Path planning

Abstract—The paper tackles the problem of designing intuitive graphical interfaces for selecting navigational targets for an autonomous robot. Our work focuses on the design and validation of such a flexible interface for an intelligent wheelchair navigating in a large indoor environment. We begin by describing the robot platform and interface design. We then present results from a user study in which participants were required to select navigational targets using a variety of input and filtering methods. We considered two types of input modalities (point-and-click and single-switch), to investigate the effect of constraints on the input mode. We take a particular look at the use of filtering methods to reduce the amount of information presented onscreen and thereby accelerate selection of the correct option. I.

We study the problem of computing a planar curve, restricted to lie between two given polygonal chains, such that the integral of the square of arc-length derivative of curvature along the curve is minimized. We introduce the minimum variation B-spline problem, which is a linearly constrained optimization problem over curves, defined by B-spline functions only. An empirical investigation indicates that this problem has one unique solution among all uniform quartic B-spline functions. Furthermore, we prove that, for any B-spline function, the convexity properties of the problem are preserved subject to a scaling and translation of the knot sequence defining the B-spline.

We present a new approach to path planning, called the "Ariadne's clew algorithm". It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments - ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called SEARCH and EXPLORE, applied in an interleaved

This paper shows a strategy suitable for navigating autonomous robots in a completely unknown environment. The method proposed combines optimum path planning techniques with fuzzy logic to avoid obstacles and to determine the shortest path towards its goal. The technique computes the potential surface using Dijkstra’s algorithm in a moving window, updating the cost map as it moves with the information obtained by the ultrasonic sensors. A Fuzzy Logic Controller (FLC) controls the wheels of a differential drive robot to the angle of minimum potential. This ensures a smooth trajectory towards the objective. A second FLC controls the average speed of the platform.

In this study, a path smoothing strategy is proposed for sensor-based coverage problems. Smooth paths are generated for the coverage problems considering mobile robot kinematics constraints. An open agent architecture-based control structure is used to implement the proposed approach on real robots. The algorithm is coded with C++ and implemented on P3-DX mobile robots in MobileSim simulation environments. It is

This paper addresses the solution of smooth trajectory planning for industrial robots in environments with obstacles using a direct method, creating the trajectory gradually as the robot moves. The method presented deals with the uncertainties associated with the lack of knowledge of kinematic properties of intermediate via-points since they are generated as the algorithm evolves looking for the solution. Several cost functions are also proposed, which use the time that has been calculated, to guide the robot motion. The method has been applied successfully to a PUMA 560 robot and four operational parameters (execution time, computational time, distance travelled and number of configurations) have been computed to study the properties and influence of each cost function on the trajectory obtained.

... Sensory information comes from a Laser Range Finder also developed by the author [6]. Range data are made up of hundreds of 2D relative coordinate points of targets in the field of view of the sensor camera, already filtered and grouped ... [2] JC Garcı́a, M. Marrón, JA Garcı́a, J ...

Knowing the geometry of a space is desirable for many applications, e.g. sound source localization, sound field reproduction or auralization. In circumstances where only acoustic signals can be obtained, estimating the geometry of a room is a challenging proposition. Existing methods have been proposed to reconstruct a room from the room impulse responses (RIRs). However, the sound source and microphones must be deployed in a feasible region of the room for it to work, which is impractical when the room is unknown. This work propose to employ a robot equipped with a sound source and four acoustic sensors, to follow a proposed path planning strategy to moves around the room to collect first image sources for room geometry estimation. The strategy can effectively drives the robot from a random initial location through the room so that the room geometry is guaranteed to be revealed. Effectiveness of the proposed approach is extensively validated in a synthetic environment, where the results obtained are highly promising.

—This paper presents a B-spline based path planning approach for agricultural guidance applications which is able to handle successively generated trajectories. Additionally, the lateral controller which was used to calculate the steering angle is described and the method by which the required parameters were determined is introduced. We explain stepwise how the output of a perception system is used to determine the control points for the planner and why B-splines and Bézier curves are used to model the trajectory. Moreover, it is shown how the inputs for the path tracker are derived from the B-splines. The approach was implemented on two different farm machines where the performance of the planning and control approach were evaluated. Furthermore, both algorithms were used to build a windrow guidance demonstrator.

The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constraints of the available observing network. Examples of objectives are better oceanic understanding, to improve forecast quality, or to sample regions of high interest. This work provides a new path-planning scheme for the adaptive sampling problem. We ...

