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Showing papers on "Motion planning published in 2005"


Book
20 May 2005
TL;DR: In this paper, the mathematical underpinnings of robot motion are discussed and a text that makes the low-level details of implementation to high-level algorithmic concepts is presented.
Abstract: A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts. Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts.

1,811 citations


Journal ArticleDOI
TL;DR: D/sup */ Lite is introduced, a heuristic search method that determines the same paths and thus moves the robot in the same way but is algorithmically different, and is at least as efficient as D/Sup */.
Abstract: Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz' Focussed Dynamic A/sup */ (D/sup */) is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan faster than planning from scratch since it modifies its previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to D/sup */ that determines the same paths and thus moves the robot in the same way but is algorithmically different. D/sup */ Lite is simple, can be rigorously analyzed, extendible in multiple ways, and is at least as efficient as D/sup */. We believe that our results will make D/sup */-like replanning methods even more popular and enable robotics researchers to adapt them to additional applications.

601 citations


Proceedings Article
05 Jun 2005
TL;DR: A graph-based planning and replanning algorithm able to produce bounded suboptimal solutions in an anytime fashion that combines the benefits of anytime and incremental planners to provide efficient solutions to complex, dynamic search problems.
Abstract: We present a graph-based planning and replanning algorithm able to produce bounded suboptimal solutions in an anytime fashion. Our algorithm tunes the quality of its solution based on available search time, at every step reusing previous search efforts. When updated information regarding the underlying graph is received, the algorithm incrementally repairs its previous solution. The result is an approach that combines the benefits of anytime and incremental planners to provide efficient solutions to complex, dynamic search problems. We present theoretical analysis of the algorithm, experimental results on a simulated robot kinematic arm, and two current applications in dynamic path planning for outdoor mobile robots.

594 citations


Journal ArticleDOI
TL;DR: An approach for the efficient solution of motion-planning problems for time-invariant dynamical control systems with symmetries, such as mobile robots and autonomous vehicles, under a variety of differential and algebraic constraints on the state and on the control input.
Abstract: In this paper, we introduce an approach for the efficient solution of motion-planning problems for time-invariant dynamical control systems with symmetries, such as mobile robots and autonomous vehicles, under a variety of differential and algebraic constraints on the state and on the control inputs. Motion plans are described as the concatenation of a number of well-defined motion primitives, selected from a finite library. Rules for the concatenation of primitives are given in the form of a regular language, defined through a finite-state machine called a Maneuver Automaton. We analyze the reachability properties of the language, and present algorithms for the solution of a class of motion-planning problems. In particular, it is shown that the solution of steering problems for nonlinear dynamical systems with symmetries and invariant constraints can be reduced to the solution of a sequence of kinematic inversion problems. A detailed example of the application of the proposed approach to motion planning for a small aerobatic helicopter is presented.

450 citations


Journal ArticleDOI
TL;DR: A technique for learning collections of trajectories that characterize typical motion patterns of persons and how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot is proposed.
Abstract: Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns enables a mobile robot to robustly keep track of persons in its environment and to improve its behavior. In this paper we propose a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders are clustered using the expectation maximization algorithm. Based on the result of the clustering process, we derive a hidden Markov model that is applied to estimate the current and future positions of persons based on sensory input. We also describe how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot. We present several experiments carried out in different environments with a mobile robot equipped with a laser-range scanner and a camera system. The results demonstrate that our approach can reliably learn motion patterns of persons, can robustly estimate and predict positions of persons, and can be used to improve the navigation behavior of a mobile robot.

