided into unsafe and safe areas, and based on the model predictive control strategy, a path-tracking controller with switchable stability constraints is established. In the unsafe area, the vehicle states are allowed to break through the SR temporarily, and to ensure path-tracking accuracy, the ESR is taken as the stability constraint. In the safe area, the stability constraint is switched to the SR, and to make the vehicle states quickly return to SR, a regain stability controller is established. Finally, a Simulink-CarSim simulation and a hardware-in-the-loop (HIL) experiment are conducted. The simulation results show that a balance between tracking effect and vehicle stability can be effectively achieved through the proposed path-tracking system under the investigated emergency conditions. The real-time performance of the proposed control strategy is validated through the HIL experiment.

Federated deep reinforcement learning based trajectory design for UAV-assisted networks with mobile ground devices

Exploiting highly maneuverable unmanned aerial vehicles (UAVs) has been considered as an efficient way to assist wireless systems, e.g., for applications of data collection. However, several challenges remain to be addressed in the design of such UAV-assisted networks, including multi-UAV joint trajectory determination, data privacy protection, and adaption to the complex channel environment particularly with mobile ground devices (GDs). In this paper, we study a multi-UAV assisted data collection system where UAVs collect data locally from mobile GDs. The aim is to minimize the whole operation time cost via jointly optimizing the UAVs’ three-dimensional (3D) trajectory together with the GDs’ communication scheduling, while satisfying the constraints of no-fly zones (NFZs) and collision avoidance. With a nonconvex feasible set (due to the NFZs), the established problem is nonconvex. Moreover, the randomness of GDs movements significantly reduces the performance of a typical redesign, i.e., determining the UAVs’ trajectory and users’ scheduling before starting the data collection task. To tackle these issues, we first transform the established problem into a Markov decision one, and then propose a multi-agent federated reinforcement learning (MAFRL)-based approach to optimize the dynamic long-term objective via jointly determining UAVs’ 3D trajectory and GD’s communication scheduling. A multi-step propagation technique and a dueling network architecture are adopted to enhance the neural network utilized to train agents, i.e., to accelerate the convergence rate of the proposed method and improve its overall stability. Finally, experimental results reveal the effectiveness of our proposed method in the considered practical scenario.

Differential Game-Based Cooperative Interception Guidance Law with Collision Avoidance

To deal with the offense-defense confrontation problem of multi-missile cooperative intercepting a high-speed and large-maneuvering target, a differential game-based cooperative interception guidance law with collision avoidance is proposed, in which the offense-defense parties are the incoming target and the interceptors, respectively. Given that both offense-defense parties have uniformly decreasing speeds and first-order biproper dynamics, the relative motion models among the offense-defense parties are established, and the performance indices of the target and the interceptors are proposed. After that, the cooperative interception guidance law with collision avoidance is derived based on a differential game. The guidance law considers the effects of speed variations and rudder layouts on the motions of both offense-defense parties, ensuring excellent algorithmic real-time property and interception accuracy while introducing inter-missile collision avoidance constraints. In addition, the parameters of the target performance index are set according to the target acceleration information estimated by the interceptors. The simulation results verify the effectiveness of the guidance law designed in this paper, under various three-to-one scenarios, the interceptors could achieve collision-free interceptions with the interception accuracy of less than 5 m and the interception time difference of less than 0.1 s.

Robust Stochastic Model Predictive Control for Autonomous Vehicle Motion Planning

