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Showing papers on "Motion planning published in 2009"


01 Jan 2009
TL;DR: This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.
Abstract: Boss is an autonomous vehicle that uses on-board sensors (global positioning system, lasers, radars, and cameras) to track other vehicles, detect static obstacles, and localize itself relative to a road model. A three-layer planning system combines mission, behavioral, and motion planning to drive in urban environments. The mission planning layer considers which street to take to achieve a mission goal. The behavioral layer determines when to change lanes and precedence at intersections and performs error recovery maneuvers. The motion planning layer selects actions to avoid obstacles while making progress toward local goals. The system was developed from the ground up to address the requirements of the DARPA Urban Challenge using a spiral system development process with a heavy emphasis on regular, regressive system testing. During the National Qualification Event and the 85-km Urban Challenge Final Event, Boss demonstrated some of its capabilities, qualifying first and winning the challenge. © 2008 Wiley Periodicals, Inc.

1,275 citations


Proceedings ArticleDOI
12 May 2009
TL;DR: This paper presents CHOMP, a novel method for continuous path refinement that uses covariant gradient techniques to improve the quality of sampled trajectories and relax the collision-free feasibility prerequisite on input paths required by those strategies.
Abstract: Existing high-dimensional motion planning algorithms are simultaneously overpowered and underpowered. In domains sparsely populated by obstacles, the heuristics used by sampling-based planners to navigate “narrow passages” can be needlessly complex; furthermore, additional post-processing is required to remove the jerky or extraneous motions from the paths that such planners generate. In this paper, we present CHOMP, a novel method for continuous path refinement that uses covariant gradient techniques to improve the quality of sampled trajectories. Our optimization technique both optimizes higher-order dynamics and is able to converge over a wider range of input paths relative to previous path optimization strategies. In particular, we relax the collision-free feasibility prerequisite on input paths required by those strategies. As a result, CHOMP can be used as a standalone motion planner in many real-world planning queries. We demonstrate the effectiveness of our proposed method in manipulation planning for a 6-DOF robotic arm as well as in trajectory generation for a walking quadruped robot.

811 citations


Journal ArticleDOI
TL;DR: The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.
Abstract: This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree (RRT) approach, applicable to autonomous vehicles operating in an urban environment. Extensions to the standard RRT are predominantly motivated by: 1) the need to generate dynamically feasible plans in real-time; 2) safety requirements; 3) the constraints dictated by the uncertain operating (urban) environment. The primary novelty is in the use of closed-loop prediction in the framework of RRT. The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.

802 citations


Journal ArticleDOI
TL;DR: A framework to automatically generate a hybrid controller that guarantees that the robot can achieve its task when a robot model, a class of admissible environments, and a high-level task or behavior for the robot are provided.
Abstract: This paper provides a framework to automatically generate a hybrid controller that guarantees that the robot can achieve its task when a robot model, a class of admissible environments, and a high-level task or behavior for the robot are provided. The desired task specifications, which are expressed in a fragment of linear temporal logic (LTL), can capture complex robot behaviors such as search and rescue, coverage, and collision avoidance. In addition, our framework explicitly captures sensor specifications that depend on the environment with which the robot is interacting, which results in a novel paradigm for sensor-based temporal-logic-motion planning. As one robot is part of the environment of another robot, our sensor-based framework very naturally captures multirobot specifications in a decentralized manner. Our computational approach is based on first creating discrete controllers satisfying specific LTL formulas. If feasible, the discrete controller is then used to guide the sensor-based composition of continuous controllers, which results in a hybrid controller satisfying the high-level specification but only if the environment is admissible.

717 citations


Proceedings ArticleDOI
10 Oct 2009
TL;DR: A novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment is presented and improvement in hindrance-sensitive robot trajectory planning is quantitatively shown.
Abstract: We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.

490 citations


Journal ArticleDOI
TL;DR: This paper designs a control law that enables the dynamic model to track a simpler kinematic model with a globally bounded error and builds a robust temporal logic specification that takes into account the tracking errors of the first step.

