Showing papers on "Motion planning published in 2001"

Randomized kinodynamic planning

Abstract: This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an ...

A solution to the simultaneous localization and map building (SLAM) problem

Abstract: The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from estimation-theoretic foundations of this problem, the paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. The paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, the paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal map-building algorithms and map management.

Coverage for robotics – A survey of recent results

Abstract: This paper surveys recent results in coverage path planning, a new path planning approach that determines a path for a robot to pass over all points in its free space. Unlike conventional point-to-point path planning, coverage path planning enables applications such as robotic de-mining, snow removal, lawn mowing, car-body painting, machine milling, etc. This paper will focus on coverage path planning algorithms for mobile robots constrained to operate in the plane. These algorithms can be classified as either heuristic or complete. It is our conjecture that most complete algorithms use an exact cellular decomposition, either explicitly or implicitly, to achieve coverage. Therefore, this paper organizes the coverage algorithms into four categories: heuristic, approximate, partial-approximate and exact cellular decompositions. The final section describes some provably complete multi-robot coverage algorithms.

Planning walking patterns for a biped robot

Abstract: Biped robots have better mobility than conventional wheeled robots, but they tend to tip over easily. To be able to walk stably in various environments, such as on rough terrain, up and down slopes, or in regions containing obstacles, it is necessary for the robot to adapt to the ground conditions with a foot motion, and maintain its stability with a torso motion. When the ground conditions and stability constraint are satisfied, it is desirable to select a walking pattern that requires small torque and velocity of the joint actuators. We first formulate the constraints of the foot motion parameters. By varying the values of the constraint parameters, we can produce different types of foot motion to adapt to ground conditions. We then propose a method for formulating the problem of the smooth hip motion with the largest stability margin using only two parameters, and derive the hip trajectory by iterative computation. Finally, the correlation between the actuator specifications and the walking patterns is described through simulation studies, and the effectiveness of the proposed methods is confirmed by simulation examples and experimental results.

Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization

Abstract: This paper presents a new method for simultaneous localization and mapping that exploits the topology of the robot's free space to localize the robot on a partially constructed map. The topology of the environment is encoded in a topological map; the particular topological map used in this paper is the generalized Voronoi graph (GVG), which also encodes some metric information about the robot's environment, as well. In this paper, we present the low-level control laws that generate the GVG edges and nodes, thereby allowing for exploration of an unknown space. With these prescribed control laws, the GVG can be viewed as an arbitrator for a hybrid control system that determines when to invoke a particular low-level controller from a set of controllers all working toward the high-level capability of mobile robot exploration. The main contribution, however, is using the graph structure of the GVG, via a graph matching process, to localize the robot. Experimental results verify the described work.

Real-time motion planning for agile autonomous vehicles

Abstract: Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized motion planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. Simulation examples involving a ground robot and a small autonomous helicopter, are presented and discussed.

Mixed integer programming for multi-vehicle path planning

Abstract: This paper presents a new approach to fuel-optimal path planning of multiple vehicles using a combination of linear and integer programming. The basic problem formulation is to have the vehicles move from an initial dynamic state to a final state without colliding with each other, while at the same time avoiding other stationary and moving obstacles. It is shown that this problem can be rewritten as a linear program with mixed integer/linear constraints that account for the collision avoidance. A key benefit of this approach is that the path optimization can be readily solved using the CPLEX optimization software with an AMPL/Matlab interface. An example is worked out to show that the framework of mixed integer/linear programming is well suited for path planning and collision avoidance problems. Implementation issues are also considered. In particular, we compare receding horizon strategies with fixed arrival time approaches.

Posture-based motion planning: applications to grasping.

Abstract: This article describes a model of motion planning instantiated for grasping. According to the model, one of the most important aspects of motion planning is establishing a constraint hierarchy - a set of prioritized requirements defining the task to be performed. For grasping, constraints include avoiding collisions with the to-be-grasped objects and minimizing movement-related effort. These and other constraints are combined with instant retrieval (recall of stored postures) and instance generation (generation of new postures and movements to them) to simulate flexible prehension. Dynamic deadline setting is used to regulate termination of instance generation, and performance of more than one movement at a time with a single effector is used to permit obstacle avoidance. Old and new data are accounted for with the model.