430 citations


Journal ArticleDOI
TL;DR: A feasible, hierarchal approach for real-time motion planning of small autonomous flxed-wing UAVs by dividing the trajectory generation into four tasks: waypoint path planning, dynamic trajectory smoothing, trajectory tracking, and low-level autopilot compensation.
Abstract: Autonomous unmanned air vehicle ∞ight control systems require robust path generation to account for terrain obstructions, weather, and moving threats such as radar, jammers, and unfriendly aircraft. In this paper, we outline a feasible, hierarchal approach for real-time motion planning of small autonomous flxed-wing UAVs. The approach divides the trajectory generation into four tasks: waypoint path planning, dynamic trajectory smoothing, trajectory tracking, and low-level autopilot compensation. The waypoint path planner determines the vehicle’s route without regard for the dynamic constraints of the vehicle. This results in a signiflcant reduction in the path search space, enabling the generation of complicated paths that account for pop-up and dynamically moving threats. Kinematic constraints are satisfled using a trajectory smoother which has the same kinematic structure as the physical vehicle. The third step of the approach uses a novel tracking algorithm to generate a feasible state trajectory that can be followed by a standard autopilot. Monte-Carlo simulations were done to analyze the performance and feasibility of the approach and determine real-time computation requirements. A planar version of the algorithm has also been implemented and tested in a low-cost micro-controller. The paper describes a custom UAV built to test the algorithms.

397 citations


Proceedings ArticleDOI
05 Dec 2005
TL;DR: A prioritized method, based on a powerful method for motion planning in dynamic environments, recently developed by the authors, is introduced, showing that high-quality paths can be produced in less than a second of computation time, even in confined environments involving many robots.
Abstract: In this paper we address the problem of motion planning for multiple robots. We introduce a prioritized method, based on a powerful method for motion planning in dynamic environments, recently developed by the authors. Our approach is generically applicable: there is no limitation on the number of degrees of freedom of each of the robots, and robots of various types - for instance free-flying robots and articulated robots - can be used simultaneously. Results show that high-quality paths can be produced in less than a second of computation time, even in confined environments involving many robots. We examine three issues in particular in this paper: the assignment of priorities to the robots, the performance of prioritized planning versus coordinated planning, and the influence of the extent by which the robot motions are constrained on the performance of the method. Results are reported in terms of both running time and the quality of the paths produced.

303 citations


Journal ArticleDOI
TL;DR: A gradient-approximation algorithm is proposed to generate a suboptimal loop path for a mobile agent to traverse a sequence of target points in the multi-MSA-multitarget case and a cooperative online motion-planning approach is developed.
Abstract: In the surveillance of multiple targets by mobile sensor agents (MSAs), system performance relies greatly on the motion-control strategy of the MSAs. This paper investigates the motion-planning problem for a limited resource of M MSAs in an environment of N targets (M

289 citations


Proceedings ArticleDOI
05 Dec 2005
TL;DR: Partial motion planning is a motion planning scheme with an anytime flavor: when the time available is over, PMP returns the best partial motion to the goal computed so far, which relies upon the concept of inevitable collision states (ICS).
Abstract: This paper addresses the problem of motion planning (MP) in dynamic environments. It is first argued that dynamic environments impose a real-time constraint upon MP: it has a limited time only to compute a motion, the time available being a function of the dynamicity of the environment. Now, given the intrinsic complexity of MP, computing a complete motion to the goal within the time available is impossible to achieve in most real situations. Partial motion planning (PMP) is the answer to this problem proposed in this paper. PMP is a motion planning scheme with an anytime flavor: when the time available is over, PMP returns the best partial motion to the goal computed so far. Like reactive navigation scheme, PMP faces a safety issue: what guarantee is there that the system will never end up in a critical situation yielding an inevitable collision? The answer proposed in this paper to this safety issue relies upon the concept of inevitable collision states (ICS). ICS takes into account the dynamics of both the system and the moving obstacles. By computing ICS-free partial motion, the system safety can be guaranteed. Application of PMP to the case of a car-like system in a dynamic environment is presented.