467 citations


Journal ArticleDOI
TL;DR: A direct transcription method is presented that reduces finding the globally optimal trajectory to solving a second-order cone program using robust numerical algorithms that are freely available.
Abstract: This paper focuses on time-optimal path tracking, a subproblem in time-optimal motion planning of robot systems. Through a nonlinear change of variables, the time-optimal path tracking problem is transformed here into a convex optimal control problem with a single state. Various convexity-preserving extension are introduced, resulting in a versatile approach for optimal path tracking. A direct transcription method is presented that reduces finding the globally optimal trajectory to solving a second-order cone program using robust numerical algorithms that are freely available. Validation against known examples and application to a more complex example illustrate the versatility and practicality of the new method.

451 citations


Journal ArticleDOI
01 Jun 2009
TL;DR: A novel proposal to solve the problem of path planning for mobile robots based on Simple Ant Colony Optimization Meta-Heuristic (SACO-MH), named SACOdm, where d stands for distance and m for memory.
Abstract: In the Motion Planning research field, heuristic methods have demonstrated to outperform classical approaches gaining popularity in the last 35 years. Several ideas have been proposed to overcome the complex nature of this NP-Complete problem. Ant Colony Optimization algorithms are heuristic methods that have been successfully used to deal with this kind of problems. This paper presents a novel proposal to solve the problem of path planning for mobile robots based on Simple Ant Colony Optimization Meta-Heuristic (SACO-MH). The new method was named SACOdm, where d stands for distance and m for memory. In SACOdm, the decision making process is influenced by the existing distance between the source and target nodes; moreover the ants can remember the visited nodes. The new added features give a speed up around 10 in many cases. The selection of the optimal path relies in the criterion of a Fuzzy Inference System, which is adjusted using a Simple Tuning Algorithm. The path planner application has two operating modes, one is for virtual environments, and the second one works with a real mobile robot using wireless communication. Both operating modes are global planners for plain terrain and support static and dynamic obstacle avoidance.

366 citations


Journal ArticleDOI
TL;DR: ESIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot, and a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-ro robot problem.
Abstract: The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-robot problem. This procedure approximately generalizes any guarantees for the single-robot problem to the multi-robot case. We extensively evaluate the effectiveness of our approach on several experiments performed infield for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets.

352 citations


Journal ArticleDOI
TL;DR: It is shown that planning in belief space can be performed efficiently for linear Gaussian systems by using a factored form of the covariance matrix, allowing several prediction and measurement steps to be combined into a single linear transfer function, leading to very efficient posterior belief prediction during planning.
Abstract: When a mobile agent does not know its position perfectly, incorporating the predicted uncertainty of future position estimates into the planning process can lead to substantially better motion performance However, planning in the space of probabilistic position estimates, or belief space, can incur a substantial computational cost In this paper, we show that planning in belief space can be performed efficiently for linear Gaussian systems by using a factored form of the covariance matrix This factored form allows several prediction and measurement steps to be combined into a single linear transfer function, leading to very efficient posterior belief prediction during planning We give a belief-space variant of the probabilistic roadmap algorithm called the belief roadmap (BRM) and show that the BRM can compute plans substantially faster than conventional belief space planning We conclude with performance results for an agent using ultra-wide bandwidth radio beacons to localize and show that we can efficiently generate plans that avoid failures due to loss of accurate position estimation

331 citations


Proceedings ArticleDOI
12 May 2009
TL;DR: CBIRRT extends the Bi-directional RRT (BiRRT) algorithm by using projection techniques to explore the configuration space manifolds that correspond to constraints and to find bridges between them, and can solve many problems that the BiRRT cannot.
Abstract: We present the Constrained Bi-directional Rapidly-Exploring Random Tree (CBiRRT) algorithm for planning paths in configuration spaces with multiple constraints. This algorithm provides a general framework for handling a variety of constraints in manipulation planning including torque limits, constraints on the pose of an object held by a robot, and constraints for following workspace surfaces. CBiRRT extends the Bi-directional RRT (BiRRT) algorithm by using projection techniques to explore the configuration space manifolds that correspond to constraints and to find bridges between them. Consequently, CBiRRT can solve many problems that the BiRRT cannot, and only requires one additional parameter: the allowable error for meeting a constraint. We demonstrate the CBiRRT on a 7DOF WAM arm with a 4DOF Barrett hand on a mobile base. The planner allows this robot to perform household tasks, solve puzzles, and lift heavy objects.

