.png){kind=icon}
.png){kind=icon}
Return
Interval Analysis Technique for Versatile and Parallel Multi-Agent Collision Detection and Avoidance
.png){kind=icon}
PRE
AI
DOI：10.1007/s10846-019-01091-1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Collision detection and avoidance are vital sub-tasks in any autonomous robotic task. This work presents a technique that guarantees collision avoidance in an environment containing multiple agents. These agents can be of different types such as static, dynamic obstacles and other robots. The technique supports parallel implementation and is appropriate for real-time applications. In this work, the theory of interval arithmetic is used to represent the pose of agents as intervals in a fixed time period. Geometrically, the intervals correspond to finite-length arcs and line-segments. Theoretical results on the inclusion of one interval, in another interval, in terms of sub-intervals are derived. Breaking down the problem in sub-intervals supports parallelism in performing multiple interval inclusion tests and in handling multiple agents. The proposed interval-arithmetic based framework leads directly to a hardware-efficient collision detection scheme. In particular, the proposed strategy admits a solution even for a dynamic environment using just shift and add capability, an important aspect for embedded implementation. The solution of interval inclusion is also used to find a set of solutions for guaranteed collision avoidance with multiple agents with known or unknown trajectories. Simulation results in MATLAB and experiments with an FPGA-driven differential drive mobile robot demonstrate the versatility of the proposed approach.
Keywords:
Collision detection
Collision avoidance
Dynamic obstacle
Interval analysis
Multi-agent
Embedded vehicle
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
2.8
Papers:
3.8K
Citations:
6.9K
View more organizations