In the past mobile robot research was often focused to various kinds of point-to-point transportation tasks. Service tasks, such as floor cleaning, require specific approaches for path planning and vehicle guidance in real indoor environments. This article discusses automatic planning of a feasible cleaning path considering a 2D-map as well as kinematic and geometric robot models. Path construction makes use of two typical motion patterns. Each pattern is defined by a sequence of subgoals indicating robot position and orientation. Results of automatic path planning are illustrated by realistic examples of typical robots and cleaning environments. Vehicle guidance includes initialization of robot location, path execution, accurate path tracking and detection of unexpected environmental changes. Path tracking is achieved by subgoal modification during cleaning motion using data from the dead-reckoning and landmark localization systems. If obstacles permanently block the preplanned path, an automatic map update and path replanning is performed. Experimental results with the mobile robot MACROBE confirm the feasibility of the developed planning and guidance system.

Three-dimensional digital terrain models are of fundamental importance in many areas such as the geo-sciences and outdoor robotics. Accurate modeling requires the ability to deal with a varying data density and to balance smoothing against the preservation of discontinuities. The latter is particularly important for robotics applications, as discontinuities that arise, for example, at steps, stairs, or building walls are

This paper presents a summary of SciAutonics-Auburn Engineering’s efforts in the 2005 DARPA Grand Challenge. The areas discussed in detail include the team makeup and strategy, vehicle choice, software architecture, vehicle control, navigation, path planning, and obstacle detection. In particular, the advantages and complications involved in fielding a low budget all-terrain vehicle are presented. Emphasis is placed on detailing the methods used for high-speed control, customized navigation, and a novel stereo vision system. The platform chosen required a highly accurate model and a well-tuned navigation system in order to meet the demands of the Grand Challenge. Overall, the vehicle completed three out of four runs at the National Qualification Event and traveled 16 miles in the Grand Challenge before a hardware failure disabled operation. The performance in the events is described, along with a success and failure analysis.

In this paper, we present a novel bi-directional cooperative obstacle avoidance system of heterogeneous unmanned vehicles, consisting of an unmanned ground vehicle (UGV) and a micro-aerial vehicle (MAV), equipped with cameras, operating in an indoor environment without Global Positioning System (GPS) signals. The system demonstrates the synergistic relationship between the two platforms by sharing different perspectives and information collected independently using their on-board sensors in performing a navigation task in an indoor environment, including avoiding obstacles and entering narrow pathways. The MAV uses an aerial view of the environment to develop an obstacle free path for the UGV using the A* algorithm. The UGV deploys the planned path in conjunction with information gathered from its own front facing camera to navigate through a cluttered environment using a Lyapunov stable sliding mode controller. The UGV is responsible for detecting low and narrow pathways and to guide the MAV to move through them. The bidirectional cooperation has been tested in hardware as well as in simulation, showing the system’s effectiveness.

This paper discusses opportunities to parallelize graph based path planning algorithms in a time varying environment. Parallel architectures have become commonplace, requiring algorithm to be parallelized for efficient execution. An additional focal point of this paper is the inclusion of inaccuracies in path planning as a result of forecast error variance, accuracy of calculation in the cost functions and a different observed vehicle speed in the real mission than planned. In this context, robust path planning algorithms will be described. These algorithms are equally applicable to land based, aerial, or underwater mobile autonomous systems. The results presented here provide the basis for a future Research project in which the parallelized algorithms will be evaluated on multi and many core systems such as the dual core ARM Panda board and the 48 core Single-chip Cloud Computer (SCC). Modern multi and many core processors support a wide range of performance vs. energy tradeoffs th...

Multi-robot exploration problem is generally constituted of a determination of next-best-views (NBVs), path planning, and coordination algorithm. This paper presents a unified 3D multi-robot exploration algorithm in order to solve the inefficiency that takes place when the aforementioned three components are constructed individually. The proposed algorithm is composed of two parts: an allocation of exploration regions and a determination of the best path. For the allocation of the region to explore, each robot generates a sampling-based tree, e.g. RRT, which composes a Voronoi-biased forest (VBF). A VBF, a new data structure introduced within this work, assigns a region for each robot to explore in a probabilistic manner. The amount of the space a VBF covers is quantitatively analyzed depending on its parameters. From the generated VBF, each robot determines the best path from branches of its tree based on the amount of information can be gained along with the paths of peer robots. Only the first edge of the best branch of each tree is executed in a receding horizon scheme. The overall exploration algorithm is evaluated in a computer simulated environment. The results demonstrate that our coordination algorithm allows robots to quickly and reliably explore the environment.