This work presents a Robust Stochastic Model Predictive Control (RSMPC) framework for real-time motion planning autonomous vehicles, addressing the complex multi-modal vehicle interactions. The proposed framework involves adding expert policy from observations to the dataset and applying the Data Aggregation (DAgger) method to filter unsafe demonstrations and resolve expert conflicts. A Dual-Stage Attention-based Recurrent Neural Network (DA-RNN) model is integrated to predict dual class variables from the dataset, producing a set containing constraints collision-avoidance predicted to be active. The RSMPC framework enhances formulation optimization by eliminating irrelevant collision avoidance constraints, resulting in faster control signals. The framework is applied iteratively, continuously updating observations and solving the RSMPC optimization formulation in real-time. Evaluation of the DA-RNN model achieved a recall value of 0.97 and a high accuracy rate of 98.1% in predicting dual interactions, with a minimal false negative rate of 0.026, highlighting its effectiveness in capturing interaction intricacies. Validated through simulations of interactive traffic intersections, the proposed framework demonstrably excels, showing high feasibility of 99.84% and a 15-fold increase in response speed compared to the baseline. This approach ensures autonomous vehicles navigate safely and efficiently in complex traffic scenarios, paving the way for more reliable and scalable autonomous driving solutions.

Deep reinforcement learning based online lifting path planning for tower cranes in unknown dynamic environments

Lifting path planning is critical for the safety and efficiency of tower cranes operating in dynamic construction environments. This paper proposes a lifting path planner to efficiently generate safe and smooth lifting paths for tower cranes in an unknown construction environment through a deep reinforcement learning (DRL) method. Based on the Twin-Delayed DDPG (TD3) framework, the planner effectively plans a lifting path within constraints of collision avoidance and operational limitations using the local environmental information measured by lidar. A Long Short-Term Memory network is applied in the planner to handle the dynamic characteristics of the obstacles in the construction sites to ensure that the lifting path is collision-free with dynamic obstacles. A discrete-continuous hybrid action space for tower cranes is proposed to optimize planned lifting paths more suitable for practical engineering operations. Moreover, a novel reward function is introduced to optimize the smoothness of the lifting path, which improves the success rate and optimizes the energy and time cost. A new Hindsight Experience Replay algorithm is proposed to address the reward sparsity problem in lifting path planning, which improves the training speed. Simulation results in Webots platform show the presented method effectively reduces the planning time and achieves better performance on online path planning compared with the existing DRL path planning methods.

An Approach to Near-Optimal Continuous-Thrust Solution for Plane Constellation Deployment

Paving the way in satellite constellation deployment, this article presents the Comprehensive Program (GCP) for planning constellation deployment missions by introducing a near-optimal solution for continuous-thrust maneuvers. The GCP establishes time constraints, enabling appropriate time scheduling and eliminating reconfiguration phases. It considers various cases of satellite departure and arrival sequences, collision avoidance constraints, and time limitations. A near-optimal solution for the continuous-thrust trajectory of each satellite is introduced, relying on the Fourier series approximation of the thrust pointing angle. The Fourier series with constant coefficients replaces the thrust angle in the satellite’s orbital motion equations in polar coordinates. The primary advantage of this near-optimal approach lies in its simplicity in accommodating space perturbations, posing no challenges in problem-solving. Additionally, the approach is beneficial due to its straightforward implementation and simple mathematical computations. This approach does not require pre-determining the revolution number of the transfer trajectory, unlike shape-based methods. The proposed method’s flexibility allows it to adapt and optimize the trajectory without prior assumptions about the number of revolutions required for the mission. Analyses demonstrate the proposed algorithm’s versatility and precision across various scenarios involving different orbital eccentricities, thrust forces, and thrust pointing angles. The constant coefficients of the Fourier series are determined by defining a set of objective functions and minimizing them using the Multi-Objective Particle Swarm Optimization algorithm (MOPSO). The first and second objective functions ensure that the transfer orbit reaches the desired deployment point, while the third and fourth ones guarantee the tangential conditions between the transfer orbit and the final orbit. An additional objective function minimizes the trajectory time. As a perturbation of interest, the effect of Earth’s oblateness is considered.

