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The paper at hand proposes a real-time capable approach to combined trajectory planning and control. One single prediction model is used to plan a feasible trajectory and to perform lateral guidance of the vehicle at the same time. Nonlinear model predictive control (NMPC) methods are applied to solve the optimal control problem, which incorporates environmental constraints leading to a model predictive...
This paper considers the problem of optimal trajectory generation for autonomous driving under both continuous and logical constraints. Classical approaches based on continuous optimization formulate the trajectory generation problem as a nonlinear program, in which vehicle dynamics and obstacle avoidance requirements are enforced as nonlinear equality and inequality constraints. In general, gradient-based...
Automated driving is a safety critical process, which requires complex decision making. In order to validate driving decisions, it is possible to maintain at all times a contingency maneuver, which transfers the vehicle to a safe standstill, if other decision making processes fail. In this paper we present a motion planner, which computes contingency maneuvers for an automated vehicle in a 0.1[s]...
This paper presents a framework that integrates vector field based motion planning techniques with an optimal path planner. The main motivation for this integration is the solution of UAVs' motion planning problems that are easily and intuitively solved using vector fields, but are very difficult to be even posed as optimal motion planning problems, mainly due to the lack of clear cost functions....
Most existing path planning algorithms focus on generating piecewise linear functions, while the smoothness of the path is usually ignored. Route stability and energy saving are key issues for an autonomous underwater vehicle (AUV) working in the underwater environment. To ensure that AUV can pass through the target area steadily and safely, a path smoother is required. This paper puts forward a novel...
This work presents a novel cooperative path planning for formation keeping robots traversing along a road with obstacles and possible narrow passages. A unique challenge in this problem is a requirement for spatial and temporal coordination between vehicles while ensuring collision and obstacle avoidance. A two-step approach is used for fast real-time planning. The first step uses the A* search on...
In this paper a distributed, mathematical and optimization-based planning problem is introduced to control motion of multiple vehicles as a team which are connected to each other by a communication network. This framework is defined based on a Linear Time Varying Model Predictive Control (LTV-MPC). To do so, we define linear time varying constraints to cover obstacle avoidance and maintain connectivity...
Path planning and autonomous navigation are some of the most important challenges in mobile robotics. These are difficult tasks because the robot has to accurately and safely perform autonomous maneuverings. This paper presents a methodology to efficiently plan the trajectory of a robot in dynamic and complex environments, which it should traverse autonomously. A planner based in the AD* algorithm...
Path planning among obstacles for nonholonomic systems is a widely researched area nowadays, but it is still one of the most challenging problems in autonomous navigation. We have recently presented a rapidly exploring random tree based global planner (RTR) and a steering method (C*CS) for car-like vehicles, which uses circular and straight movements. With the aid of these two methods it is possible...
This paper presents a system for collision-free trajectory planning with multiple Unmanned Aerial Vehicles (UAVs) which automatically identifies conflicts among them. After detecting conflicts between UAVs, the system resolves them cooperatively using a collision-free trajectory planning algorithm based on a stochastic optimization technique named Particle Swarm Optimization (PSO). The new implementation...
In this paper we propose an algorithm for decentralized control of Automated Guided Vehicles (AGVs) operating in automated warehouse environments. The motion planning part of the algorithm provides vehicles with capabilities for autonomous motion planning considering nonholonomic vehicle constraints and collision-free path execution. The decision making part of the algorithm ensures safe vehicle motions...
Navigation through an intersection is a fundamental task that will enable an autonomous car to operate in a real traffic environment. Previous studies about intersection navigation generally assume vehicle to vehicle communication ability for all of the vehicles. Since this is unattainable in the near future, we focus on the scenario that vehicles on the road cannot communicate with each other. A...
This paper focuses on motion-planning problems for high-dimensional mobile robots with nonlinear dynamics operating in complex environments. It is motivated by a recent framework that combines sampling-based motion planning in the state space with discrete search over a workspace decomposition. Building on this line of work, the premise of this paper is that the computational efficiency can be significantly...
We describe a variable-velocity trajectory planning algorithm for navigating car-like robots through unknown, unstructured environments along a series of possibly corrupted GPS waypoints. The trajectories are guaranteed to be kine-matically feasible, i.e., they respect the robot's acceleration and deceleration capabilities as well as its maximum steering angle and steering rate. Their costs are computed...
This paper presents a trajectory planning method for automated parking. The proposed method constructs a state roadmap in which each node contains not only position but also orientation information of the vehicle. The roadmap is constructed by dividing the orientation space in multiple resolutions considering the non-holonomic constraints of the vehicle and the collision-avoidance constraints between...
We present a novel biased sampling technique, Cloud RRT∗, for efficiently computing high-quality collision-free paths, while maintaining the asymptotic convergence to the optimal solution. Our method uses sampling cloud for allocating samples on promising regions. Our sampling cloud consists of a set of spheres containing a portion of the C-space. In particular, each sphere projects to a collision-free...
Path planning is one of the critical issues in mobile robot applications. Traditional methods for path planning in unknown dynamic environment generally plan one step rather than multiple controlling steps. This paper proposes an approach with multiple controlling steps which integrates receding horizon control (RHC) for mobile robot path planning in which the obstacle avoidance problem is converted...
This paper presents a novel method on the motion and path planning for unicycle robots in environments with static circular obstacles. The method employs a family of 2-dimensional analytic vector fields, which have singular points of high-order type and whose integral curves exhibit various patterns depending on the value of a parameter λ. More specifically, for a known value of λ the vector field...
This paper addresses the distributed formation trajectory planning for a group of nonholonomic vehicles. This is realized with a decentralized Model Predictive Control under dynamic virtual structure architecture. A specific limitation of virtual structure based formation method is the necessity of access to the desired reference. To remove this requirement, a distributed estimator is developed so...
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