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Path planning algorithms have evolved during decades to become computationally less expensive and optimal. In this paper a deterministic approach is used to find a path near to the shortest path using motion primitives. The motion primitives are constructed using a non-holonomic vehicle model. The physical model enables the algorithm to use a friction map and calculate paths with lower lateral slip...
In this paper we propose a trajectory planning approach for autonomous vehicles on structured road maps. Therefore we are using the well-known A∗ optimal path planning algorithm. We generate a safe optimal trajectory through a three-dimensional graph, considering the two-dimensional position and time. (1) The graph is generated dynamically with fixed time differences and flexible distances between...
In order for travelers to arrive at their destination comfortably, it is important that they plan their trip carefully. Our motivation in this paper is to free the traveler from tedious planning tasks. Several studies and applications have been proposed to help travelers plan their trip according to explicit preferences, but most ignore implicit preferences. For the first time, to the best of our...
Path planning of an autonomous vehicle as a non-holonomic system is an essential part for many automated driving applications. Parking a car into a parking lot and maneuvering it through a narrow corridor would be a common driving scenarios in an urban environment. In this study a hybrid approach for dynamic path planning is presented which deals with the limited degrees of freedom of a non-holonomic...
We have been developing a system, called Tour Miner, which mines tour plans from SNS. It consists of two functions: mining and smelting. The mining function searches SNS for a given keyword and discovers travel records related to the keyword. The smelting function combines the travel records and extracts tour plans from the combination. In this paper, we elucidate the implementation of the smelting...
Many variants of the Rapidly-exploring Random Tree (RRT) algorithm use biased-sampling strategies for solving computationally intensive tasks. One of such tasks is the planning of safe trajectories with the simultaneous intervention in both the longitudinal and the lateral dynamics of the vehicle in complex traffic-scenarios with multiple static and dynamic objects. A recently proposed hybrid statistical...
This paper proposes an alternative approach to analyze the convergence of multistage deterministic dual dynamic programming (DDDP). A new convergence stop criteria is proposed and it is based on a measure of gain produced when a new cut is inserted in cost-to-go function, during the backward phase of the algorithm. To do so, an additional linear programming problem is solved to decide if new cut (hyper...
Electric vehicles (EVs) play a significant role in the current transportation systems. The main factor that affects the acceptance of existing EV models is the range anxiety problem caused by limited charging stations and long recharge times. Recently, the solar-powered EV has drawn many attentions due to being free of charging limitations. However, the solarpowered EVs may still struggle with the...
As a heuristic search algorithm, the A∗ algorithm is often used to solve the problem of ships routing optimization. For convex obstacles, the A∗ algorithm is improved in the local optimal path planning of ships, when the increased number of waypoints lead a lower efficiency. The improved algorithm uses the angle between the starting point, the ending point and north direction as the heuristic information...
UAV route planning in complicated environment is researched. The UAV route planning algorithm based on MO-RRT is proposed to ensure the safe execution in the complicated battlefield with incident threatens. First, threaten avoidance model is established by radar identification mechanism which can highly reduce its threaten space. Then the target heuristic strategy is utilized to accelerate the convergence...
We present a mobile robot motion planning approach under kinodynamic constraints that exploits learned perception priors in the form of continuous Gaussian mixture fields. Our Gaussian mixture fields are statistical multi-modal motion models of discrete objects or continuous media in the environment that encode e.g. the dynamics of air or pedestrian flows. We approach this task using a recently proposed...
We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) the planner focuses on the most relevant states to the current planning problem; and (3) the planner focuses on the most informative and/or high-value...
Motion planning under differential constraints is one of the canonical problems in robotics. State-of-the-art methods evolve around kinodynamic variants of popular sampling-based algorithms, such as Rapidly-exploring Random Trees (RRTs). However, there are still challenges remaining, for example, how to include complex dynamics while guaranteeing optimality. If the open-loop dynamics are unstable,...
In this paper, we introduce an informative path planning (IPP) framework for active classification using unmanned aerial vehicles (UAVs). Our algorithm uses a combination of global viewpoint selection and evolutionary optimization to refine the planned trajectory in continuous 3D space while satisfying dynamic constraints. Our approach is evaluated on the application of weed detection for precision...
This paper presents a method for constructing 3D maps of marine archaeological sites using deployments of Autonomous Underwater Vehicles (AUV) equipped with sonar and cameras. The method requires multiple AUV missions in which the first mission directs the AUV to conduct a high altitude lawnmower scan over the area to create a course bathymetry map using sonar. Subsequent AUV missions then direct...
The problem of optimal motion planing and control is fundamental in robotics. However, this problem is intractable for continuous-time stochastic systems in general and the solution is difficult to approximate if non-instantaneous nonlinear performance indices are present. In this work, we provide an efficient algorithm, PIPC (Probabilistic Inference for Planning and Control), that yields approximately...
Model predictive control (MPC) is a popular control method that has proved effective for robotics, among other fields. MPC performs re-planning at every time step. Re-planning is done with a limited horizon per computational and real-time constraints and often also for robustness to potential model errors. However, the limited horizon leads to suboptimal performance. In this work, we consider the...
When group of robots' actions are planned there is a problem of target distribution. The construction of exact sequences of operations performed by robots is computationally expensive and some of the calculated trajectories will not be used. To distribute targets between robots can be used evaluation function of traffic and overload operations cost estimation. The paper presents a study of the cost...
This work addresses the problem of kinematic trajectory planning for mobile manipulators with non-holonomic constraints, and holonomic operational-space tracking constraints. We obtain whole-body trajectories and time-varying kinematic feedback controllers by solving a Constrained Sequential Linear Quadratic Optimal Control problem. The employed algorithm features high efficiency through a continuous-time...
Optimal path planning in dynamic environments for an unmanned vehicle is a complex task of mobile robotics that requires an integrated approach. This paper describes a path planning algorithm, which allows to build a preliminary motion trajectory using global information about environment, and then dynamically adjust the path in real-time by varying objective function weights. We introduce a set of...
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