Obstacle Avoidance Constraints Research Articles

An enlarged polygon method without binary variables for obstacle avoidance trajectory optimization

In this article, an Enlarged Polygon/Polyhedron (ELP) method without binary variables is proposed to represent the Convex Polygonal/Polyhedral Obstacle Avoidance (CPOA) constraints in trajectory optimization. First, the equivalent condition of a point outside the convex set is given and proved rigorously. Then, the ELP condition describing the CPOA constraints equivalently is given without introducing binary variables, and its geometric meaning is explained. Finally, the ELP method is used to transform the CPOA trajectory optimization problem into an optimal control problem without binary variables. The effectiveness and validity of ELP method are demonstrated through simulations with both simple linear dynamic model (unmanned aerial vehicle) and complex nonlinear dynamic model (hypersonic glide vehicle). Comparison indicates the computational time of ELP method is only 1%-20% of that of the traditional Mixed-Integer Programming (MIP) method.

Trajectory planning for satellite cluster reconfigurations with sequential convex programming method

This paper presents trajectory planning algorithms for large-scale satellite clusters reconfiguration based on sequential convex programming, and these algorithms consider fuel consumption and collision avoidance. Firstly, the trajectory planning problem is formulated as a nonconvex optimal control problem with nonlinear dynamics and nonconvex path constraints. Secondly, the original nonlinear continuous optimal control problem is transformed into a discrete convex optimization subproblem through linearization and discretization. Collision avoidance strategy between discrete points and obstacle avoidance constraints are considered in the convex subproblem to ensure that the trajectories are collision-free. Thirdly, coupled and decoupled sequential convex programming methods are proposed for rapidly generating collision-free and fuel-efficient trajectories. Finally, comparative numerical simulations are presented to demonstrate that as the number of satellites increases, 94 to 99 percent performance improvement over the pseudo-spectral method is achieved in terms of computational efficiency.

Integrated Post-Impact Planning and Active Safety Control for Autonomous Vehicles

How to reduce traffic accidents and improve vehicle safety has always aroused widespread concern. Secondary and serial collision accidents can result in more serious hazards as the initial collision can easily destabilize a high-speed vehicle and the driver may fail to maintain effective control due to panic. With this in mind, a planning-integrated active safety control system is developed for post-impact vehicles to avoid subsequent accidents. The vehicle dynamics model is established considering the roll degree of freedom. The constraint equivalent methods based on the octagon and rhombus envelopes are proposed to linearize the road adhesion constraint and the obstacle avoidance constraint, respectively. A planning-integrated model predictive controller (MPC) is developed for post-impact vehicles to achieve the coordination of stability recovery and the avoidance of secondary collision with surrounding vehicles. In the meantime, an obstacle avoidance decision strategy based on the safe braking distance is designed to cope with different traffic scenes. Furthermore, a control allocator based on particle swarm optimization is developed to achieve the high-efficiency allocation of the resultant control output of MPC under post-impact extreme conditions. The proposed scheme is verified under comprehensive driving scenarios through hardware-in-loop tests.

Collaborative pursuit-evasion game of multi-UAVs based on Apollonius circle in the environment with obstacle

In the game of a two-dimensional plane with a circular obstacle, a strategy of multi-pursuers and a single evader is studied. All the agents are Unmanned Aerial Vehicle and the evader is faster than the pursuers. The collaborative pursuit-evasion mission is divided into two stages: the encirclement stage and pursuit-evasion stage. When pursuers fly to the mission area from a distance, the formation and obstacle avoidance method based on leader-follower mode is designed for the encirclement process. In the pursuit-evasion stage, considering obstacle avoidance and manoeuver constraint, a collaborative strategy combining Apollonius circle algorithm and geometric algorithm is proposed, and the condition for the successful capture of a single superior evader is analysed. In the simulation, the influence of obstacle size and manoeuver constraint is compared and analysed in detail, which indicates that they will increase the capture time even changes the result of the game in the limited map. In addition, the complexity of the algorithm is M times higher than that of the obstacle-free case ( M is the number of obstacles in the map).

