Showing papers on "Motion planning published in 1997"

Mobile robot localization using landmarks

Abstract: We describe an efficient method for localizing a mobile robot in an environment with landmarks. We assume that the robot can identify these landmarks and measure their bearings relative to each other. Given such noisy input, the algorithm estimates the robot's position and orientation with respect to the map of the environment. The algorithm makes efficient use of our representation of the landmarks by complex numbers. The algorithm runs in time linear in the number of landmarks. We present results of simulations and propose how to use our method for robot navigation.

Path planning in expansive configuration spaces

Abstract: We introduce the notion of expansiveness to characterize a family of robot configuration spaces whose connectivity can be effectively captured by a roadmap of randomly-sampled milestones. The analysis of expansive configuration spaces has inspired us to develop a new randomized planning algorithm. This algorithm tries to sample only the portion of the configuration space that is relevant to the current query, avoiding the cost of precomputing a roadmap for the entire configuration space. Thus, it is well-suited for problems where a single query is submitted for a given environment. The algorithm has been implemented and successfully applied to complex assembly maintainability problems from the automotive industry.

Adaptive evolutionary planner/navigator for mobile robots

Abstract: Based on evolutionary computation (EC) concepts, we developed an adaptive evolutionary planner/navigator (EP/N) as a novel approach to path planning and navigation. The EP/N is characterized by generality, flexibility, and adaptability. It unifies off-line planning and online planning/navigation processes in the same evolutionary algorithm which 1) accommodates different optimization criteria and changes in these criteria, 2) incorporates various types of problem-specific domain knowledge, and 3) enables good tradeoffs among near-optimality of paths, high planning efficiency, and effective handling of unknown obstacles. More importantly, the EP/N can self-tune its performance for different task environments and changes in such environments, mostly through adapting probabilities of its operators and adjusting paths constantly, even during a robot's motion toward the goal.

A random sampling scheme for path planning

Abstract: Several randomized path planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type of robots, and their empirically observed success. In this paper we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot’s configuration space. We then describe two previously developed planners as instances of planners based on this scheme, but applying very different sampling strategies. These planners are probabilistically complete: if a path exists, they will find one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characterizing the “goodness” of the robot’s free space, we show that the running time only grows as the absolute value of the logarithm of the probability of failure that we are willing to tolerate. We also show that it increases at a reasonable rate as the space goodness degrades. In the last section we suggest directions for future research.

Useful metrics for modular robot motion planning

Abstract: In this paper the problem of dynamic self-reconfiguration of a class of modular robotic systems referred to as metamorphic systems is examined. A metamorphic robotic system is a collection of mechatronic modules, each of which has the ability to connect, disconnect, and climb over adjacent modules. We examine the near-optimal reconfiguration of a metamorphic robot from an arbitrary initial configuration to a desired final configuration. Concepts of distance between metamorphic robot configurations are defined, and shown to satisfy the formal properties of a metric. These metrics, called configuration metrics, are then applied to the automatic self-reconfiguration of metamorphic systems in the case when one module is allowed to move at a time. There is no simple method for computing the optimal sequence of moves required to reconfigure. As a result, heuristics which can give a near optimal solution must be used. We use the technique of simulated annealing to drive the reconfiguration process with configuration metrics as cost functions. The relative performance of simulated annealing with different cost functions is compared and the usefulness of the metrics developed in this paper is demonstrated.

Sensor fusion for mobile robot navigation

Abstract: We review techniques for sensor fusion in robot navigation, emphasizing algorithms for self-location. These find use when the sensor suite of a mobile robot comprises several different sensors, some complementary and some redundant. Integrating the sensor readings, the robot seeks to accomplish tasks such as constructing a map of its environment, locating itself in that map, and recognizing objects that should be avoided or sought. The review describes integration techniques in two categories: low-level fusion is used for direct integration of sensory data, resulting in parameter and state estimates; high-level fusion is used for indirect integration of sensory data in hierarchical architectures, through command arbitration and integration of control signals suggested by different modules. The review provides an arsenal of tools for addressing this (rather ill-posed) problem in machine intelligence, including Kalman filtering, rule-based techniques, behavior based algorithms, and approaches that borrow from information theory, Dempster-Shafer reasoning, fuzzy logic and neural networks.