Autonomous vehicle positioning with GPS in urban canyon environments

Abstract: The Global Positioning System (GPS) has been widely used in land vehicle navigation applications. However, the positioning systems based on GPS alone face great problems in the so-called urban canyon environments, where the GPS signals are often blocked by high-rise buildings and there are not enough available satellite signals to estimate the positioning information of a fix. To solve the problem, a constrained method is presented by approximately modeling the path of the vehicle in the urban canyon environments as pieces of lines. By adding this constraint, the minimum number of available satellites reduces to two, which is satisfied in many urban canyon environments. Then, different approaches using the constrained method are systematically developed. In addition, a state-augmentation method is proposed to simultaneously estimate the positions of the GPS receiver and the parameters of the line. Furthermore, the interacting multiple model method is used to determine the correct path which the vehicle follows after passing an intersection of roads. Simulation results show that this approach can solve the urban canyon problems successfully.

The CLARAty architecture for robotic autonomy

Abstract: This paper presents an overview of a newly developed Coupled Layer Architecture for Robotic Autonomy (CLARAty), which is designed for improving the modularity of system software while more tightly coupling the interaction of autonomy and controls. First, we frame the problem by briefly reviewing previous work in the field and describing the impediments and constraints that been encountered. Then we describe why a fresh approach to the topic is warranted, and introduce our new two-tiered design as an evolutionary modification of the conventional three-level robotics architecture. The new design features a tight coupling of the planner and executive in one Decision Layer, which interacts with a separate Functional Layer at all levels of system granularity. The Functional Layer is an object-oriented software hierarchy that provides basic capabilities of system operation, resource prediction, state estimation, and status reporting. The Decision Layer utilizes these capabilities of the Functional Layer to achieve goals by expanding, ordering, initiating and terminating activities. Both declarative and procedural planning methods are used in this process. Current efforts are targeted at implementing an initial version of this architecture on our research Mars rover platforms, Rocky 7 and 8. In addition, we are working with the NASA robotics and autonomy communities to expand the scope and participation in this architecture, moving toward a flight implementation in the 2007 time-frame.

Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar

Abstract: This paper describes a new framework for segmentation of sonar images, tracking of underwater objects and motion estimation. This framework is applied to the design of an obstacle avoidance and path planning system for underwater vehicles based on a multi-beam forward looking sonar sensor. The real-time data flow (acoustic images) at the input of the system is first segmented and relevant features are extracted. We also take advantage of the real-time data stream to track the obstacles in following frames to obtain their dynamic characteristics. This allows us to optimize the preprocessing phases in segmenting only the relevant part of the images. Once the static (size and shape) as well as dynamic characteristics (velocity, acceleration,...) of the obstacles have been computed, we create a representation of the vehicle's workspace based on these features. This representation uses constructive solid geometry (CSG) to create a convex set of obstacles defining the workspace. The tracking takes also into account obstacles which are no longer in the field of view of the sonar in the path planning phase. A well-proven nonlinear search (sequential quadratic programming) is then employed, where obstacles are expressed as constraints in the search space. This approach is less affected by local minima than classical methods using potential fields. The proposed system is not only capable of obstacle avoidance but also of path planning in complex environments which include fast moving obstacles. Results obtained on real sonar data are shown and discussed. Possible applications to sonar servoing and real-time motion estimation are also discussed.

Cooperative localization and control for multi-robot manipulation

Abstract: We describe a framework for coordinating multiple robots in cooperative manipulation tasks in which vision is used for establishing relative position and orientation and maintaining formation. The two key contributions are a cooperative scheme for localizing the robots based on visual imagery that is more robust than decentralized localization, and a set of control algorithms that allow the robots to maintain a prescribed formation (shape and size). The ability to maintain a prescribed formation allows the robots to "trap" objects in their midst, and to "flow" the formation to a desired position. We derive the cooperative localization and control algorithms and present experimental results that illustrate the implementation and the performance of these algorithms.