Accurate Pseudospectral Optimization of Nonlinear Model Predictive Control for High-Performance Motion Planning

Nonlinear Model Predictive Control (NMPC) is an effective method for motion planning of automated vehicles, but the high computational load of numerical optimization limits its practical application. This paper designs an NMPC based motion planner and presents two techniques, named adaptive Lagrange discretization and hybrid obstacle avoidance constraints, to accelerate the numerical optimization by reducing the optimization variables and simplifying the non-convex constraints. Given the high nonlinearity of vehicle dynamics, the Lagrange interpolation is adopted to convert the state equation of vehicle dynamics and the objective function to ensure a preset accuracy but with less discretization points. An adaptive strategy is then designed to adjust the order of Lagrange polynomials based on the distribution of discretization error. Moreover, a hybrid strategy is presented to construct the constraints for obstacle avoidance by combing the elliptic and linear time-varying methods together. It can ensure driving safety and also make a good balance between computing load and accuracy. The performance of these techniques on accelerating the NMPC based motion planner is validated and analyzed by comparative numerical simulations and experimental tests under various scenarios. Compared with traditional methods, the results show that these techniques improve accuracy and efficiency by 74% and 60%, respectively.

Decentralized motion planning for intelligent bus platoon based on hierarchical optimization framework

Intelligent bus platoon effectively meets the high demand for public transport during rush hours. A decentralized motion planning method based on hierarchical optimization framework is proposed for intelligent bus platoon to improve flexibility and efficiency. This method realizes the optimization of the guidance trajectory in the higher layer, and the optimization of the motion trajectory in the lower layer. First, a guidance trajectories generation method is presented to decouple the platoon motion planning. The state and trajectory of the preceding bus are used as a reference to constrain the lateral and longitudinal feasible space for the current bus. Meanwhile, the quintic polynomial is adopted to generate the trajectory cluster and the optimal trajectory is selected using the multi-objective optimization. Second, the joint kinematics model of the bus is built as the prediction model. And independent model predictive control modules are utilized to optimize each guidance trajectory to make them smoother and safer. The obstacle avoidance constraints are reconstructed based on the duality theorem. Finally, simulations demonstrate the advantages of the decentralized motion planning method under different scenarios, and results demonstrate our method improves the flexibility and consistency of the platoon.

Simulation Diagram of Unmanned Vehicle Driving from Current Position to Optimal Parking Space Based on A* Algorithm

To draw the simulation diagram of the unmanned vehicle driving from the current position to the optimal parking space, the parking space with the shortest distance from the starting point to the control point is defined as the optimal point in this paper. We simplify the driving route into a straight line and establish the coordinate axes. Depending on the driving direction, the driving trajectory can be divided into different cases. In each case, the order of the parking spaces passed by the vehicle is fixed. Therefore, if the parking space is occupied, its value is assigned as 0; otherwise, it is 1. The initial value is assigned as 0, and we accumulate the weighted value in each case. When the total weight reaches 1, accumulation stops, and the optimal parking space in this case is recorded. The optimal parking space is in which the vehicle travels the shortest distance among all the cases. Based on the A* algorithm, a raster map is constructed. The obstacle area is expanded by narrowing the feasible area range of control points in the parking lot so that the path of control points can meet the kinematic and obstacle avoidance constraints of vehicles. Finally, a simulation diagram of the parking trajectory is drawn.

Motion Route Planning and Obstacle Avoidance Method for Mobile Robot Based on Deep Learning

In order to improve the motion route planning effect and obstacle avoidance effect of mobile robots, this paper combines the deep learning theory to analyze the motion route planning and obstacle avoidance process of mobile robots. According to the obstacle avoidance trajectory and constraints, this paper establishes a safe distance model for obstacle avoidance, then analyzes the braking process of the robot, and designs an improved safety model for obstacle avoidance. This model integrates two relatively mature safety models, complements their advantages and disadvantages, and comprehensively considers robot safety and the utilization of the motion path. According to the simulation test research, the robot based on deep learning proposed in this paper has a good motion route planning effect and obstacle avoidance effect and can effectively improve the autonomous motion effect of the robot.

Trajectory Optimization with Complex Obstacle Avoidance Constraints via Homotopy Network Sequential Convex Programming

Space vehicles’ real-time trajectory optimization is the key to future automatic guidance. Still, the current sequential convex programming (SCP) method suffers from a low convergence rate and poor real-time performance when dealing with complex obstacle avoidance constraints (OACs). Given the above challenges, this work combines homotopy and neural network techniques with SCP to propose an innovative algorithm. Firstly, a neural network was used to fit the minimum signed distance field at obstacles’ different “growth” states to represent the OACs. Then, the network was embedded with the SCP framework, thus smoothly transforming the OACs from simple to complex. Numerical simulations showed that the proposed algorithm can efficiently deal with trajectory optimization under complex OACs such as a “maze”, and the algorithm has a high convergence rate and flexible extensibility.