275 citations


Journal ArticleDOI
TL;DR: This paper presents a computational framework for automatic generation of provably correct control laws for planar robots in polygonal environments using polygon triangulation and discrete abstractions to map continuous motion planning and control problems to computationally inexpensive problems on finite-state-transition systems.
Abstract: In this paper, we present a computational framework for automatic generation of provably correct control laws for planar robots in polygonal environments. Using polygon triangulation and discrete abstractions, we map continuous motion planning and control problems, specified in terms of triangles, to computationally inexpensive problems on finite-state-transition systems. In this framework, discrete planning algorithms in complex environments can be seamlessly linked to automatic generation of feedback control laws for robots with underactuation constraints and control bounds. We focus on fully actuated kinematic robots with velocity bounds and (underactuated) unicycles with forward and turning speed bounds.

274 citations


Journal ArticleDOI
TL;DR: In this paper, a solution strategy for achieving cooperative timing among teams of vehicles is presented based on the notion of coordination variables and coordination functions, the strategy facilitates cooperative timing by making efficient use of communication and computation resources.
Abstract: A solution strategy for achieving cooperative timing among teams of vehicles is presented. Based on the notion of coordination variables and coordination functions, the strategy facilitates cooperative timing by making efficient use of communication and computation resources. The application of the coordination variable/function approach to trajectory-planning problems for teams of unmanned air vehicles with timing constraints is described. Three types of timing constraints are considered: simultaneous arrival, tight sequencing, and loose sequencing. Simulation results demonstrating the viability of the approach are presented.

Proceedings ArticleDOI
18 Apr 2005
TL;DR: This paper first construct discrete abstractions of robot motion based on some environmental decomposition, then generate discrete plans satisfying the temporal logic formula using powerful model checking tools, and finally translate the discrete plans to continuous trajectories using hybrid control.
Abstract: In this paper, we consider the problem of robot motion planning in order to satisfy formulas expressible in temporal logics. Temporal logics naturally express traditional robot specifications such as reaching a goal or avoiding an obstacle, but also more sophisticated specifications such as sequencing, coverage, or temporal ordering of different tasks. In order to provide computational solutions to this problem, we first construct discrete abstractions of robot motion based on some environmental decomposition. We then generate discrete plans satisfying the temporal logic formula using powerful model checking tools, and finally translate the discrete plans to continuous trajectories using hybrid control. Critical to our approach is providing formal guarantees ensuring that if the discrete plan satisfies the temporal logic formula, then the continuous motion also satisfies the exact same formula.

Proceedings ArticleDOI
18 Apr 2005
TL;DR: Results indicate that substantial energy savings of planned paths compared to straight line trajectories are obtained when the current intensity of the eddy structures and the vehicle speed are comparable.
Abstract: Autonomous Underwater Vehicles (AUVs) operate in ocean environments characterized by complex spatial variability which can jeopardize their missions. To avoid this, planning safety routes with minimum energy cost is of primary importance. This work explores the benefits, in terms of energy cost, of path planning in marine environments showing certain spatial variability. Extensive computations have been carried out to calculate, by means of an A* search procedure, optimal paths on ocean environments with different length scale of eddies and different current intensities. To get statistical confidence, different realizations of the eddy field and starting-ending points of the path have been considered for each environment. Unlike previous works, the more realistic and applied case of constant thrust power navigation is considered. Results indicate that substantial energy savings of planned paths compared to straight line trajectories are obtained when the current intensity of the eddy structures and the vehicle speed are comparable. Conversely, the straight line path between starting and ending points can be considered an optimum path when the current speed does not exceed half of the vehicle velocity. In both situations, benefits of path planning seem dependent of the spatial structure of the eddy field.

01 Jan 2005
TL;DR: This work describes a family of recently developed heuristicbased algorithms used for path planning in the real world, and introduces the motivation behind each class of algorithms, and discusses their use on real robotic systems.
Abstract: We describe a family of recently developed heuristicbased algorithms used for path planning in the real world. We discuss the fundamental similarities between static algorithms (e.g. A*), replanning algorithms (e.g. D*), anytime algorithms (e.g. ARA*), and anytime replanning algorithms (e.g. AD*). We introduce the motivation behind each class of algorithms, discuss their use on real robotic systems, and highlight their practical benefits and disadvantages.