Footstep planning among obstacles for biped robots

Abstract: We present an algorithm for planning safe navigation strategies for biped robots moving in obstacle-cluttered environments. From a discrete set of plausible statically-stable, single-step motions, a forward dynamic programming approach is used to compute a sequence of feasible footstep locations. In contrast to existing navigation strategies for mobile robots, our method is a global method that takes into account the unique ability of legged robots such as bipedal humanoids to traverse obstacles by stepping over them. Heuristics designed to minimize the number and complexity of the step motions are used to encode cost functions used for searching a footstep transition graph. We show preliminary results of an experimental implementation of the algorithm using a model of the H6 humanoid navigating on an office floor littered with obstacles.

A cooperative search framework for distributed agents

Abstract: This paper presents an approach for cooperative search of a team of distributed agents. We consider two or more agents, or vehicles, moving in a geographic environment, searching for targets of interest and avoiding obstacles or threats. The moving agents are equipped with sensors to view a limited region of the environment they are visiting, and are able to communicate with one another to enable cooperation. The agents are assumed to have some "physical" limitations including possibly maneuverability limitations, fuel/time constraints and sensor range and accuracy. The developed cooperative search framework is based on two inter-dependent tasks: (1) online learning of the environment and storing of the information in the form of a "search map"; and (2) utilization of the search map and other information to compute online a guidance trajectory for the agent to follow. The distributed learning and planning approach for cooperative search is illustrated by computer simulations.

Optimizing schedules for prioritized path planning of multi-robot systems

Abstract: The coordination of robot motions is one of the fundamental problems for multi-robot systems. A popular approach to avoid planning in the high-dimensional composite configuration space is the prioritized and decoupled technique. In this paper we present a method for optimizing priority schemes for such prioritized and decoupled planning technique. Our approach performs a randomized search with hill-climbing to find solutions and to minimize the overall path lengths. The technique has been implemented and tested on real robots and in extensive simulation runs. The experimental results demonstrate that our method is able to greatly reduce the number of failures and to significantly reduce the overall path length for different prioritized and decoupled path planning techniques and even for large teams of robots.

Kinematic controllability for decoupled trajectory planning in underactuated mechanical systems

Abstract: We introduce the notion of kinematic controllability for second-order underactuated mechanical systems. For systems satisfying this property, the problem of planning fast collision-free trajectories between zero velocity states can be decoupled into the computationally simpler problems of path planning for a kinematic system followed by time-optimal time scaling. While this approach is well known for fully actuated systems, until now there has been no way to apply it to underactuated dynamic systems. The results in this paper form the basis for efficient collision-free trajectory planning for a class of underactuated mechanical systems including manipulators and vehicles in space and underwater environments.

Vision based navigation for an unmanned aerial vehicle

Abstract: We are developing a system for autonomous navigation of unmanned aerial vehicles (UAVs) based on computer vision. A UAV is equipped with on-board cameras and each UAV is provided with noisy estimates of its own state, coming from GPS/INS. The mission of the UAV is low altitude navigation from an initial position to a final position in a partially known 3-D environment while avoiding obstacles and minimizing path length. We use a hierarchical approach to path planning. We distinguish between a global offline computation, based on a coarse known model of the environment and a local online computation, based on the information coming from the vision system. A UAV builds and updates a virtual 3-D model of the surrounding environment by processing image sequences and fusing them with sensor data. Based on such a model the UAV will plan a path from its current position to the terminal point. It will then follow such path, getting more data from the on-board cameras, and refining map and local path in real time.

Randomized path planning for linkages with closed kinematic chains

Abstract: We extend randomized path planning algorithms to the case of articulated robots that have closed kinematic chains. This is an important class of problems, which includes applications such as manipulation planning using multiple open-chain manipulators that cooperatively grasp an object and planning for reconfigurable robots in which links might be arranged in a loop to ease manipulation or locomotion. Applications also exist in areas beyond robotics, including computer graphics, computational chemistry, and virtual prototyping. Such applications typically involve high degrees of freedom and a parameterization of the configurations that satisfy closure constraints is usually not available. We show how to implement key primitive operations of randomized path planners for general closed kinematics chains. These primitives include the generation of random free configurations and the generation of local paths. To demonstrate the feasibility of our primitives for general chains, we show their application to recently developed randomized planners and present computed results for high-dimensional problems.

