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34
Path planning of autonomous underwater vehicles (AUVs) for adaptive sampling
, 2005
"... Abstract—The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constrain ..."
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Cited by 25 (5 self)
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Abstract—The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constraints of the available observing network. Examples of objectives are better oceanic understanding, to improve forecast quality, or to sample regions of high interest. This work provides a new path-planning scheme for the adaptive sampling problem. We define the path-planning problem in terms of an optimization framework and propose a method based on mixed integer linear programming (MILP). The mathematical goal is to find the vehicle path that maximizes the line integral of the uncertainty of field estimates along this path. Sampling this path can improve the accuracy of the field estimates the most. While achieving this objective, several constraints must be satisfied and are implemented. They relate to vehicle motion, intervehicle coordination, communication, collision avoidance, etc. The MILP formulation is quite powerful to handle different problem constraints and flexible enough to allow easy extensions of the problem. The formulation covers single- and multiple-vehicle cases as well as singleand multiple-day formulations. The need for a multiple-day formulation arises when the ocean sampling mission is optimized for several days ahead. We first introduce the details of the formulation, then elaborate on the objective function and constraints, and finally, present a varied set of examples to illustrate the applicability of the proposed method. Index Terms—Adaptive sampling, Autonomous Ocean Sampling Network (AOSN), autonomous underwater vehicle (AUV), data
Flocking with Obstacle Avoidance: A New Distributed Coordination Algorithm Based on Voronoi Partitions
"... A new distributed coordination algorithm for multi-vehicle systems is presented in this paper. The algorithm combines a particular choice of navigation function with Voronoi partitions. This results not only in obstacle avoidance and motion to the goal, but also in a desirable geographical distribut ..."
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Cited by 16 (0 self)
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A new distributed coordination algorithm for multi-vehicle systems is presented in this paper. The algorithm combines a particular choice of navigation function with Voronoi partitions. This results not only in obstacle avoidance and motion to the goal, but also in a desirable geographical distribution of the vehicles. Our algorithm is decentralized in that each vehicle needs only to know the position of neighboring vehicles, but no other inter-vehicle communication or centralized control are required. The algorithm gives a natural priority to safety, goal convergence, and formation keeping, in that (1) collision avoidance is guaranteed under all circumstances, (2) the vehicles will move toward the goal as long as a given optimization problem is feasible, and (3) if prior criteria admit, the vehicles tend to a desirable lattice formation. These theoretical properties are discussed in the paper and the performance of the algorithm is illustrated in simulations with realistic models of twenty all-terrain vehicles. Planned experimental evaluation using customized miniature cars is also briefly described.
Abstracting vehicle shape and kinematics constraints from obstacle avoidance methods
- AUTONOMOUS ROBOTS
, 2006
"... Most obstacle avoidance techniques do not take into account vehicle shape and kinematic constraints. They assume a punctual and omnidirectional vehicle and thus they are doomed to rely on approximations when used on real vehicles. Our main contribution is a framework to consider shape and kinemati ..."
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Cited by 15 (6 self)
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Most obstacle avoidance techniques do not take into account vehicle shape and kinematic constraints. They assume a punctual and omnidirectional vehicle and thus they are doomed to rely on approximations when used on real vehicles. Our main contribution is a framework to consider shape and kinematics together in an exact manner in the obstacle avoidance process, by abstracting these constraints from the avoidance method usage. Our approach can be applied to many non-holonomic vehicles with arbitrary shape. For these vehicles, the configuration space is three-dimensional, while the control space is two-dimensional. The main idea is to construct (centred on the robot at any time) the two-dimensional manifold of the configuration space that is defined by elementary circular paths. This manifold contains all the configurations that can be attained at each step of the obstacle avoidance and is thus general for all methods. Another important contribution of the paper is the exact calculus of the obstacle representation in this manifold for any robot shape (i.e. the configuration regions in collision). Finally, we propose a change of coordinates of this manifold so that the elementary paths become straight lines. Therefore, the three-dimensional obstacle avoidance problem with kinematic constraints is transformed into the simple obstacle avoidance problem for a point moving in a two-dimensional space without any kinematic restriction (the usual approximation in obstacle avoidance). Thus, existing avoidance techniques become applicable. The relevance of this proposal is to improve the domain of applicability of a wide range of obstacle avoidance methods. We
A tractable convergent dynamic window approach to obstacle avoidance
- IN IEEE/RSJ INT. CONF. ON INTELLIGENT ROBOTS AND SYSTEMS
, 2002
"... The dynamic window approach is a well known navigation scheme developed by Fox et. al. [1] and extended by Brock and Khatib [2]. It is safe by construction and has been shown to perform very efficiently in experimental setups. However, one can construct examples where the proposed scheme fails to at ..."