Sensory-based motion planning with global proofs

Abstract: We present DistBug, a new navigation algorithm for mobile robots which exploits range data. The algorithm belongs to the Bug family, which combines local planning with global information that guarantees convergence. Most Bug-type algorithms use contact sensors and consist of two reactive modes of motion: moving toward the target between obstacles and following obstacle boundaries, DistBug uses range data in a new "leaving condition" which allows the robot to abandon obstacle boundaries as soon as global convergence is guaranteed, based on the free range in the direction of the target. The leaving condition is tested directly on the sensor readings, thus making the algorithm simple to implement. To further improve performance, local information is utilized for choosing the boundary following direction, and a search manager is introduced for bounding the search area. The simulation results indicate a significant advantage of DistBug relative to the classical Bug2 algorithm. The algorithm was implemented and tested on a real robot, demonstrating the usefulness and applicability of our approach.

Introducing the "SPHERICLE": an experimental testbed for research and teaching in nonholonomy

Abstract: In this paper we describe an experimental apparatus developed in our laboratory for research and advanced teaching purposes. The device consists of an untethered spherical vehicle that autonomously rolls on the laboratory floor, and can reach arbitrary positions and orientations in the environment. The kinematics of the vehicle are nonholonomic and result from the combination of the kinematics of two classical nonholonomic systems, namely, a unicycle and a plate-ball system. The "SPHERICLE" introduces features that are new with respect to the two systems.

Autonomous exploration: driven by uncertainty

Abstract: Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously, they will always be faced with this dilemma, and can only be successful if they play a much more active role. This paper presents such a machine. It deliberately seeks out those parts of the world which maximize the fidelity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm autonomous exploration, and the machine an autonomous explorer. This paper has two major contributions. The first is a theory that tells us how to explore, and which confirms the intuitive ideas we have put forward previously. The second is an implementation of that theory. In our laboratory, we have constructed a working autonomous explorer and here, for the first time, show it in action. The system is entirely bottom-up and does not depend on any a priori knowledge of the environment. To our knowledge, it is the first to have successfully closed the loop between gaze planning and the inference of complex 3D models.

Motion strategies for maintaining visibility of a moving target

Abstract: We introduce the problem of computing robot motion strategies that maintain visibility of a moving target in a cluttered workspace. Both motion constraints (as considered in standard motion planning) and visibility constraints (as considered in visual tracking) must be satisfied. Additional criteria, such as the total distance traveled, can be optimized. The general problem is divided into two categories, on the basis of whether the target is predictable. For the predictable case, an algorithm that computes optimal, numerical solutions is presented. For the more challenging case of a partially-predictable target, two online algorithms are presented that each attempt to maintain future visibility with limited prediction. One strategy maximizes the probability that the target will remain in view in a subsequent time step, and the other maximizes the minimum time in which the target could escape the visibility region. We additionally discuss issues resulting from our implementation and experiments on a mobile robot system.

The autonomous mobile robot SENARIO: a sensor aided intelligent navigation system for powered wheelchairs

Abstract: The SENARIO project is develoing a sensor-aided intelligent navigation system that provides high-level navigational aid to users of powered wheelchairs. The authors discuss new and improved technologies developed within SENARIO concerning task/path planning, sensing and positioning for indoor mobile robots as well as user interface issues. The autonomous mobile robot SENARIO, supports semi- or fully autonomous navigation. In semi-autonomous mode the system accepts typical motion commands through a voice-activated or standard joystick interface and supports robot motion with obstacle/collision avoidance features. Fully autonomous mode is a superset of semi-autonomous mode with the additional ability to execute autonomously high-level go-to-goal commands. At its current stage, the project has succeeded in fully supporting semi-autonomous navigation, while experiments on the fully autonomous mode are very encouraging.

