The use of awareness in collision prediction

Foisy, A.; Hayward, Vincent; Aubry, Stéphane

doi:10.1109/robot.1990.125998

Cited by 18 publications

(8 citation statements)

References 11 publications

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“…These algorithms perform quite badly if the balls are very far apart or if there are few collisions. Several other collision-detection algorithms are known that are efficient, at least in practice, for many types of objects; see, for example, [10], [26], [36], and [43]. Almost none of these have theoretical guarantees on their performance.…”

Section: Playing Poolmentioning

confidence: 99%

Raising Roofs, Crashing Cycles, and Playing Pool: Applications of a Data Structure for Finding Pairwise Interactions

Eppstein¹,

Erickson²

1999

Discrete Comput Geom

147

165

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The straight skeleton of a polygon is a variant of the medial axis introduced by Aichholzer et al., defined by a shrinking process in which each edge of the polygon moves inward at a fixed rate. We construct the straight skeleton of an n-gon with r reflex vertices in time O(n 1+ε + n 8/11+ε r 9/11+ε ), for any fixed ε > 0, improving the previous best upper bound of O(nr log n). Our algorithm simulates the sequence of collisions between edges and vertices during the shrinking process, using a technique of Eppstein for maintaining extrema of binary functions to reduce the problem of finding successive interactions to two dynamic range query problems: (1) maintain a changing set of triangles in R 3 and answer queries asking which triangle is first hit by a query ray, and (2) maintain a changing set of rays in R 3 and answer queries asking for the lowest intersection of any ray with a query triangle. We also exploit a novel characterization of the straight skeleton as a lower envelope of triangles in R 3 . The same time bounds apply to constructing non-self-intersecting offset curves with mitered or beveled corners, and similar methods extend to other problems of simulating collisions and other pairwise interactions among sets of moving objects.

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Section: Playing Poolmentioning

confidence: 99%

Raising Roofs, Crashing Cycles, and Playing Pool: Applications of a Data Structure for Finding Pairwise Interactions

Eppstein¹,

Erickson²

1999

Discrete Comput Geom

147

165

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“…While the map of the static structure is directly used as computed by the modeling module, the map of dynamic obstacles is processed to compute an alternative map based on the predicted collision point [17]. This new map of obstacles is the other input of the obstacle avoidance.…”

Section: Integration Of the Modeling Within The Motion Layermentioning

confidence: 99%

Modeling dynamic scenarios for local sensor-based motion planning

2008

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This paper addresses the modeling of the static and dynamic parts of the scenario and how to use this information with a sensor-based motion planning system. The contribution in the modeling aspect is a formulation of the detection and tracking of mobile objects and the mapping of the static structure in such a way that the nature (static/dynamic) of the observations is included in the estimation process. The algorithm provides a set of filters tracking the moving objects and a local map of the static structure constructed on line. In addition, this paper discusses how this modeling module is integrated in a real sensor-based motion planning system taking advantage selectively of the dynamic and static information. The experimental results confirm that the complete navigation system is able to move a vehicle in unknown and dynamic scenarios. Furthermore, the system overcomes many of the limitations of previous systems associated to the ability to distinguish the nature of the parts of the scenario.

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“…Candidates for collision checking are prioritized in order to test only those pairs which are more likely to collide. The first criterion one may consider is distance, but it is not enough if the relative velocities are not taken into account, as pointed out in [20]. These authors introduce the concept of awareness or imminence of a collision.…”

Section: The Optimization Approachmentioning

confidence: 99%