MAP: Medial Axis Based Geometric Routing in Sensor Networks

One of the challenging tasks in the deployment of dense wireless networks (like sensor networks) is in devising a routing scheme for node to node communication. Important consideration includes scalability, routing complexity, the length of the communication paths and the load sharing of the routes. In this paper, we show that a compact and expressive abstraction of network connectivity by the medial axis enables efficient and localized routing. We propose MAP, a Medial Axis based naming and routing Protocol that does not require locations, makes routing decisions locally, and achieves good load balancing. In its preprocessing phase, MAP constructs the medial axis of the sensor field, defined as the set of nodes with at least two closest boundary nodes. The medial axis of the network captures both the complex geometry and non-trivial topology of the sensor field. It can be represented compactly by a graph whose size is comparable with the complexity of the geometric features (e.g., the number of holes). Each node is then given a name related to its position with respect to the medial axis. The routing scheme is derived through local decisions based on the names of the source and destination nodes and guarantees delivery with reasonable and natural routes. We show by both theoretical analysis and simulations that our medial axis based geometric routing scheme is scalable, produces short routes, achieves excellent load balancing, and is very robust to variations in the network model

Using a Cognitive Architecture for Opponent Target Prediction

One of the most important aspects of a compelling game AI is that it anticipates the player’s actions and responds to them in a convincing manner. The first step towards doing this is to understand what the player is doing and predict their possible future actions. In this paper we show an approach where the AI system focusses on testing hypotheses made about the player’s actions using an implementation of a cognitive architecture inspired by the simulation theory of mind. The application used in this paper is to predict the target that the player is heading towards, in an RTS-style game. We improve the prediction accuracy and reduce the number of hypotheses needed by using path planning and path clustering

Bayesian Optimisation for Safe Navigation under Localisation Uncertainty

In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for navigation based on its own percepts about the environment while avoiding damage to itself. Bayesian optimisation (BO) has been successfully applied to the task of learning a model of terrain traversability while guiding the robot through more traversable areas. An issue, however, is that localisation uncertainty can end up guiding the robot to unsafe areas and distort the model being learnt. In this paper, we address this problem and present a novel method that allows BO to consider localisation uncertainty by applying a Gaussian process model for uncertain inputs as a prior. We evaluate the proposed method in simulation and in experiments with a real robot navigating over rough terrain and compare it against standard BO methods.Comment: To appear in the proceedings of the 18th International Symposium on Robotics Research (ISRR 2017

Flow Computations on Imprecise Terrains

We study the computation of the flow of water on imprecise terrains. We consider two approaches to modeling flow on a terrain: one where water flows across the surface of a polyhedral terrain in the direction of steepest descent, and one where water only flows along the edges of a predefined graph, for example a grid or a triangulation. In both cases each vertex has an imprecise elevation, given by an interval of possible values, while its (x,y)-coordinates are fixed. For the first model, we show that the problem of deciding whether one vertex may be contained in the watershed of another is NP-hard. In contrast, for the second model we give a simple O(n log n) time algorithm to compute the minimal and the maximal watershed of a vertex, where n is the number of edges of the graph. On a grid model, we can compute the same in O(n) time

Automatic fine motor control behaviours for autonomous mobile agents operating on uneven terrains

A novel mechanism able to produce increasingly stable paths for mobile robotic agents travelling over uneven terrain is proposed in this paper. In doing so, cognitive agents can focus on higher-level goal planning, with the increased confidence the resulting tasks will be automatically accomplished via safe and reliable paths within the lower-level skills of the platform. The strategy proposes the extension of the Fast Marching level-set method of propagating interfaces in 3D lattices with a metric to reduce robot body instability. This is particularly relevant for kinematically reconfigurable platforms which significantly modify their mass distribution through posture adaptation, such as humanoids or mobile robots equipped with manipulator arms or varying traction arrangements. Simulation results of an existing reconfigurable mobile rescue robot operating on real scenarios illustrate the validity of the proposed strategy. Copyright 2010 ACM

Altitude-Loss Optimal Glides in Engine Failure Emergencies -- Accounting for Ground Obstacles and Wind

Engine failure is a recurring emergency in General Aviation and fixed-wing UAVs, often requiring the pilot or remote operator to carry out carefully planned glides to safely reach a candidate landing strip. We tackle the problem of minimizing the altitude loss of a thrustless aircraft flying towards a designated target position. Extending previous work on optimal glides without obstacles, we consider here trajectory planning of optimal gliding in the the presence of ground obstacles, while accounting for wind effects. Under simplifying model assumptions, in particular neglecting the effect of turns, we characterize the optimal solution as comprising straight glide segments between iteratively-determined extreme points on the obstacles. Consequently, the optimal trajectory is included in an iteratively-defined reduced visibility graph, and can be obtained by a standard graph search algorithm, such as A$^*$. We further quantify the effect of turns to verify a safe near-optimal glide trajectory. We apply our algorithm on a Cessna 172 model, in realistic scenarios, demonstrating both the altitude-loss optimal trajectory calculation, and determination of airstrip reachability

A kyno-dynamic metric to plan stable paths over uneven terrain

A generic methodology to plan increasingly stable paths for mobile platforms travelling over uneven terrain is proposed in this paper. This is accomplished by extending the Fast Marching level-set method of propagating interfaces in 3D lattices with an analytical kyno-dynamic metric which embodies robot stability in the given terrain. This is particularly relevant for reconfigurable platforms which significantly modify their mass distribution through posture adaptation, such as robots equipped with manipulator arms or varying traction arrangements. Results obtained from applying the proposed strategy in a mobile rescue robot operating on simulated and real terrain data illustrate the validity of the proposed strategy. ©2010 IEEE