Stereo vision based mapping and navigation for mobile robots

Abstract: This paper describes a visually guided robot that can plan paths, construct maps and explore an indoor environment. The robot uses a trinocular stereo vision system to produce highly accurate depth images at 2 Hz allowing it to safely travel through the environment at 0.5 m/s. The algorithm integrates stereo vision, occupancy grid mapping, and potential field path planning techniques to form a robust and cohesive robotic system for mapping and navigation. Stereo vision is shown to be a viable alternative to active sensing devices such as sonar and laser range finders.

Finding an unpredictable target in a workspace with obstacles

Abstract: This paper introduces a visibility-based motion planning problem in which the task is to coordinate the motions of one or more robots that have omnidirectional vision sensors, to eventually "see" a target that is unpredictable, has unknown initial position, and is capable of moving arbitrarily feast. A visibility region is associated with each robot, and the goal is to guarantee that the target will ultimately lie in at least one visibility region. Both a formal characterization of the general problem and several interesting problem instances are presented. A complete algorithm for computing the motion strategy of the robots is also presented, and is based on searching a finite cell complex that is constructed on the basis of critical information changes. A few computed solution strategies are shown. Several bounds on the minimum number of needed robots are also discussed.

Dynamic path modification for car-like nonholonomic mobile robots

Abstract: A method combining planning and reactive control for car-like nonholonomic mobile robots is discussed. Firstly, a "bubble" for a car-like mobile robot is defined as the locally reachable space from a given configuration considering the obstacles and using the appropriate metric. Then a flexible feasible trajectory, based on the elastic band concepts, is constructed. This trajectory is smoothed using Bezier curves satisfying a minimum curvature constraint, and a parameterization is proposed which satisfies the robot kinematics constraints.

Genetic algorithms for adaptive motion planning of an autonomous mobile robot

Abstract: This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and online motion planning. We first present a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are presented. Then, we discuss extensions of the GA for solving both path planning and trajectory planning simultaneously.

Decentralized motion planning for multiple mobile robots: the cocktail party model

Abstract: This paper presents an approach for decentralized real-time motion planning for multiple mobile robots operating in a common 2-dimensional environment with unknown stationary obstacles. In our model, a robot can see (sense) the surrounding objects. It knows its current and its target‘s position, is able to distinguish a robot from an obstacle, and can assess the instantaneous motion of another robot. Other than this, a robot has no knowledge about the scene or of the paths and objectives of other robots. There is no mutual communication among the robots; no constraints are imposed on the paths or shapes of robots and obstacles. Each robot plans its path toward its target dynamically, based on its current position and the sensory feedback; only the translation component is considered for the planning purposes. With this model, it is clear that no provable motion planning strategy can be designed (a simple example with a dead-lock is discussed); this naturally points to heuristic algorithms. The suggested strategy is based on maze-searching techniques. Computer simulation results are provided that demonstrate good performance and a remarkable robustness of the algorithm (meaning by this a virtual impossibility to create a dead-lock in a “random” scene).

Generation of conflict resolution manoeuvres for air traffic management

Abstract: We explore the use of distributed online motion planning algorithms for multiple mobile agents, in air traffic management systems (ATMS). The work is motivated by current trends in ATMS to move towards decentralized air traffic management, in which the aircraft operate in "free flight" mode instead of following prespecified "sky freeways". Conflict resolution strategies are an integral part of the free flight setting. The purpose of this paper is to obtain a set of manoeuvres to cover all possible conflict scenarios involving multiple agents. A distributed motion planning algorithm based on potential and vortex fields is used. While the algorithm is not always guaranteed to generate flyable trajectories, the obtained trajectories can serve as qualitative prototypes for coordination manoeuvres between multiple aircraft. The actual manoeuvres are generated by approximating these prototypes with trajectories made zip of straight lines and are further verified using hybrid verification techniques.

Building a global map of the environment of a mobile robot: the importance of correlations

Abstract: The work presented in this paper is aimed at evaluating the influence of correlations between map entities on the process of robot relocation and global map building of the environment of a mobile robot navigating in an indoor environment. An EKF filter approach, supported by a probabilistic model to represent uncertain geometric information, is used to process the information obtained by the sensors mounted on the robot. We have developed two approaches, first, considering the existence of correlations, and second assuming independence between entities of the map. We have experimented with the mobile robot MACROBE, using its laser rangefinder.

