Path Planning Framework Research Articles

Sampling-Based Visual Path Planning Framework for a Multirotor UAV

This paper proposes a sampling-based visual path planning framework for the servoing of a multirotor unmanned aerial vehicle. There are two main problems of the traditional image-based visual servoing: one is that it cannot guarantee the bounded control input because of the high disparity between the initial and desired features, the other is that it could lead to slow convergence rates caused by exponential attenuation over time and insufficient control inputs in the final phase. To overcome these problems, this paper proposes a sampling-based visual path planning framework to generate control inputs using reference features over the interval between the initial and desired features. Our proposed contribution can generate small and bounded control inputs in the large pose difference environments. In detail, it can maintain a small bounded error task function because of preventing singularities, uncertainties, and local minimums during calculating image Jacobian. Therefore, our proposed contribution can not only overcome the deficiencies of traditional image-based visual servoing approaches but also improve control performance. A variety of simulation results conducted to verify the performance of the proposed framework indicate that it can overcome the vulnerabilities of traditional image-based visual servoing with respect to large disparity environment and generate relative body velocity commands.

Autonomous Exploration and Mapping System Using Heterogeneous UAVs and UGVs in GPS-Denied Environments

In this paper, we present a novel integrated vehicular system using collaborative unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for autonomous exploration, mapping, and navigation in GPS-denied 3-dimensional (3-D) unknown environments. The system implements a novel two-layered exploration strategy to decompose the perception task into a coarse exploration layer and a fine mapping layer. The coarse exploration makes use of a UGV to carry out fast autonomous exploration and active 2.5D simultaneous localization and mapping (SLAM) to generate a coarse environment model, which serves as a navigation reference for subsequent complementary 3-D fine mapping conducted by a UAV. The two layers share a novel optimized exploration path planning and navigation framework, which provides optimal exploration paths and integrates the collaborative exploration and mapping efforts through an OctoMap-based volumetric motion planning interface. The proposed system provides an efficient pipeline of fast environment perception taking advantages of the agility of the UAVs as well as the powerful computation resource aboard UGVs, also allowing assistive local perception with augmented object information when necessary. The effectiveness of our system is verified by both simulations and experiments, which demonstrate its capability of implementing heterogeneous UAV and UGV collaborative exploration and structural reconstruction of the environments through active SLAM, providing optimized perception for navigation tasks.

Path planning and tracking for autonomous mining articulated vehicles

This paper presents a path planning and tracking framework for autonomous mining articulated vehicles (AVs). The relation space method is proposed for path planning. In this method, a self-organising, competitive neural network is adopted to identify the space around the vehicle. Then, the vehicle's optimal driving direction is determined by using the spatial geometric relationships of the identified space. A proportional-integral-derivative (PID) controller is used to control the vehicle speed, whereas a model predictive control (MPC) is designed for steering control, where the path tracking error model is used for MPC development. In addition, the AV's dynamics model is built in Adams. The effect and sensitivity of the path-tracking controller are validated through the Matlab-Adams co-simulation. The test results show the satisfactory path-tracking performance.

Pairwise controllability and motion primitives for micro-rotors in a bounded Stokes flow

Micro-robots that can propel themselves in a low Reynolds number fluid flow by converting their rotational motion into translation have begun attracting much attention due to their ease of fabrication. The dynamics and controllability of the motion of such microswimmers are investigated in this paper. The microswimmers under consideration here are spinning spheres (or rotors) whose dynamics are approximated by rotlets, a singularity solution of the Stokes equations. While singularities of Stokes flows are commonly used as theoretical models for microswimmers and micro-robots, rotlet models of microswimmers have received less attention. While a rotlet alone cannot generate translation, a pair of rotlets can interact and execute net motion. Taking the control inputs to be the strengths of the micro rotors, the positions of a pair of rotors are not controllable in an unbounded planar fluid domain. However, in a bounded domain, which is often the case of practical interest, we show that the positions of the micro rotors are controllable. This is enabled by the interaction of the rotors with the boundaries of the domain. We show how control inputs can be constructed based on combinations of Lie brackets to move the rotors from one point to another in the domain. Another contribution of this paper is the creation of a framework for path planning and control of the motion of Stokes singularities that model the dynamics of microswimmers. This can be extended to microswimmers with other shapes moving in confined fluid domains with complex boundaries.

Drift control for cornering maneuver of autonomous vehicles

Expert drivers have the ability to perform high side-slip angle maneuvers, like drifting, during racing to minimize lap time or avoid obstacles. Designing planning and control algorithms for autonomous drift maneuvers, however, is challenging because of the high lateral motion and nearly full saturation of rear tires. In this paper, the authors propose a complete path planning and motion control framework to plan and track a reference drift trajectory along a sharp bend in a track. The path planner divides the path horizon into three regions, finds a path using different planning sub-modules, and then concatenates the solutions to generate the reference trajectory. The controller then applies a mixed open-loop and closed-loop scheme to track the reference trajectory. We validate the planning and control algorithms in simulation using a high-fidelity model in Simulink/Carsim, and through experimentation using 1/10 scale Radio-Control (RC) vehicle.