Proceedings ArticleDOI
18 Jul 2005
TL;DR: A case study of a mobile robot called Pioneer 3DX is presented, which shows that motion consume less than 50% power on average, and two energy-conservation techniques are introduced: dynamic power management and real-time scheduling.
Abstract: Mobile robots are used in many applications, such as carpet cleaning, pickup and delivery, search and rescue, and entertainment. Energy limitation is one of the most important challenges for mobile robots. Most existing studies on mobile robots focus on motion planning to reduce motion power. However, motion is not the only power consumer. In this paper, we present a case study of a mobile robot called Pioneer 3DX. We analyze the energy consumers. We build power models for motion, sonar sensing and control based on experimental results. The results show that motion consume less than 50% power on average. Therefore, it is important to consider the other components in energy-efficient designs. We introduce two energy-conservation techniques: dynamic power management and real-time scheduling. We provide several examples showing how these techniques can be applied to robots. These techniques together with motion planning provide greater opportunities to achieve better energy efficiency for mobile robots. Although our study is based on a specific robot, the approach can be applied to other types of robots

Proceedings ArticleDOI
12 Dec 2005
TL;DR: This paper presents an integrated approach to the design of closed–loop hybrid controllers that guarantees by construction that the resulting continuous robot trajectories satisfy sophisticated specifications expressed in the so–called Linear Temporal Logic.
Abstract: Robot motion planning algorithms have focused on low-level reachability goals taking into account robot kinematics, or on high level task planning while ignoring low-level dynamics. In this paper, we present an integrated approach to the design of closed–loop hybrid controllers that guarantee by construction that the resulting continuous robot trajectories satisfy sophisticated specifications expressed in the so–called Linear Temporal Logic. In addition, our framework ensures that the temporal logic specification is satisfied even in the presence of an adversary that may instantaneously reposition the robot within the environment a finite number of times. This is achieved by obtaining a Buchi automaton realization of the temporal logic specification, which supervises a finite family of continuous feedback controllers, ensuring consistency between the discrete plan and the continuous execution.

Book ChapterDOI
01 Jan 2005
TL;DR: An overview of some of the recent efforts to develop motion planning methods for humanoid robots for application tasks involving navigation, object grasping and manipulation, footstep placement, and dynamically-stable full-body motions is given.
Abstract: Humanoid robotics hardware and control techniques have advanced rapidly during the last five years. Presently, several companies have announced the commercial availability of various humanoid robot prototypes. In order to improve the autonomy and overall functionality of these robots, reliable sensors, safety mechanisms, and general integrated software tools and techniques are needed. We believe that the development of practical motion planning algorithms and obstacle avoidance software for humanoid robots represents an important enabling technology. This paper gives an overview of some of our recent efforts to develop motion planning methods for humanoid robots for application tasks involving navigation, object grasping and manipulation, footstep placement, and dynamically-stable full-body motions. We show experimental results obtained by implementations running within a simulation environment as well as on actual humanoid robot hardware.

Journal ArticleDOI
TL;DR: The planner not only achieves a smooth spectrum between multiple-query and single-query planning, but it combines advantages of both and is significantly more decoupled than PRM and sampling-based tree planners.
Abstract: This paper shows how to effectively combine a sampling-based method primarily designed for multiple-query motion planning [probabilistic roadmap method (PRM)] with sampling-based tree methods primarily designed for single-query motion planning (expansive space trees, rapidly exploring random trees, and others) in a novel planning framework that can be efficiently parallelized. Our planner not only achieves a smooth spectrum between multiple-query and single-query planning, but it combines advantages of both. We present experiments which show that our planner is capable of solving problems that cannot be addressed efficiently with PRM or single-query planners. A key advantage of our planner is that it is significantly more decoupled than PRM and sampling-based tree planners. Exploiting this property, we designed and implemented a parallel version of our planner. Our experiments show that our planner distributes well and can easily solve high-dimensional problems that exhaust resources available to single machines and cannot be addressed with existing planners.