Constructive Approximate Nash Equilibrium for In-Orbit Target Enclosing with Collision Avoidance and Full-state Constraint via Nonzero-Sum Differential Games

The problem of in-orbit cooperative target enclosing involving N thrust-limited satellites under collision avoidance and maneuver amplitude constraints is studied. In order to find global optimal trajectories for target enclosing task with all constraints above, by integrating the collision threat and maneuver boundaries into a novel nonlinear cost functional, the studied target enclosing problem is described as a nonlinear nonzero-sum differential game. Further, to avoid iterative calculations caused by traditional nonlinear-game-solving methods, an approximate solution which can be constructed directly is designed. Then the approximate Nash equilibrium strategies can be educed by the constructive approximate solution for the proposed nonzero-sum game. Analysis shows that the proposed control strategies can asymptotically approach the exact one and can ensure a zero-error tracking of the enclosing configuration. Simulation results illustrate lower time costs and better enclosing accuracy while the collision avoidance and maneuver amplitude constraints are satisfied simultaneously.

Multi-Channel Cooperative Spectrum Sensing and Scheduling for Cognitive IoT Networks

This paper presents a novel multi-channel cooperative spectrum sensing and scheduling (MC$_2$S$_3$) framework for spectrum and energy harvesting cognitive Internet of Things (IoT) networks. We address the challenge of maximizing network throughput by formulating a combinatorial problem that jointly optimizes the sensing scheduling of primary channels (PCs), the assignment of IoT devices for sensing scheduled PCs, and the clustering and allocation of IoT nodes to efficiently use discovered idle PCs; subject to spectrum utilization and collision avoidance constraints. Recognizing the inherent complexity of the underlying NP-hard mixed-integer non-linear programming (MINLP) problem, we propose a decomposition strategy that decouples the master problem into PC exploration and exploitation sub-problems. In the exploration phase, we derive closed-form solutions for optimal sensing durations and detection thresholds that satisfies spectrum utilization and collision avoidance constraints, which are then used to develop a priority metric to rank PCs. The proposed PC ranking informs a sequential PC scheduling and IoT sensing assignment approach that exploits a linear bottleneck assignment (LBA) strategy, proceeding until further scheduling does not enhance network utility. For the exploitation phase, we leverage a non-orthogonal multiple access (NOMA) strategy to multiplex multiple IoT nodes on a single PC, employing an iterative linear sum assignment (LSA) method for efficient allocation to maximize utilization of idle PCs. Numerical results validate the efficacy of our proposed methodologies, reaching an accuracy of approximately 99% in the order of milliseconds, significantly outperforming time complexity of brute-force benchmarks.

Distributed virtual formation control for railway trains with nonlinear dynamics and collision avoidance constraints

To improve the model accuracy and control efficiency for the movements of a virtual formation, this paper investigates distributed optimal control for the virtual formation control system in railways. Adopting the relative distance braking mode, a coupled optimal control problem with nonlinear train dynamics and constraints regarding collision avoidance and jerk is formulated for the virtual formation. To handle the non-convex constrained problem efficiently, a distributed augmented Lagrangian based alternating direction inexact newton (ALADIN) method under the model predictive control (MPC) framework is developed. For the execution of the distributed computational process, the copied variables are introduced to reformulate the original coupled problem in an objective separable form. By exploiting the problem separability, the ALADIN method decomposes the reformulation into a coordinated quadratic programming problem of small-scale and several local nonlinear programming problems that can be calculated in parallel, thereby facilitating real-time control and relieving communication burden. Numerical experiments on a metro line are carried out to verify the effectiveness of the proposed model and method. Experimental results demonstrate that high-performance tracking control for virtually coupled train units can be achieved in real time.

Sequential Convex Programming for Nonlinear Optimal Control in UAV Trajectory Planning

In this paper, an algorithm is proposed to solve the non-convex optimization problem using sequential convex programming. An approximation method was used to solve the collision avoidance constraint. An iterative approach was utilized to estimate the non-convex constraints, replacing them with their linear approximations. Through the simulation, we can see that this method allows for quadcopters to take off from a given initial position and fly to the desired final position within a specified flight time. It is guaranteed that the quadcopters will not collide with each other in different scenarios.