Offline and Online Search: UAV Multiobjective Path Planning Under Dynamic Urban Environment

This paper is concerned with path planning for unmanned aerial vehicles (UAVs) flying through low altitude urban environment. Although many different path planning algorithms have been proposed to find optimal or near-optimal collision-free paths for UAVs, most of them either do not consider dynamic obstacle avoidance or do not incorporate multiple objectives. In this paper, we propose a multiobjective path planning (MOPP) framework to explore a suitable path for a UAV operating in a dynamic urban environment, where safety level is considered in the proposed framework to guarantee the safety of UAV in addition to travel time. To this aim, two types of safety index maps (SIMs) are developed first to capture static obstacles in the geography map and unexpected obstacles that are unavailable in the geography map. Then an MOPP method is proposed by jointly using offline and online search, where the offline search is based on the static SIM and helps shorten the travel time and avoid static obstacles, while the online search is based on the dynamic SIM of unexpected obstacles and helps bypass unexpected obstacles quickly. Extensive experimental results verify the effectiveness of the proposed framework under the dynamic urban environment.

Grey wolf optimization based sense and avoid algorithm in a Bayesian framework for multiple UAV path planning in an uncertain environment

Unmanned Air Vehicles (UAVs), which have been popular in the military context, have recently attracted attention of many researchers because of their potential civilian applications. However, before UAVs can fly in civilian airspace, they need to be able to navigate safely to their goal while maintaining separation with other manned and unmanned aircraft during the transit. Algorithms for autonomous navigation of UAVs require access to accurate information about the state of the environment in order to perform well. However, this information is often uncertain and dynamically changing. In this paper, a Grey Wolf Optimization (GWO) based algorithm is proposed to find the optimal UAV trajectory in presence of moving obstacles, referred to as Intruder Aircraft (IAs), with unknown trajectories. The solution uses an efficient Bayesian formalism with a notion of cell weighting based on Distance Based Value Function (DBVF). The assumption is that the UAV is equipped with the Automatic Dependent Surveillance-Broadcast (ADS-B) and is provided with the position of IAs either via the ADS-B or ground-based radar. However, future trajectories of the IAs are unknown to the UAV. The proposed method is verified using simulations performed on multiple scenarios. The results demonstrate the effectiveness of the proposed method in solving the trajectory planning problem of the UAVs.

Path planning for solar-powered UAV in urban environment

Abstract Aiming at the complexity and particularity of urban environment, a solar-powered UAV (SUAV) path planning framework is proposed in this paper. The framework can be decomposed into three aspects to resolve. First, to make SUAV avoid the building obstacles, a nature-inspired path planning method called Interfered Fluid Dynamical System (IFDS) is introduced. Aiming at the defect that the traditional IFDS is not suitable for SUAV energy optimization calculation, the dynamic constraints and model are introduced to IFDS. The modified IFDS, called Restrained IFDS (RIFDS), is proposed. Second, to resolve the path planning issue efficiently, a novel intelligent optimization algorithm called Whale Optimization Algorithm (WOA) is selected as the basic framework solver. To further overcome the drawback of local minima, adaptive chaos-Gaussian switching solving strategy and coordinated decision-making mechanism are introduced to the basic WOA. The modified algorithm, called Improved WOA (IWOA), is proposed. Third, to solve the accurate modeling problem of solar energy in urban environment, two measures are adopted: (1) A practical judgment method for sunlight occlusions is proposed; (2) Aiming at some unreasonable aspects in the solar energy production model, the received solar energy is modified and recalculated by ASHRAE Clear Sky Model and the solar irradiance calculation principle for slant surfaces in this paper. Finally, the effectiveness of the proposed framework is tested by the simulations.

Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints

A path planning and tracking framework is presented to maintain a collision-free path for autonomous vehicles. For path-planning approaches, a 3-D virtual dangerous potential field is constructed as a superposition of trigonometric functions of the road and the exponential function of obstacles, which can generate a desired trajectory for collision avoidance when a vehicle collision with obstacles is likely to happen. Next, to track the planned trajectory for collision avoidance maneuvers, the path-tracking controller formulated the tracking task as a multiconstrained model predictive control (MMPC) problem and calculated the front steering angle to prevent the vehicle from colliding with a moving obstacle vehicle. Simulink and CarSim simulations are conducted in the case where moving obstacles exist. The simulation results show that the proposed path-planning approach is effective for many driving scenarios, and the MMPC-based path-tracking controller provides dynamic tracking performance and maintains good maneuverability.