Journal ArticleDOI
TL;DR: A practical algorithm based on a roadmap that is created for the static part of the scene, which finds an approximately time-optimal trajectory from a start to a goal configuration, such that the robot does not collide with any moving obstacle.
Abstract: In this paper, a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap, an approximately time-optimal trajectory from a start to a goal configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a two-level search for a shortest path. On the local level, trajectories on single edges of the roadmap are found using a depth-first search on an implicit grid in state-time space. On the global level, these local trajectories are coordinated using an A/sup */-search to find a global trajectory to the goal configuration. The approach is applicable to any robot type in configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for both free-flying and articulated robots in three-dimensional workspaces, and it has been applied to multirobot motion planning, as well. Experiments show that the method achieves interactive performance in complex environments.

Proceedings ArticleDOI
18 Apr 2005
TL;DR: This paper formulates the problem of steering a very flexible needle through firm tissue as a nonholonomic kinematics problem, and demonstrates how planning can be accomplished using diffusion-based motion planning on the Euclidean group, SE(3).
Abstract: Fine needles facilitate diagnosis and therapy because they enable minimally invasive surgical interventions. This paper formulates the problem of steering a very flexible needle through firm tissue as a nonholonomic kinematics problem, and demonstrates how planning can be accomplished using diffusion-based motion planning on the Euclidean group, SE(3). In the present formulation, the tissue is treated as isotropic and no obstacles are present. The bevel tip of the needle is treated as a nonholonomic constraint that can be viewed as a 3D extension of the standard kinematic cart or unicycle. A deterministic model is used as the starting point, and reachability criteria are established. A stochastic differential equation and its corresponding Fokker-Planck equation are derived. The Euler-Maruyama method is used to generate the ensemble of reachable states of the needle tip. Inverse kinematics methods developed previously for hyper-redundant and binary manipulators that use this probability density information are applied to generate needle tip paths that reach the desired targets.

Book ChapterDOI
01 Jan 2005
TL;DR: A variety of important issues for sampling-based motion planning are discussed, including uniform and regular sampling, topological issues, and search philosophies, and the role of randomization is addressed.
Abstract: In this paper, we discuss the field of sampling-based motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, samplingbased methods use only information from a collision detector as they search the configuration space. The simplicity of this approach, along with increases in computation power and the development of efficient collision detection algorithms, has resulted in the introduction of a number of powerful motion planning algorithms, capable of solving challenging problems with many degrees of freedom. First, we trace how sampling-based motion planning has developed. We then discuss a variety of important issues for sampling-based motion planning, including uniform and regular sampling, topological issues, and search philosophies. Finally, we address important issues regarding the role of randomization in sampling-based motion planning.

Journal ArticleDOI
TL;DR: Experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.
Abstract: Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but sampling narrow passages in a robot's configuration space remains a challenge for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which reduces sample density in many unimportant parts of a configuration space, resulting in increased sample density in narrow passages. The bridge test can be implemented efficiently in high-dimensional configuration spaces using only simple tests of local geometry. The strengths of the bridge test and uniform sampling complement each other naturally. The two sampling strategies are combined to construct the hybrid sampling strategy for our planner. We implemented the planner and tested it on rigid and articulated robots in 2-D and 3-D environments. Experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.