Incremental algorithms for collision detection between polygonal models

Abstract: Fast and accurate collision detection between general polygonal models is a fundamental problem in physically based and geometric modeling, robotics, animation, and computer-simulated environments. Most earlier collision detection algorithms are either restricted to a class of models (such as convex polytopes) or are not fast enough for practical applications. The authors present an incremental algorithm for collision detection between general polygonal models in dynamic environments. The algorithm combines a hierarchical representation with incremental computation to rapidly detect collisions. It makes use of coherence between successive instances to efficiently determine the number of object features interacting. For each pair of objects, it tracks the closest features between them on their respective convex hulls. It detects the objects' penetration using pseudo internal Voronoi cells and constructs the penetration region, thus identifying the regions of contact on the convex hulls. The features associated with these regions are represented in a precomputed hierarchy. The algorithm uses a coherence based approach to quickly traverse the precomputed hierarchy and check for possible collisions between the features. They highlight its performance on different applications.

Complete coverage path planning and guidance for cleaning robots

Abstract: This paper describes a complete coverage path planning and guidance methodology for a mobile robot, having the automatic floor cleaning of large industrial areas as a target application. The proposed algorithms rely on the a priori knowledge of a 2D map of the environment and cope with unexpected obstacles not represented on the map. A template based approach is used to control the path execution, thus incorporating, in a natural way, the kinematic and the geometric model of the mobile robot on the path planning procedure. The novelty of the proposed approach is the capability of the path planner to deal with a priori mapped or unexpected obstacles in the middle of the working space. If unmapped obstacles permanently block the planned trajectory, the path tracking control avoids these obstacles. The paper presents experimental results with a LABMATE mobile robot, confirming the feasibility of the total coverage path and the robustness of the path tracking behaviour based control.

A path space approach to nonholonomic motion planning in the presence of obstacles

Abstract: This paper presents an algorithm for finding a kinematically feasible path for a nonholonomic system in the presence of obstacles. We first consider the path planning problem without obstacles by transforming it into a nonlinear least squares problem in an augmented space which is then iteratively solved. Obstacle avoidance is included as inequality constraints. Exterior penalty functions are used to convert the inequality constraints Into equality constraints. Then the same nonlinear least squares approach is applied. We demonstrate the efficacy of the approach by solving some challenging problems, including a tractor-trailer and a tractor with a steerable trailer backing in a loading dock. These examples demonstrate the performance of the algorithm in the presence of obstacles and steering and jackknife angle constraints.

Conflict-free motion of multiple mobile robots based on decentralized motion planning and negotiation

Abstract: This article presents a novel approach to conflict-resolution for multiple mobile robots. By use of online motion planning, a framework for negotiation is developed, which permits quick decentralized and parallel decision-making. The key objective of the negotiation procedure is dynamic assignment of robot motion priorities. Thus, robots involved in a local conflict situation cooperate in planning and execution of the lowest cost motion paths without application of any centralized components. The features required for individual and cooperative motion are embedded in a hybrid control architecture. Results obtained from realistic simulation of a multirobot environment and also from experiments performed with two mobile robots demonstrate the flexibility and the efficiency of the proposed method.

Motion planning for carlike robots using a probabilistic learning approach

Abstract: In this article, a recently developed learning approach for robot motion planning is extended and applied to two types of carlike robots: normal ones, and robots that can only move for ward. In this learning approach the motion planning process is split into two phases: the learning phase and the query phase. In the learning phase, a probabilistic roadmap is incremen tally constructed in configuration space. This roadmap is an undirected graph where nodes correspond to randomly chosen configurations in free space and edges correspond to simple collision-free paths between the nodes. These simple motions are computed using a fast local method. In the query phase, this roadmap can be used to find paths between different pairs of configurations.The approach can be applied to normal carlike robots (with nonholonomic constraints) by using suitable local methods that compute paths feasible for the robots. Application to carlike robots that can move only forward demands a more fundamen tal adaptation of the lear...