Journal ArticleDOI
TL;DR: This work proposes a representation and a planning algorithm able to deal with problems integrating task planning as well as motion and manipulation planning knowledge involving several robots and objects and describes the main features of an implemented planner.
Abstract: We propose a representation and a planning algorithm able to deal with problems integrating task planning as well as motion and manipulation planning knowledge involving several robots and objects. Robot plans often include actions where the robot has to place itself in some position in order to perform some other action or to "modify" the configuration of its environment by displacing objects. Our approach aims at establishing a bridge between task planning and manipulation planning that allows a rigorous treatment of geometric preconditions and effects of robot actions in realistic environments. We show how links can be established between a symbolic description and its geometric counterpart and how they can be used in an integrated planning process that is able to deal with intricate symbolic and geometric constraints. Finally, we describe the main features of an implemented planner and discuss several examples of its use.

Journal ArticleDOI
TL;DR: This work proposes a tightly coupled approach, in which sensor models and estimation objectives are used online for path planning, and seeks to develop decentralized, autonomous control strategies that can account for a wide variety of sensing missions.
Abstract: Unmanned aerial vehicles (UAVs) have shown promise in recent years for autonomous sensing. UAVs systems have been proposed for a wide range of applications such as mapping, surveillance, search, and tracking operations. The recent availability of low-cost UAVs suggests the use of teams of vehicles to perform sensing tasks. To leverage the capabilities of a team of vehicles, efficient methods of decentralized sensing and cooperative path planning are necessary. The goal of this work is to examine practical control strategies for a team of fixed-wing vehicles performing cooperative sensing. We seek to develop decentralized, autonomous control strategies that can account for a wide variety of sensing missions. Sensing goals are posed from an information theoretic standpoint to design strategies that explicitly minimize uncertainty. This work proposes a tightly coupled approach, in which sensor models and estimation objectives are used online for path planning.

Journal ArticleDOI
TL;DR: A Bayesian optimization method that dynamically trades off exploration and exploitation for optimal sensing with a mobile robot and is applicable to other closely-related domains, including active vision, sequential experimental design, dynamic sensing and calibration with mobile sensors.
Abstract: We address the problem of online path planning for optimal sensing with a mobile robot. The objective of the robot is to learn the most about its pose and the environment given time constraints. We use a POMDP with a utility function that depends on the belief state to model the finite horizon planning problem. We replan as the robot progresses throughout the environment. The POMDP is high-dimensional, continuous, non-differentiable, nonlinear, non-Gaussian and must be solved in real-time. Most existing techniques for stochastic planning and reinforcement learning are therefore inapplicable. To solve this extremely complex problem, we propose a Bayesian optimization method that dynamically trades off exploration (minimizing uncertainty in unknown parts of the policy space) and exploitation (capitalizing on the current best solution). We demonstrate our approach with a visually-guide mobile robot. The solution proposed here is also applicable to other closely-related domains, including active vision, sequential experimental design, dynamic sensing and calibration with mobile sensors.

Journal ArticleDOI
TL;DR: The proposed algorithm that uses only passive RFID is able to estimate the robot's location and orientation more precisely by using trigonometric functions and the IC tags' Cartesian coordinates in a regular gridlike pattern.
Abstract: This paper proposes an efficient method for localization and pose estimation for mobile robot navigation using passive radio-frequency identification (RFID). We assume that the robot is able to identify IC tags and measure the robot's pose based on the relation between the previous and current location according to the IC tags. However, there arises the problem of uncertainty of location due to the nature of the antenna and IC tags. In other words, an error is always present which is relative to the sensing area of the antenna. Many researches have used external sensors in order to reduce the location errors, with few researches presented involving purely RFID driven systems. Our proposed algorithm that uses only passive RFID is able to estimate the robot's location and orientation more precisely by using trigonometric functions and the IC tags' Cartesian coordinates in a regular gridlike pattern. The experimental results show that the proposed method effectively estimates both the location and the pose of a mobile robot during navigation.