Monitoring Moving Objects Using Aerial Mobile Sensors

We propose an optimization-based path-planning framework for an aerial mobile sensor network. The purpose of the path planning is to monitor a set of moving surface objects. The algorithm provides collision-free mobile sensor trajectories that are feasible with respect to user-defined vehicle dynamics. The objective of the resulting optimal control problem is to minimize the uncertainty of the objects, represented as the trace of the augmented state and parameter estimation error covariance. The dynamic optimization problem is discretized into a large-scale nonlinear programming problem using the direct transcription method known as simultaneous collocation. The optimization problem is solved with a receding horizon and both a field experiment and a numerical simulation illustrate the approach.

Motion Planning via Manifold Samples

We present a general and modular algorithmic framework for path planning of robots. Our framework combines geometric methods for exact and complete analysis of low-dimensional configuration spaces, together with practical, considerably simpler sampling-based approaches that are appropriate for higher dimensions. In order to facilitate the transfer of advanced geometric algorithms into practical use, we suggest taking samples that are entire low-dimensional manifolds of the configuration space that capture the connectivity of the configuration space much better than isolated point samples. Geometric algorithms for analysis of low-dimensional manifolds then provide powerful primitive operations. The modular design of the framework enables independent optimization of each modular component. Indeed, we have developed, implemented and optimized a primitive operation for complete and exact combinatorial analysis of a certain set of manifolds, using arrangements of curves of rational functions and concepts of generic programming. This in turn enabled us to implement our framework for the concrete case of a polygonal robot translating and rotating amidst polygonal obstacles. We demonstrate that the integration of several carefully engineered components leads to significant speedup over the popular PRM sampling-based algorithm, which represents the more simplistic approach that is prevalent in practice. We foresee possible extensions of our framework to solving high-dimensional problems beyond motion planning.

An Integral Framework of Task Assignment and Path Planning for Multiple Unmanned Aerial Vehicles in Dynamic Environments

In this paper, a hierarchical framework for task assignment and path planning of multiple unmanned aerial vehicles (UAVs) in a dynamic environment is presented. For multi-agent scenarios in dynamic environments, a candidate algorithm should be able to replan for a new path to perform the updated tasks without any collision with obstacles or other agents during the mission. In this paper, we propose an intersection-based algorithm for path generation and a negotiation-based algorithm for task assignment since these algorithms are able to generate admissible paths at a smaller computing cost. The path planning algorithm is also augmented with a potential field-based trajectory replanner, which solves for a detouring trajectory around other agents or pop-up obstacles. For validation, test scenarios for multiple UAVs to perform cooperative missions in dynamic environments are considered. The proposed algorithms are implemented on a fixed-wing UAVs testbed in outdoor environment and showed satisfactory performance to accomplish the mission in the presence of static and pop-up obstacles and other agents.

Optimal Path Computation for Autonomous Aerial Vehicles

In this paper, a path planning approach is developed and demonstrated for an unmanned aerial vehicle (UAV); the algorithm is applicable for autonomous robot path planning also. The main contribution of the paper is the development of an extension to the Bellman–Ford algorithm that enables incorporation of constraints directly into the algorithm during run-time. This, therefore, provides a framework for path planning, which does not cause violation of the dynamical constraints of the vehicle (or robot), such as its angle of turn. Furthermore, a procedure for computing a number of sub-optimal paths is developed so that a range of options is available for selection; the optimality of the paths is also proved. These sub-optimal paths are generated in an order of priority (optimality). An objective function is developed that models different conflicting objectives in a unified framework; these objectives can be assigned different weights. The objectives may include minimizing the length of the path, keeping the path as straight as possible, visiting areas of interest, avoiding obstacles, approaching the terminal point from a given direction, etc. The algorithm is tested for complex mission objectives, and results are discussed.

Cooperative Control of Multiple Uninhabited Aerial Vehicles for Monitoring and Fighting Wildfires

Uninhabited aerial vehicles provide numerous advantages in fighting wildland fires that include persistent operation and elimination of humans from performing what can be dull, dangerous, and dirty work. Multiple cooperating uninhabited aerial vehicles can potentially bring about a paradigm shift in the way we fight complex wildland fires. This paper investigates algorithmic development for cooperative control of a number of uninhabited aerial vehicles engaged in fighting a wildland fire. The paper considers two tasks to be performed by a group of uninhabited aerial vehicles: 1) Cooperative tracking of a fire front for accurate situational awareness, and 2) cooperative, autonomous fire fighting using fire suppressant fluid. The scenario considered in this paper makes the following assumptions: information regarding the location of the fire and position of all uninhabited aerial vehicles is made available to each uninhabited aerial vehicle; and each uninhabited aerial vehicle is equipped with unlimited fire suppressant fluid which extinguishes fire in a circle of specified area directly beneath it. This paper formulates these two tasks of fire fighting based upon optimization of respective utility functions, develops a decentralized control method for the cooperative uninhabited aerial vehicles, and analyzes the system for its stability and its ability to carry out the tasks. The proposed strategies have been verified with the help of extensive simulations. Although simplifying assumptions have been made, this preliminary study presents a framework for path planning and cooperative control of multiple uninhabited aerial vehicles engaged in gathering data and actively fighting forest fires.