Real-time path planning using harmonic potentials in dynamic environments

Abstract: Motivated by fluid analogies, artificial harmonic potentials can eliminate local minima problems in robot path planning. In this paper, simple analytical solutions to planar harmonic potentials are derived using tools from fluid mechanics, and are applied to two-dimensional planning among multiple moving obstacles. These closed-form solutions enable real-time computation to be readily achieved.

Collision-free path planning for a diamond-shaped robot using two-dimensional cellular automata

Abstract: This paper presents a new parallel algorithm for collision-free path planning of a diamond-shaped robot among arbitrarily shaped obstacles, which are represented as a discrete image, and its implementation in VLSI. The proposed algorithm is based on a retraction of free space onto the Voronoi diagram, which is constructed through the time evolution of cellular automata, after an initial phase during which the boundaries of obstacles are identified and coded with respect to their orientation. The proposed algorithm is both space and time efficient, since it does not require the modeling of objects or distance and intersection calculations. Additionally, the proposed twodimensional multistate cellular automaton architecture achieves high frequency of operation and it is particularly suited for VLSI implementation due to its inherent parallelism, structural locality, regularity, and modularity.

Natural motion generation of biped locomotion robot using hierarchical trajectory generation method consisting of GA, EP layers

Abstract: The purpose of this research is to generate natural motion of the biped locomotion robot like a human walking in various environments. In this paper, we report a method of natural motion generation of a biped locomotion robot. We apply the hierarchical trajectory generation method to generate the trajectory of a biped locomotion robot through energy optimization and aim to generate more natural motion by considering dynamic effect. The hierarchical trajectory generation method consists of two layers, one is the GA layer which minimizes the total energy of all actuators, and the other is the evolutionary programming (EP) layer which optimizes interpolated configuration of a biped locomotion robot. Then we formulate the trajectory generation problem, as a minimizing problem of energy and apply the hierarchical trajectory generation method. Further, we build a biped locomotion robot in trial, which has 13 joints and made of aluminium materials. Finally, we confirm that the calculated natural motion trajectory can be applied successfully to the practical biped locomotion.

Nonholonomic motion planning for multiple mobile manipulators

Abstract: We address the problem of motion planning for nonholonomic cooperating mobile robots manipulating and transporting objects while holding them in a stable grasp. We present a general approach based on the calculus of variations that allows us to obtain optimal trajectories and actuator forces/torques for any manoeuvre in the presence of obstacles. In addition, geometric constraints such as joint limits, kinematic constraints such as nonholonomic velocity constraints and dynamic constraints can be easily incorporated into the planning scheme. The application of the method is illustrated by computing motion plans for several examples.

A wall-following method for escaping local minima in potential field based motion planning

Abstract: A wall-following method for escaping local minima encountered by the potential field based motion planning method used in real-time obstacle avoidance is presented. The new algorithm switches to a wall-following control mode when the robot falls into a local minimum. A switches back to the potential field guided control mode when a certain condition is met. A simple switch condition derived from monitoring the distance from the robot's position to the goal position is shown to be effective in escaping local minima in typical laboratory environments. A provision is built into the algorithm, allowing the robot to follow a wall in a different direction if the first attempt fails. The new algorithm is implemented on a Nomad 200 mobile robot. Simulation and experimental results are presented to demonstrate the usefulness of the method.

Active mobile robot localization by entropy minimization

Abstract: Localization is the problem of determining the position of a mobile robot from sensor data. Most existing localization approaches are passive, i.e., they do not exploit the opportunity to control the robot's effecters during localization. This paper proposes an active localization approach. The approach provides rational criteria for (1) setting the robot's motion direction (exploration), and (2) determining the pointing direction of the sensors so as to most efficiently localize the robot. Furthermore, it is able to deal with noisy sensors and approximate world models. The appropriateness of our approach is demonstrated empirically using a mobile robot in a structured office environment.