A method for obtaining the starting set of formation based on IPSO

The formation of intelligent platforms is a multi-objective constraint problem. It is necessary for the multi-agent to automatically generate the path from the initial position to the specified end point, and at the same time meet the constraints of space collision avoidance on the multi-target intersection path and the constraint of the agent’s motion ability, and pursue the shortest formation time of the whole formation as much as possible. In this paper, we propose an improved method for obtaining the starting set of multi-agent formation based on particle swarm optimization. First, the starting point set of the formation is defined, and then the particle swarm optimization algorithm is selected as the optimization algorithm to find the best starting point of the formation. Then the turning performance of the intelligent platform is studied, and the assembly route of each agent is designed by combining the Dubins curve. The simulation results show that the proposed method avoids collision between agents, shortens the time of agent formation assembly, and effectively ensures the reliability of the assembly route.

Multi-UAV Cooperative Coverage Search for Various Regions Based on Differential Evolution Algorithm.

In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality.

Leader-follower and leaderless bipartite formation-consensus over undirected coopetition networks and under proximity and collision-avoidance constraints

We propose a method to control networks involving both cooperative and competitive interactions simultaneously under proximity and collision-avoidance constraints. The control design is of gradient type, using a barrier-Lyapunov function. Then, under the assumption that the network is structurally balanced, we establish asymptotic stability of the leaderless and leader-follower bipartite formation-consensus manifold. We assume that the agents are modelled by second-order integrators, but we also demonstrate the utility of our theoretical findings via numerical simulations on a problem of formation-consensus control of nonholonomic vehicles.

Trajectory planning of a free-floating dual-arm space robot with minimal base disturbance in obstacle environments

This paper addresses the issue of trajectory planning for a free-floating dual-arm space robot, considering the constraints of collision avoidance while simultaneously minimizing the base position and attitude disturbance during on-orbit tasks. One arm is designated as the mission arm, tracking a prescribed path to complete specific goals, while the other serves as the balance arm, compensating for any base attitude disturbance. A collision-free path planning approach is presented for the mission arm, which utilizes a hybrid map, improved artificial potential field (IAPF) and adaptive coupling guidance method. The hybrid map in the mission arm joint space contains the coupling relationship between the deviation of the base position and the motion of the dual arms, along with obstacles transformed from Cartesian space. Then, to address the problem of the goals nonreachable with obstacles nearby (GNRON) of the artificial potential field (APF), a novel repulsive potential field is incorporated into the IAPF. To minimize the base position disturbance, a coupling guidance method is presented that enables the mission arm to move in areas with lower coupling factors on the hybrid map. Finally, simulations are conducted to demonstrate the effectiveness and superiority of the proposed hybrid map and coordinated trajectory planning approach.

Convex distributed robust model predictive control for collision and obstacle avoidance

AbstractThe article proposes a convex distributed robust model predictive control algorithm for collision avoidance in multi‐agent systems with additive disturbances, which utilizes the concept of tube model predictive control to address the disturbances. To tackle the coupled collision avoidance constraints, each agent integrates assumed nominal position and input trajectories from its neighbors, rather than relying on actual ones. Compatibility constraints are then designed using the normal vector of the constructed separating hyperplanes for collision avoidance and the residual collision avoidance margin of the optimal solutions at the last time step to restrict deviations between assumed and actual trajectories and ensure consistency among the agents. The nonconvex collision and obstacle avoidance constraints are convexified using the concept of safe sets, transforming them into time‐varying closed polyhedral constraints while accounting for the impact of disturbances. Particularly, the residual collision avoidance margin is incorporated into the construction of safe sets for the satisfaction of coupled collision avoidance constraints. Consequently, the original collision avoidance optimal control problems can be efficiently and simultaneously solved for all agents using standard quadratic programming techniques. Under the design of local terminal constraint sets for each agent, the proposed algorithm ensures a rigorous analysis of robust constraint satisfaction for collision avoidance constraints, as well as an assessment of recursive feasibility and closed‐loop stability. The effectiveness of the algorithm is demonstrated through an illustrative example, and simulation results validate its ability to successfully achieve collision and obstacle avoidance even in the presence of disturbances.