Null-Space-Based Multi-Player Pursuit-Evasion Games Using Minimum and Maximum Approximation Functions

In this article, pursuit and evasion policies are developed for multi-player pursuit–evasion games, while obstacle avoidance and velocity constraints are considered simultaneously. As minimum and maximum approximation functions are both differentiable, pursuit and evasion objectives can be transformed into solving the corresponding differential expressions. For obstacle avoidance, a modified null-space-based approach is designed, which can ensure that all pursuers and evaders of pursuit–evasions are safe to minimize pursuit objective and maximize evasion objective, respectively. Rigorous theoretical analyses are provided to design constrained pursuit and evasion policies with obstacle avoidance. Finally, the performance of proposed policies is demonstrated by simulation results in 3-dimensional space.

Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization. A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations. The feasibility of the formulated optimization problem is guaranteed under derived conditions. The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure. Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.

Configuration-aware model predictive motion planning for Tractor–Trailer Mobile Robot

ABSTRACT This study proposes a motion planning method with strict obstacle avoidance constraints among polygonal-shaped objects for a tractor–trailer mobile robot. The conventional method generally expresses collision-free constraints by approximating a robot as one circle or several circles or ellipsoids. However, accomplishing moving tasks with minimum margins is desirable, with improved efficiency of space availability when multiple robots operate in one area such as a factory, restaurant, office, or warehouse. The target of this study is a tractor–trailer mobile robot that can transport more goods efficiently, while significantly reducing margins during movement. To control the target, multiple convex polygonal objects expressing the mobile robot and surroundings are transformed into linear simultaneous inequalities using ‘Farkas’ lemma.' Embedding inequalities into model predictive control yields a motion planner that allows tractor–trailer mobile robot movement and possesses non-holonomic constraints. The proposed model is validated through simulation and real machine experiments.

EMPC with adaptive APF of obstacle avoidance and trajectory tracking for autonomous electric vehicles

In this paper, event-triggered model predictive control (EMPC) with adaptive artificial potential field (APF) is designed to realize obstacle avoidance and trajectory tracking for autonomous electric vehicles. An adaptive APF cost function is added to achieve obstacle avoidance and guarantee stability. The optimization problem for MPC is feasible by considering a special obstacle avoidance constraint. An event-triggered mechanism is proposed to reduce computational burden and ensure effectiveness of obstacle avoidance. Input and state constraints of autonomous electric vehicles are considered in both feasibility and stability by a robust terminal set. Effectiveness of both obstacle avoidance and trajectory tracking is shown by experimental results on autonomous electric vehicles.

Robot motion planning with orientational constraints based on offline sampling datasets

Currently available algorithms developed for planning robot motion paths in configuration spaces with both obstacle avoidance constraints and end-effector pose constraints suffer from low sampling rates, long computation times, and requirements for specific manipulator structures. The present work addresses this issue by developing an improved sampling-type motion planning algorithm based on a modified form of the rapidly-exploring random tree (RRT) algorithm. The sampling process is based on the standard premise that the constrained manifold is continuous within a particular range. Therefore, configurations that meet the specific constraints associated with a proscribed motion planning task are sampled in advance, and those configurations meeting the constraints are stored in an offline configuration dataset that is employed exclusively in the RRT process. Accordingly, the proposed algorithm facilitates a greatly streamlined motion planning process for manipulators with high degrees of freedom. The computational speed and planning precision of the algorithm are further enhanced by introducing a target bias mechanism and applying an adaptive mechanism to improve the obstacle collision detection performance. The high motion-planning performance of the proposed algorithm is verified by comparisons with the performances of other state-of-the-art RRT-based algorithms based on numerical simulations.

Reactive Navigation of an Unmanned Aerial Vehicle With Perception-Based Obstacle Avoidance Constraints

In this article, we propose a reactive constrained navigation scheme, with embedded obstacles avoidance for an unmanned aerial vehicle (UAV), for enabling navigation in obstacle-dense environments. The proposed navigation architecture is based on a nonlinear model predictive controller (NMPC) and utilizes an onboard 2-D LiDAR to detect obstacles and translate online the key geometric information of the environment into parametric constraints for the NMPC that constrain the available position space for the UAV. This article focuses also on the real-world implementation and experimental validation of the proposed reactive navigation scheme, and it is applied in multiple challenging laboratory experiments, where we also conduct comparisons with relevant methods of reactive obstacle avoidance. The solver utilized in the proposed approach is the optimization engine (OpEn) and the proximal averaged Newton for optimal control (PANOC) algorithm, where a penalty method is applied to properly consider obstacles and input constraints during the navigation task. The proposed novel scheme allows for fast solutions while using limited onboard computational power, which is a required feature for the overall closed-loop performance of a UAV and is applied in multiple real-time scenarios. The combination of built-in obstacle avoidance and real-time applicability makes the proposed reactive constrained navigation scheme an elegant framework for UAVs that is able to perform fast nonlinear control, local path planning, and obstacle avoidance, all embedded in the control layer.