Journal IssueDOI
TL;DR: Two new approaches to solve the coverage path planning problem in the case of agricultural fields and agricultural machines are presented for consideration and are greedy algorithms that are applicable to both robots and human-driven machines.
Abstract: In this article, a coverage path planning problem is discussed in the case of agricultural fields and agricultural machines. Methods and algorithms to solve this problem are developed. These algorithms are applicable to both robots and human-driven machines. The necessary condition is to cover the whole field, and the goal is to find as efficient a route as possible. As yet, there is no universal algorithm or method capable of solving the problem in all cases. Two new approaches to solve the coverage path planning problem in the case of agricultural fields and agricultural machines are presented for consideration. Both of them are greedy algorithms. In the first algorithm the view is from on top of the field, and the goal is to split a single field plot into subfields that are simple to drive or operate. This algorithm utilizes a trapezoidal decomposition algorithm, and a search is developed of the best driving direction and selection of subfields. This article also presents other practical aspects that are taken into account, such as underdrainage and laying headlands. The second algorithm is also an incremental algorithm, but the path is planned on the basis of the machine's current state and the search is on the next swath instead of the next subfield. There are advantages and disadvantages with both algorithms, neither of them solving the problem of coverage path planning problem optimally. Nevertheless, the developed algorithms are remarkable steps toward finding a way to solve the coverage path planning problem with nonomnidirectional vehicles and taking into consideration agricultural aspects. © 2009 Wiley Periodicals, Inc.

Journal ArticleDOI
TL;DR: An efficient, Bezier curve based approach for the path planning of a mobile robot in a multi-agent robot soccer system and an obstacle avoidance scheme is incorporated for dealing with the stationary and moving obstacles.

Proceedings ArticleDOI
10 Oct 2009
TL;DR: By reparametrising the configuration space to match the course of the road, it can be sampled very economically with few vertices, and this reduces absolute runtime further and the trajectories generated are quintic splines.
Abstract: We present a method for motion planning in the presence of moving obstacles that is aimed at dynamic on-road driving scenarios. Planning is performed within a geometric graph that is established by sampling deterministically from a manifold that is obtained by combining configuration space and time. We show that these graphs are acyclic and shortest path algorithms with linear runtime can be employed. By reparametrising the configuration space to match the course of the road, it can be sampled very economically with few vertices, and this reduces absolute runtime further. The trajectories generated are quintic splines. They are second order continuous, obey nonholonomic constraints and are optimised for minimum square of jerk. Planning time remains below 20 ms on general purpose hardware.

01 Jan 2009
TL;DR: The architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data are described, providing a strong platform for future research in autonomous driving in GPS-denied and highly dynamic environments with poor a priori information.
Abstract: This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high-resolution, high-rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kinodynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three-point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed-loop simulation in a rapidly exploring randomized trees algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in global positioning system–denied and highly dynamic environments with poor a priori information. © 2008 Wiley Periodicals, Inc.

Proceedings ArticleDOI
10 Oct 2009
TL;DR: This paper investigates solving the inverse kinematics problem and motion planning for dual-arm manipulation and re-grasping tasks by combining a gradient-descent approach in the robot's pre-computed reachability space with random sampling of free parameters.
Abstract: In this paper, we present efficient solutions for planning motions of dual-arm manipulation and re-grasping tasks. Motion planning for such tasks on humanoid robots with a high number of degrees of freedom (DoF) requires computationally efficient approaches to determine the robot's full joint configuration at a given grasping position, i.e. solving the Inverse Kinematics (IK) problem for one or both hands of the robot. In this context, we investigate solving the inverse kinematics problem and motion planning for dual-arm manipulation and re-grasping tasks by combining a gradient-descent approach in the robot's pre-computed reachability space with random sampling of free parameters. This strategy provides feasible IK solutions at a low computation cost without resorting to iterative methods which could be trapped by joint-limits. We apply this strategy to dual-arm motion planning tasks in which the robot is holding an object with one hand in order to generate whole-body robot configurations suitable for grasping the object with both hands. In addition, we present two probabilistically complete RRT-based motion planning algorithms (J+−RRT and IK-RRT) that interleave the search for an IK solution with the search for a collision-free trajectory and the extension of these planners to solving re-grasping problems. The capabilities of combining IK methods and planners are shown both in simulation and on the humanoid robot ARMAR-III performing dual-arm tasks in a kitchen environment.