Collision Avoiding Max-Sum for Mobile Sensor Teams

Recent advances in technology have large teams of robots with limited computation skills work together in order to achieve a common goal. Their personal actions need to contribute to the joint effort, however, they also must assure that they do not harm the efforts of the other members of the team, e.g., as a result of collisions. We focus on the distributed target coverage problem, in which the team must cooperate in order to maximize utility from sensed targets, while avoiding collisions with other agents. State of the art solutions focus on the distributed optimization of the coverage task in the team level, while neglecting to consider collision avoidance, which could have far reaching consequences on the overall performance. Therefore, we propose CAMS: a collision-avoiding version of the Max-sum algorithm, for solving problems including mobile sensors. In CAMS, a factor-graph that includes two types of constraints (represented by function-nodes) is being iteratively generated and solved. The first type represents the task-related requirements, and the second represents collision avoidance constraints. We prove that consistent beliefs are sent by target representing function-nodes during the run of the algorithm, and identify factor-graph structures on which CAMS is guaranteed to converge to an optimal (collision-free) solution. We present an experimental evaluation in extensive simulations, showing that CAMS produces high quality collision-free coverage also in large and complex scenarios. We further present evidence from experiments in a real multi-robot system that CAMS outperforms the state of the art in terms of convergence time.

Iterative distributed model predictive control for nonlinear systems with coupled non‐convex constraints and costs

AbstractThis paper proposes a distributed model predictive control (DMPC) algorithm for dynamic decoupled discrete‐time nonlinear systems subject to nonlinear (maybe non‐convex) coupled constraints and costs. Solving the resulting nonlinear optimal control problem (OCP) using a DMPC algorithm that is fully distributed, termination‐flexible, and recursively feasible for nonlinear systems with coupled constraints and costs remains an open problem. To address this, we propose a fully distributed and globally convergence‐guaranteed framework called inexact distributed sequential quadratic programming (IDSQP) for solving the OCP at each time step. Specifically, the proposed IDSQP framework has the following advantages: (i) it uses a distributed dual fast gradient approach for solving inner quadratic programming problems, enabling fully distributed execution; (ii) it can handle the adverse effects of inexact (insufficient) calculation of each internal quadratic programming problem caused by early termination of iterations, thereby saving computational time; and (iii) it employs distributed globalization techniques to eliminate the need for an initial guess of the solution. Under reasonable assumptions, the proposed DMPC algorithm ensures the recursive feasibility and stability of the entire closed‐loop system. We conduct simulation experiments on multi‐agent formation control with non‐convex collision avoidance constraints and compare the results against several benchmarks to verify the performance of the proposed DMPC method.

Advancing spacecraft rendezvous and docking through safety reinforcement learning and ubiquitous learning principles

As spacecraft rendezvous and docking missions become increasingly complex, the need for advanced solutions has surged. In recent years, the application of reinforcement learning techniques to tackle spacecraft rendezvous guidance challenges has emerged as a prominent international trend. Vital to ensuring the secure rendezvous and docking of spacecraft is the task of obstacle avoidance. However, traditional reinforcement learning algorithms lack the ability to enforce safety constraints within the exploration space, which presents a formidable obstacle in the design of spacecraft rendezvous guidance strategies. In response to this challenge, a spacecraft rendezvous guidance methodology founded on safety reinforcement learning is proposed. Firstly, a Markov model is crafted for autonomous spacecraft rendezvous in scenarios involving collision avoidance. A reward system, contingent on obstacle warnings and collision avoidance constraints, is introduced to establish a safety reinforcement learning framework for devising spacecraft rendezvous guidance strategies. Secondly, within the framework of safety reinforcement learning, two deep reinforcement learning (DRL) algorithms, Proximal Policy Optimisation (PPO) and Deep Deterministic Policy Gradient (DDPG), are leveraged to generate these guidance strategies. Experimental findings validate the effectiveness of this approach in successfully executing obstacle avoidance and achieving rendezvous with remarkable precision. Furthermore, through an analysis of the performance and generalization capabilities of these two algorithms, the efficacy of the proposed methodology is further underscored. This fusion of advanced space guidance technology with the principles of Ubiquitous Learning marks a significant step forward in the quest for safer and more efficient spacecraft rendezvous and docking operations.