Smooth motion planning for car-like vehicles

Abstract: Presents a steering method for a car-like vehicle providing smooth paths subjected to curvature constraints. We show how to integrate this steering method in a global motion planning scheme taking obstacles into account. The main idea of the paper is to consider the car as a 4-D system from a kinematic point of view and as a 3-D system from a geometric point of view of collision checking. The resulting planned motions are guaranteed to be collision-free and C/sup 2/ between two cusp points.

Motion planning in dynamic environments: obstacles moving along arbitrary trajectories

Abstract: This paper generalizes the concept of velocity obstacles given by Fiorini et al. (1998) to obstacles moving along arbitrary trajectories. We introduce the nonlinear velocity obstacle, which takes into account the shape, velocity and path curvature of the moving obstacle. The nonlinear v-obstacle allows selecting a single avoidance maneuver (if one exists) that avoids any number of obstacles moving on any known trajectories. For unknown trajectories, the nonlinear v-obstacles can be used to generate local avoidance maneuvers based on the current velocity and path curvature of the moving obstacle. This elevates the planning strategy to a second order method, compared to the first order avoidance using the linear v-obstacle, and zero order avoidance using only position information. Analytic expressions for the nonlinear v-obstacle are derived for general trajectories in the plane. The nonlinear v-obstacles are demonstrated in a complex traffic example.

A Modular Self-Reconfigurable Bipartite Robotic System: Implementation and Motion Planning

Abstract: In this manuscript, we discuss i>I-Cubes, a class of modular robotic system that is capable of reconfiguring itself in 3-D to adapt to its environment This is a bipartite system, ie, a collection of (i) active elements for actuation, and (ii) passive elements acting as connectors Active elements (i>links) are 3-DOF manipulators that are capable of attaching/detaching from/to the passive elements (i>cubes), which can be positioned and oriented using links Self-reconfiguration capability enables the system to perform locomotion tasks over difficult terrains the shape and size can be changed according to the task This paper describes the design of the system, and 3-D reconfiguration properties Specifics of the hardware implementation, results of the experiments with the current prototypes, our approach to motion planning and problems related to 3-D motion planning are given

Trajectory Planning of Differentially Flat Systems with Dynamics and Inequalities

Abstract: Trajectory planning of dynamic systems, in near real-time, is important for aerospace systems, specially, unmanned air vehicles and launched munitions. Trajectory plans that do not consider the governing dynamic equations, applicable path and actuator constraints may be unrealizable during execution. The purpose of this paper is to propose a trajectory planning scheme for a class of dynamic systems, referred to as differentially flat systems. The planner is motivated from on-line computations and is aimed to satisfy the state equations, path and actuator constraints, and given initial and terminal constraints. The essence of the approach is demonstrated by two examples: (i) a hardware implementation on a spring-mass-damper system to demonstrate real-time capabilities during pursuit; (ii) trajectory planning of a PVTOL to illustrate the application to nonlinear problems.

Planning Paths for Elastic Objects under Manipulation Constraints

Abstract: This paper addresses the problem of planning paths for an elastic object from an initial to a final configuration in a static environment. It is assumed that the object is manipulated by two actuators and that it does not touch the obstacles in its environment at any time. The object may need to deform to achieve a collision-free path from the initial to the final configuration. Any required deformations are automatically computed by the planner according to the principles of elasticity theory from mechanics. The problem considered in this paper differs significantly from that of planning for a rigid or an articulated object. In the first part of the paper, the authors point out these differences and highlight the reasons that make planning for elastic objects an extremely difficult task. The authors then present a randomized algorithm for computing collision-free paths for elastic objects under the above-mentioned restrictions of manipulation. The paper includes a number of experimental results. The work...

Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing

Abstract: The artificial potential field methods provide simple and effective motion planners for practical purposes. However, there is a major problem with the artificial potential field approach. It is the formation of local minima that can trap the robot before reaching its goal. The avoidance of local minima has been an active research topic in potential field path planning. As one of the powerful techniques for escaping local minima, simulated annealing has been applied to local and global path planning. In this paper, the authors present and apply the mobile robot path planning technique which integrate the artificial potential field approach with simulated annealing to mobile robots.