The Path-of-probability Algorithm for Steering and Feedback Control of Flexible Needles

In this paper we develop a new framework for path planning of flexible needles with bevel tips. Based on a stochastic model of needle steering, the probability density function for the needle-tip pose is approximated as a Gaussian. The means and covariances are estimated using an error propagation algorithm which has second-order accuracy. Then we adapt the path-of-probability (POP) algorithm to path planning of flexible needles with bevel tips. We demonstrate how our planning algorithm can be used for feedback control of flexible needles. We also derive a closed-form solution for the port placement problem for finding good insertion locations for flexible needles in the case when there are no obstacles. Furthermore, we propose a new method using reference splines with the POP algorithm to solve the path planning problem for flexible needles in more general cases that include obstacles.

Visual Servoing Path Planning via Homogeneous Forms and LMI Optimizations

Path planning is a useful technique for visual servoing as it allows one to take into account system constraints and achieve desired performances during the camera motion. In this paper, we propose a new framework for path planning based on the use of homogeneous forms and linear matrix inequalities (LMIs). Specifically, we introduce a general parametrization of the trajectories from the initial to the desired location based on homogeneous forms and a parameter-dependent version of the Rodrigues formula. This allows us to impose typical constraints (field of view, workspace, joint, avoidance of collision, and occlusion) via positivity conditions on suitable homogeneous forms. Then, we reformulate the problem of finding a trajectory in the 3-D space satisfying all these constraints as an LMI optimization that can handle the maximization of typical performances (e.g., visibility margin, similarity to a straight line). The planned camera path is tracked by using an image-based controller. The proposed approach is illustrated and validated through simulations and experiments.

A novel path planning and control framework for passive resistance therapy with a robot manipulator

In this article, we present a paradigm for safe path generation and control for a robotic manipulator such that it provides programmable passive resistance therapy to patients with deficits in the upper extremities. When the patient applies an interaction force at the robot's end-effector, a dynamic path generator time parameterises any therapist-specified contour in the robot's workspace–thus, the robot mimics the dynamics of a passive impedance whose anisotropy vector can be continuously reconfigured. The proposed algorithm is easily implementable because it is robust to uncertainty in the robot dynamics. Moreover, the proposed strategy also guarantees user safety by maintaining the net flow of energy during the human robot interaction from the user towards the manipulator.

The corridor map method: a general framework for real-time high-quality path planning

In many virtual environment applications, paths have to be planned for characters to traverse from a start to a goal position in the virtual world while avoiding obstacles. Contemporary application...

Local path planning using a new artificial potential function composition and its analytical design guidelines

This paper presents a new framework for path planning based on artificial potential functions (APFs). In this scheme, the APFs for path planning have a multiplicative and additive composition between APFs for goal destination and APFs for obstacle avoidance, unlike conventional composition where the APF for obstacle avoidance is added to the APF for goal destination. In particular, this paper presents a set of analytical guidelines for designing potential functions to avoid local minima for a number of representative scenarios based on the proposed framework for path planning. Specifically the following cases are addressed: (i) a non-reachable goal problem (a case in which the potential of the goal is overwhelmed by the potential of an obstacle), (ii) an obstacle collision problem (a case in which the potential of the obstacle is overwhelmed by the potential of the goal) and (iii) a narrow passage problem (a case in which the potential of the goal is overwhelmed by the potential of two obstacles). The example results for each case show that the proposed scheme can effectively construct a path-planning system with the capability of reaching a goal and avoiding obstacles despite possible local minima.

On‐line estimation and path planning for multiple vehicles in an uncertain environment

AbstractA unified approach to cooperative target tracking and path planning for multiple vehicles is presented. All vehicles, friendly and adversarial, are assumed to be aircraft. Unlike the typical target tracking problem that uses the linear state and nonlinear output dynamics, a set of aircraft nonlinear dynamics is used in this work. Target state information is estimated in order to integrate into a path planning framework. The objective is to fly from a start point to a goal in a highly dynamic, uncertain environment with multiple friendly and adversarial vehicles, without collision. The estimation architecture proposed is consistent with most path planning methods. Here, the path planning approach is based on evolutionary computation technique which is then combined with a nonlinear extended set membership filter in order to demonstrate a unified approach. A cooperative estimation approach among friendly vehicles is shown to improve speed and routing of the path. Copyright © 2004 John Wiley & Sons, Ltd.