Vector Trajectory Method for Obstacle Avoidance Constrained Planetary Landing Trajectory Optimization

Considering the requirement of avoiding obstacles in different planetary landing missions, this article presents a generalized method dealing with the obstacle avoidance constrained landing trajectory optimization problem with vectorized strategy, i.e., vector trajectory method (VTM). By introducing a trajectory direction constraint with vectorized description, the character of the obstacle avoidance trajectory can be more specifically depicted. Moreover, by satisfying proper trajectory direction constraint, the lander’s particular performances, such as the observation to the target or probability of a safe flight, can be improved. To this end, the VTM and relevant theorem are first developed, revealing the relationship between the two aspects of obstacle avoidance constraint (i.e., distance constraint and trajectory direction constraint). Then, both aspects of constraints are uniformly transformed as the auxiliary angle magnitude constraint with the vectorized strategy, which significantly simplifies the solving procedure of obstacle avoidance constrained trajectory optimization problem. Finally, the proposed VTM is illustrated in detail through the examples with the planetary landing background of atmospheric entry and powered descent landing.

Real-Time Fuel Optimization and Guidance for Spacecraft Rendezvous and Docking

Autonomous rendezvous and docking (RVD) fuel optimization with field-of-view and obstacle avoidance constraints is a nonlinear and nonconvex optimization problem, making it computationally intensive for onboard computation on CubeSats. This paper proposes an RVD fuel optimization and guidance technique suitable for onboard computation on CubeSats, considering the shape, size and computational limitations of CubeSats. The computation time is reduced by dividing the guidance problem into separate orbit and attitude guidance problems, formulating the orbit guidance problem as a convex optimization problem by considering the CubeSat shape, and then solving the orbit guidance problem with a convex optimization solver and the attitude guidance problem analytically by exploiting the attitude geometry. The performance of the proposed guidance method is demonstrated through simulations, and the results are compared with those of conventional methods that perform orbit guidance optimization with attitude quaternion feedback control. The proposed method shows better performance, in terms of fuel efficiency, than conventional methods.

Direct-Adaptive Nonlinear MPC for Spacecraft Near Asteroids

In this work, we propose a novel controller based on a simple adaptive controller methodology and model predictive control (MPC) to generate and track trajectories of a spacecraft in the vicinity of asteroids. The control formulation is based on using adaptive control as a feedback controller and MPC as a feed-forward controller. The spacecraft system model, asteroid shape and inertia are assumed to be unknown, with the exception of the estimated total mass and angular velocity of the asteroid. The MPC is used to generate feed-forward trajectories and control input using only the mass and angular velocity of the asteroid combined with obstacle avoidance constraints. However, since the control input from MPC is calculated using only an approximated model of the asteroid, it fails to control the spacecraft in the presence of disturbances due to the asteroid’s irregular gravitational field. Hence, we propose an adaptive controller in conjunction with MPC to handle unknown disturbances. The numerical results presented in this work show that the novel control system is able to handle unknown disturbances while generating and tracking sub-optimal trajectories better than adaptive control or MPC solely.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

An optimal control-based path planning method for unmanned surface vehicles in complex environments

This paper proposes an optimal control-based method named the rapid-Gauss pseudospectral method (RGPM) to obtain a smooth time-optimal path for unmanned surface vehicles (USVs). It is a general global path planning method, improving the Gauss pseudospectral method (GPM) which cannot be applied to complex ocean scenarios. In addition, we optimize the representation of obstacles and add a Maklink algorithm to reduce the computaitonal cost. More specifically, a unified obstacle modeling approach is first introduced, then a continuous-time-optimal-control path planning problem considering USV kinematics is formulated. The orthogonal collocation method is used to discretize the continuous-time-optimal control problem into a nonlinear programming (NLP) problem. Furthermore, we introduce a mesh refinement technique to achieve collision avoidance between adjacent discrete points. To enhance computational efficiency, an initialization and iteration approach is further proposed. Herein, a MAKLINK graph is established to obtain a suboptimal path as the initial guess for the NLP problem. Obstacle-avoidance constraints are iteratively updated according to whether or not there are collisions in the current path. The proposed method was compared with the traditional GPM under two different scenarios. The simulation results show that the proposed method is superior to the traditional GPM in terms of path quality and convergence speed and can obtain quasi-time-optimal paths under complex conditions simultaneously.