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Cited by 15 (1 self)
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The dynamic window approach is a well known navigation scheme developed by Fox et. al. [1] and extended by Brock and Khatib [2]. It is safe by construction and has been shown to perform very efficiently in experimental setups. However, one can construct examples where the proposed scheme fails to attain the goal configuration. What has been lacking is a theoretical treatment of the algorithm’s convergence properties. A first step towards such a treatment was presented in [4]. Here we continue that work with a computationally tractable algorithm resulting from a careful discretization of the optimal control problem of the previous paper and a way to construct a continuous Navigation Function. Inspired by the similarities between the Dynamic Window Approach and the Control Lyapunov Function and Receding Horizon Control synthesis put forth by Primbs et. al. [3] we propose a version of the Dynamic Window Approach that is tractable and provably convergent.
Probabilistic Autonomous Robot Navigation in Dynamic Environments with Human Motion Prediction. IJSR
, 2010
"... Abstract This paper considers the problem of autonomous robot navigation in dynamic and congested environments. The predictive navigation paradigm is proposed where prob-abilistic planning is integrated with obstacle avoidance along with future motion prediction of humans and/or other obstacles. Pre ..."
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Cited by 8 (0 self)
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Abstract This paper considers the problem of autonomous robot navigation in dynamic and congested environments. The predictive navigation paradigm is proposed where prob-abilistic planning is integrated with obstacle avoidance along with future motion prediction of humans and/or other obstacles. Predictive navigation is performed in a global manner with the use of a hierarchical Partially Observable Markov Decision Process (POMDP) that can be solved on-line at each time step and provides the actual actions the robot performs. Obstacle avoidance is performed within the predictive navigation model with a novel approach by decid-ing paths to the goal position that are not obstructed by other moving objects movement with the use of future motion pre-diction and by enabling the robot to increase or decrease its speed of movement or by performing detours. The robot is able to decide which obstacle avoidance behavior is optimal in each case within the unified navigation model employed.
Reactive Robot Motion using Path Replanning and Deformation
- in IEEE/RAS Int. Conf. on Robotics and Automation
"... Abstract — We present a reactive method for online robot motion replanning in dynamically changing environments by combining path replanning and deformation. Path deformation is newly integrated in our replanning method featured by efficient roadmap reuse and parallel planning and execution. This en ..."
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Cited by 6 (3 self)
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Abstract — We present a reactive method for online robot motion replanning in dynamically changing environments by combining path replanning and deformation. Path deformation is newly integrated in our replanning method featured by efficient roadmap reuse and parallel planning and execution. This enhancement allows the planner to deal with more dy-namic environments including continuously moving obstacles, by smoothly deforming the path during execution. Simulation results are shown to validate the effectiveness of the proposed method. I.
Efficient motion planning strategies for large-scale sensor networks
- in Proc. 7th Int. Workshop Algorithmic Found. Robot. (WAFR 2006),NewYork,NY
"... Abstract: In this paper, we develop a suite of motion planning strategies suitable for largescale sensor networks. These solve the problem of reconfiguring the network to a new shape while minimizing either the total distance traveled by the nodes or the maximum distance traveled by any node. Three ..."