Proceedings ArticleDOI
28 Jun 2009
TL;DR: A feedback motion planning algorithm which uses direct computation of Lyapunov functions using convex optimization to efficiently combine locally-valid linear quadratic regulator controllers into a nonlinear feedback policy which probabilistically covers the reachable area of a state space with a region of stability.
Abstract: Recent advances in the direct computation of Lyapunov functions using convex optimization make it possible to efficiently evaluate regions of stability for smooth nonlinear systems. Here we present a feedback motion planning algorithm which uses these results to efficiently combine locally-valid linear quadratic regulator (LQR) controllers into a nonlinear feedback policy which probabilistically covers the reachable area of a (bounded) state space with a region of stability, certifying that all initial conditions that are capable of reaching the goal will stabilize to the goal. We carefully investigate the algorithm on a two-dimensional model system and discuss the potential for the control of more complicated underactuated control problems like bipedal walking.

Proceedings ArticleDOI
01 Dec 2009
TL;DR: Algorithms for the on-line computation of control programs for dynamical systems that provably satisfy a class of temporal logic specifications are proposed, generalizing state-of-the-art algorithms for point-to-point motion planning.
Abstract: In this paper, we propose algorithms for the on-line computation of control programs for dynamical systems that provably satisfy a class of temporal logic specifications. Such specifications have recently been proposed in the literature as a powerful tool to synthesize provably correct control programs, for example for embedded systems and robotic applications. The proposed algorithms, generalizing state-of-the-art algorithms for point-to-point motion planning, incrementally build finite transition systems representing a discrete subset of dynamically feasible trajectories. At each iteration, local μ-calculus model-checking methods are used to establish whether the current transition system satisfies the specifications. Efficient sampling strategies are presented, ensuring the probabilistic completeness of the algorithms. We demonstrate the effectiveness of the proposed approach on simulation examples.

Book ChapterDOI
01 Jan 2009
TL;DR: Applications of motion planning have also expanded to fields such as graphics and computational biology, and the field that addresses planning for complex robots with kinematic and dynamic constraints is addressed.
Abstract: Over the last two decades, motion planning [4, 15, 17] has grown from a field that considered basic geometric problems to a field that addresses planning for complex robots with kinematic and dynamic constraints [5]. Applications of motion planning have also expanded to fields such as graphics and computational biology [16].

Journal ArticleDOI
TL;DR: A proof of heading convergence using feedback is complete, and a novel approach for heading convergence that does not require continuous feedback in the ideal case (no wind, stationary target), taking advantage of an analytical solution to the guidance field is proposed.
Abstract: In this paper, we present work on control of autonomous vehicle formations in the context of the coordinated stando tracking problem . The objective is to use a team of unmanned aircraft to fly a circular orbit around a moving target with prescribed inter-vehicle angular spacing using only local information. We use the recently introduced Lyapunov guidance vector field approach to achieve the desired circular trajectory. The contributions of this paper involve both single vehicle path planning and multiple vehicle coordination. For single vehicle path planning, we complete a proof of heading convergence using feedback, which has thus far not been fully addressed in the literature, and also oer a novel approach for heading convergence that does not require continuous feedback in the ideal case (no wind, stationary target), taking advantage of an analytical solution to the guidance field. Further, we use a variable airspeed controller to maintain the circular trajectory despite unknown wind and unknown constant velocity target motion. Adaptive estimates of the unknown wind and target motion are introduced to ensure stability to the circular trajectory. A novel feature of our results is rigorous satisfaction of vehicle specific kinematic constraints on heading rates and airspeed variations. For multiple vehicle coordination, we again use a variable airspeed controller to achieve the prescribed angular spacing. In an eort towards a unified framework for control of autonomous vehicle formations, we make a connection with some recent work that addresses information architecture in vehicle formations using graph theory. Specifically, we utilize two types of information architectures, symmetric and asymmetric, and implement decentralized control laws. The information architectures are scalable in the sense that the number of required communication/sensing links increases linearly with the number of vehicles. The control laws are decentralized in the sense that they use only local information.