Proceedings ArticleDOI
15 Aug 2005
TL;DR: A method for generating the optimal path from an initial position and orientation to a flnal position and and orientation in the two-dimensional plane for an aircraft with a bounded turning radius in the presence of a constant wind is presented.
Abstract: In this paper, we explore the problem of generating the optimal time path from an initial position and orientation to a flnal position and orientation in the two-dimensional plane for an aircraft with a bounded turning radius in the presence of a constant wind. Following the work of Boissonnat, we show using the Minimum Principle that the optimal path consists of periods of maximum turn rate or straight lines. We demonstrate, however, that unlike the no wind case, the optimal path can consist of three arcs where the length of the second arc is less than …. A method for generating the optimal path is also presented which iteratively solves the no wind case to intercept a moving virtual target. I. Introduction Optimal path planning is an important problem for robotics and unmanned vehicles. In this paper, we explore a method for flnding the shortest path from an initial position and orientation to a flnal position and and orientation in the two-dimensional plane for an aircraft with a bounded turning radius in the presence of a constant wind. This work was motivated by our group’s work with our ∞eet of small autonomous aircraft. Each modifled Sig Rascal aircraft ∞ies under the combined control of an ofi-the-shelf Piccolo avionics package for low level control and an onboard PC104 computer for higher level tasks. These aircraft ∞y at a nominal speed of 20 m/s, and wind speeds of over 5 m/s have been encountered during ∞ight testing. The problem described above was flrst solved in the case of no wind by Dubins using geometric arguments 8

Journal ArticleDOI
TL;DR: The purpose is to exploit the efficacy of a geometric conformational search as a filtering stage before subsequent energy refinements, and indicate that the geometric stage can provide highly valuable information to biologists.
Abstract: Motivation: Motion is inherent in molecular interactions. Molecular flexibility must be taken into account in order to develop accurate computational techniques for predicting interactions. Energy-based methods currently used in molecular modeling (i.e. molecular dynamics, Monte Carlo algorithms) are, in practice, only able to compute local motions while accounting for molecular flexibility. However, large-amplitude motions often occur in biological processes. We investigate the application of geometric path planning algorithms to compute such large motions in flexible molecular models. Our purpose is to exploit the efficacy of a geometric conformational search as a filtering stage before subsequent energy refinements. Results: In this paper two kinds of large-amplitude motion are treated: protein loop conformational changes (involving protein backbone flexibility) and ligand trajectories to deep active sites in proteins (involving ligand and protein side-chain flexibility). First studies performed using our two-stage approach (geometric search followed by energy refinements) show that, compared to classical molecular modeling methods, quite similar results can be obtained with a performance gain of several orders of magnitude. Furthermore, our results also indicate that the geometric stage can provide highly valuable information to biologists. Availability: The algorithms have been implemented in the general-purpose motion planning software Move3D, developed at LAAS-CNRS. We are currently working on an optimized stand-alone library that will be available to the scientific community. Contact: [email protected]

Proceedings ArticleDOI
18 Apr 2005
TL;DR: Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards, and is suitable for on-board real-time implementation.
Abstract: This paper proposes a potential field-based method for high speed navigation of unmanned ground vehicles (UGVs) on uneven terrain. A potential field is generated in the two-dimensional “trajectory space” of the UGV path curvature and longitudinal velocity. Dynamic constraints, terrain conditions, and navigation conditions can be expressed in this space. A maneuver is chosen within a set of performance bounds, based on the potential field gradient. In contrast to traditional potential field methods, the proposed method is subject to local maximum problems, rather than local minimum. It is shown that a simple randomization technique can be employed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards. Further, vehicle velocity and curvature are controlled to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for on-board real-time implementation.

Proceedings ArticleDOI
12 Dec 2005
TL;DR: A feasible path planning algorithm combined with an off the shelf autopilot system is proposed and it is shown that any execution of the system causes the series {L(k)} to converge to zero, thereby resulting in satisfactory tracking.
Abstract: Unmanned aerial vehicles (UAVs) can assist in U.S. Coast Guard maritime search and rescue missions by flying in formation with a manned helicopter while using infrared cameras to search the water for targets. Current search and rescue flight patterns contain abrupt turns that can be achieved by a helicopter but not by a fixed-wing UAV. Therefore, a necessity for UAV-assisted search and rescue is path planning and control that allows a UAV to track a helicopter performing such maneuvers while maintaining the desired sensor coverage and the safety of all aircraft. A feasible path planning algorithm combined with an off the shelf autopilot system is proposed. The path planning algorithm consists of four modes, each with an associated domain of application. Each mode is formulated to provide safety and contiguous sensor coverage between the UAV and the helicopter. For each of a series of k corners, the along-track distance between the vehicles is defined as L(k). By representing the path planner as a finite automaton, it is shown that any execution of the system causes the series {L(k)} to converge to zero, thereby resulting in satisfactory tracking. Simulations were performed with a non-linear UAV model and a commercial autopilot system in the control loop. In these simulations, the desired trajectories were commanded to the autopilot as a series of waypoints. However, the UAV was unable to accurately track the desired trajectories, resulting in oscillatory paths with unpredictable lengths.