A framework for real-time path planning in changing environments

Abstract: We present a new method for generating collision-free paths for robots operating in changing environments. Our approach is closely related to recent probabilistic roadmap approaches. These planners use preprocessing and query stages, and are aimed at planning many times in the same environment. In contrast, our preprocessing stage creates a representation of the configuration space that can be easily modified in real time to account for changes in the environment. As with previous approaches, we begin by constructing a graph that represents a roadmap in the configuration space, but we do not construct this graph for a specific workspace. Instead, we construct the graph for an obstacle-free workspace, and encode the mapping from workspace cells to nodes and arcs in the graph. When the environment changes, this mapping is used to make the appropriate modifications to the graph, and plans can be generated by searching the modified graph. As part of our presentation, we discuss the construction of the roadmap, including how samples of the configuration space may be generated and how to connect the samples to form the roadmap. We then discuss the mapping from the workspace to the roadmap, explaining efficient techniques for its generation and representation. We continue with some robustness measures and how these may be used to enhance the robustness of the roadmap. We then present an extension of our method for mobile robots. Finally, we evaluate an implementation of our approach for serial-link manipulators with up to 20 joints.

The chaotic mobile robot

Abstract: In this paper, we develop a method to impart the chaotic nature to a mobile robot. The chaotic mobile robot implies a mobile robot with a controller that ensures chaotic motions. Chaotic motion is characterized by the topological transitivity and the sensitive dependence on initial conditions. Due to the topological transitivity, the chaotic mobile robot is guaranteed to scan the whole connected workspace. For scanning motion, the chaotic robot neither requires the map of the workspace nor plans the global motion. It only requires the measurement of the local normal of the workspace boundary when it comes close to it. We design the controller such that the total dynamics of the mobile robot is represented by the Arnold equation, which is known to show the chaotic behavior of noncompressive perfect fluid. Experimental results and their analysis illustrate the usefulness of the proposed controller.

Motion planning for humanoid robots under obstacle and dynamic balance constraints

Abstract: We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using the humanoid robot "H6".

Space syntax based agent simulation

Abstract: Space syntax derives from a set of analytic measures of configuration that have been shown to correlate well with how people move through and use buildings and urban environments. Space syntax represents the open space of an environment in terms of the intervisibility of points in space. The measures are thus purely configurational, and take no account of attractors, nor do they make any assumptions about origins and destinations or path planning. Space syntax has found that, despite many proposed higher-level cognitive models, there appears to be a fundamental process that informs human and social usage of an environment. In this paper we describe an exosomatic visual architecture, based on space syntax visibility graphs, giving many agents simultaneous access to the same pre-processed information about the configuration of a space layout. Results of experiments in a simulated retail environment show that a surprisingly simple ‘random next step’ based rule outperforms a more complex ‘destination based’ rule in reproducing observed human movement behaviour. We conclude that the effects of spatial configuration on movement patterns that space syntax studies have found are consistent with a model of individual decision behaviour based on the spatial affordances offered by the morphology of the local visual field.

Coordinated motion planning and control of autonomous underwater vehicle-manipulator systems subject to drag optimization

Abstract: A new motion coordination algorithm for an autonomous underwater vehicle-manipulator system (UVMS) is proposed. This algorithm generates the desired trajectories for both the vehicle and the manipulator in such a way that the total hydrodynamic drag on the system is minimized. Resolution of kinematic redundancy of the system is performed at the acceleration level so that this algorithm can be incorporated into the system dynamics. The dynamics of the UVMS are modeled using a quasi-Lagrange approach. A state-space formulation of the system along with a model-based controller design for trajectory-following tasks that includes thruster dynamics is also presented. The computer simulation results demonstrate the effectiveness of this proposed method in reducing the drag on the system.

Real-time tracking meets online grasp planning

Abstract: Describes a synergistic integration of a grasping simulator and a real-time visual tracking system, that work in concert to (1) find an object's pose, (2) plan grasps and movement trajectories, and (3) visually monitor task execution. Starting with a CAD model of an object to be grasped, the system can find the object's pose through vision which then synchronizes the state of the robot workcell with an online, model-based grasp planning and visualization system we have developed called GraspIt. GraspIt can then plan a stable grasp for the object, and direct the robotic hand system to perform the grasp. It can also generate trajectories for the movement of the grasped object, which are used by the visual control system to monitor the task and compare the actual grasp and trajectory with the planned ones. We present experimental results using typical grasping tasks.