Proceedings ArticleDOI
28 Jun 2009
TL;DR: This work develops an algorithm to decouple a multi-robot path planning problem into subproblems whose solutions can be executed sequentially, and can decouple and solve path planning problems with many robots, even with incomplete external planners.
Abstract: We develop an algorithm to decouple a multi-robot path planning problem into subproblems whose solutions can be executed sequentially. Given an external path planner for general configuration spaces, our algorithm finds an execution sequence that minimizes the dimension of the highest-dimensional subproblem over all possible execution sequences. If the external planner is complete (at least up to this minimum dimension), then our algorithm is complete because it invokes the external planner only for spaces of dimension at most this minimum. Our algorithm can decouple and solve path planning problems with many robots, even with incomplete external planners. We show scenarios involving 16 to 65 robots, where our algorithm solves planning problems of dimension 32 to 130 using a PRM planner for at most eight dimensions. 1

Proceedings ArticleDOI
10 Nov 2009
TL;DR: The COMPANION framework is introduced, which can express an arbitrary number of social conventions and explicitly accounts for these conventions in the planning phase, and it is verified that the method produces human-like behavior in a mobile robot.
Abstract: This paper introduces the COMPANION framework: a Constraint-Optimizing Method for Person-Acceptable NavigatION. In this framework, human social conventions, such as personal space and tending to one side of hallways, are represented as constraints on the robot's navigation. These constraints are accounted for at the global planning level. In this paper, we present the rationale for, and implementation of, this framework, and we describe the experiments we have run in simulation to verify that the method produces human-like behavior in a mobile robot. Our approach is novel in that it can express an arbitrary number of social conventions and explicitly accounts for these conventions in the planning phase.


Journal ArticleDOI
TL;DR: It is proved that solving the MESPP problem requires maximizing a non-decreasing, submodular objective function, which leads to theoretical bounds on the performance of the proposed linearly scalable approximation algorithm.
Abstract: This paper examines the problem of locating a mobile, non-adversarial target in an indoor environment using multiple robotic searchers. One way to formulate this problem is to assume a known environment and choose searcher paths most likely to intersect with the path taken by the target. We refer to this as the multi-robot efficient search path planning (MESPP) problem. Such path planning problems are NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We present an approximation algorithm that utilizes finite-horizon planning and implicit coordination to achieve linear scalability in the number of searchers. We prove that solving the MESPP problem requires maximizing a non-decreasing, submodular objective function, which leads to theoretical bounds on the performance of our approximation algorithm. We extend our analysis by considering the scenario where searchers are given noisy non-line-of-sight ranging measurements to the target. For this scenario, we derive and integrate online Bayesian measurement updating into our framework. We demonstrate the performance of our framework in two large-scale simulated environments, and we further validate our results using data from a novel ultra-wideband ranging sensor. Finally, we provide an analysis that demonstrates the relationship between MESPP and the intuitive average capture time metric. Results show that our proposed linearly scalable approximation algorithm generates searcher paths that are competitive with those generated by exponential algorithms.

Journal ArticleDOI
01 Jun 2009
TL;DR: A novel approximate cell-decomposition method in which obstacles, targets, sensor's platform, and FOV are represented as closed and bounded subsets of an Euclidean workspace, and these strategies outperform shortest path, complete coverage, random, and grid search strategies.
Abstract: A methodology is developed for planning the sensing strategy of a robotic sensor deployed for the purpose of classifying multiple fixed targets located in an obstacle-populated workspace. Existing path planning techniques are not directly applicable to robots whose primary objective is to gather sensor measurements using a bounded field of view (FOV). This paper develops a novel approximate cell-decomposition method in which obstacles, targets, sensor's platform, and FOV are represented as closed and bounded subsets of an Euclidean workspace. The method constructs a connectivity graph with observation cells that is pruned and transformed into a decision tree from which an optimal sensing strategy can be computed. The effectiveness of the optimal sensing strategies obtained by this methodology is demonstrated through a mine-hunting application. Numerical experiments show that these strategies outperform shortest path, complete coverage, random, and grid search strategies, and are applicable to nonoverpass capable robots that must avoid targets as well as obstacles.

Journal ArticleDOI
Loulin Huang1
TL;DR: The potential field method is applied for both path and speed planning, or the velocity planning, for a mobile robot in a dynamic environment where the target and the obstacles are moving.