Patent
17 Feb 2005
TL;DR: In this article, a path planning system and method for an object, such as a vehicle, provides a randomized adaptive path planning from starting position (RN) to a goal posotion (202) that handles real-time path planning for a vehicle operating under kinodynamic constraints in dynamically changing and uncertain environments with probabilistic knowledge of vehicle and obstacle movement.
Abstract: A path planning system and method for an object, such as a vehicle, provides a randomized adaptive path planning from starting position (RN) to a goal posotion (202) that handles real-time path planning for a vehicle operating under kinodynamic constraints in dynamically changing and uncertain environments with probabilistic knowledge of vehicle and obstacle (204a-f) movement

Proceedings ArticleDOI
05 Dec 2005
TL;DR: This work develops a motion planning algorithm based on dynamic programming where the path of the needle is uncertain due to uncertainty in tissue properties, needle mechanics, and interaction forces and generates motion plans for bevel-tip needles that reach targets inaccessible to rigid needles.
Abstract: When inserted into soft tissues, flexible needles with bevel tips have been shown experimentally to follow a path of constant curvature in the direction of the bevel. By controlling 2 degrees of freedom at the needle base (bevel direction and insertion distance), these needles can be steered around obstacles to reach targets inaccessible to rigid needles. Motion planning for needle steering is a type of nonholonomic planning for a Dubins car with no reversal. We develop a motion planning algorithm based on dynamic programming where the path of the needle is uncertain due to uncertainty in tissue properties, needle mechanics, and interaction forces. The algorithm computes a discrete control sequence of insertions and direction changes so the needle reaches a target in an imaging plane while minimizing expected cost due to insertion distance, direction changes, and obstacle collisions. We efficiently sample the state space of needle tip positions and orientations and define bounds on the errors due to discretization. We formulate the motion planning problem as a Markov decision process (MDP) and use infinite horizon dynamic programming to compute an optimal control sequence. We first apply the method to the deterministic motion case where the needle precisely follows a path of constant curvature and then to the uncertain motion case where state transitions are defined by a probability distribution. Our implementation generates motion plans for bevel-tip needles that reach targets inaccessible to rigid needles and demonstrates that accounting for uncertainty can lead to significantly different motion plans.

Proceedings ArticleDOI
05 Dec 2005
TL;DR: A new variant of the dynamic-domain RRT, which iteratively adapts the sampling domain for the Voronoi region of each node during the search process, which allows automatic tuning of the parameter and significantly increases the robustness of the algorithm.
Abstract: Sampling based planners have become increasingly efficient in solving the problems of classical motion planning and its applications. In particular, techniques based on the rapidly-exploring random trees (RRTs) have generated highly successful single-query planners. Recently, a variant of this planner called dynamic-domain RRT was introduced by Yershova et al. (2005). It relies on a new sampling scheme that improves the performance of the RRT approach on many motion planning problems. One of the drawbacks of this method is that it introduces a new parameter that requires careful tuning. In this paper we analyze the influence of this parameter and propose a new variant of the dynamic-domain RRT, which iteratively adapts the sampling domain for the Voronoi region of each node during the search process. This allows automatic tuning of the parameter and significantly increases the robustness of the algorithm. The resulting variant of the algorithm has been tested on several path planning problems.

Journal ArticleDOI
01 Dec 2005
TL;DR: A layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment and is implemented on a real mobile robot, Koala, and tested in various environments.
Abstract: Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.