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Cited by 3 (2 self)
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Abstract: In this paper, we develop a suite of motion planning strategies suitable for largescale sensor networks. These solve the problem of reconfiguring the network to a new shape while minimizing either the total distance traveled by the nodes or the maximum distance traveled by any node. Three network paradigms are investigated: centralized, computationally distributed, and decentralized. For the centralized case, optimal solutions are obtained in O(m) time in practice using a logarithmic-barrier method. Key to this complexity is transforming the Karush-Kuhn-Tucker (KKT) matrix associated with the Newton step sub-problem into a mono-banded system solvable in O(m) time. These results are then extended to a distributed approach that allows the computation to be evenly partitioned across the m nodes in exchange for O(m) messages in the overlay network. Finally, we offer a decentralized, hierarchical approach whereby follower nodes are able to solve for their objective positions in O(1) time from observing the headings of a small number (2-4) of leader nodes. This is akin to biological systems (e.g. schools of fish, flocks of birds, etc.) capable of complex formation changes using only local sensor feedback. We expect these results will prove useful in extending the mission lives of large-scale mobile sensor networks. 1
Adaptive look-ahead for robotic navigation in unknown environments
- In IROS
, 2011
"... Abstract-Receding horizon control strategies have proven effective in many control and robotic applications. These methods simulate the state a certain time horizon into the future to choose the optimal trajectory. However, in many cases, such as in mobile robot navigation, the selection of an appr ..."
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Cited by 3 (0 self)
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Abstract-Receding horizon control strategies have proven effective in many control and robotic applications. These methods simulate the state a certain time horizon into the future to choose the optimal trajectory. However, in many cases, such as in mobile robot navigation, the selection of an appropriate time horizon is important as too long of a time horizon can amplify the detrimental effects caused by environmental uncertainties in the prediction of the future state while too of a short horizon will lead to reduced performance. In this paper we strike a balance between these two conflicting objectives by first introducing a receding horizon method for navigation founded on schema-based behaviors. We then suggest a method of adapting the time horizon by minimizing a cost function which balances the performance of the underlying control problem (which prefers longer horizons) with the performance of our state prediction (which prefers shorter time horizons). We illustrate the operation with an example which shows the usefulness of our navigation scheme with an adaptive time horizon.
Obstacle avoidance for a non-holonomic vehicle using occupancy grids
"... In this paper, we outline the strategy used for obstacle avoidance on our small, non-holonomic test vehicle, the Autonomous Tractor. This strategy relies on the fusion of data from a stereo camera, a scanning laser range-finder and the vehicle odometry to create occupancy grids which describe the tr ..."
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Cited by 2 (0 self)
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In this paper, we outline the strategy used for obstacle avoidance on our small, non-holonomic test vehicle, the Autonomous Tractor. This strategy relies on the fusion of data from a stereo camera, a scanning laser range-finder and the vehicle odometry to create occupancy grids which describe the traversability of the terrain in the current vicinity of the vehicle. Knowledge of the vehicle’s kinematics and its response to control inputs is then used to derive obstacle free paths, if they exist, where preference is given to those commands which are ‘closest ’ to those issued by the overarching navigation system. 1
Prioritized sensor detection via dynamic voronoi-based navigation
- in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems
, 2009
"... Abstract-This paper presents a decentralized coordination algorithm that allows a team of sensor-enabled robots to navigate a region containing non-convex obstacles and take measurements within the region that contain the highest probability of having "good" information first. This approa ..."
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Cited by 2 (1 self)
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Abstract-This paper presents a decentralized coordination algorithm that allows a team of sensor-enabled robots to navigate a region containing non-convex obstacles and take measurements within the region that contain the highest probability of having "good" information first. This approach is motivated by scenarios where prior knowledge of the search space is known or when time constraints are present that limit the amount of area that can be searched by a robot team. Practical applications include search and rescue, target detection, and hazardous contaminations. Our cooperative control algorithm combines Voronoi partitioning, a global optimization technique, and a modified navigation function to prioritize sensor detection. The issues we address such as non-convex obstacles as well as global search are not extensively addressed in the current literature. Simulation results of the control algorithm are given and validate the prioritized sensing behavior as well as the collision avoidance property.