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Mission planning on preference-based expression trees using heuristics-assisted evolutionary computation
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DOI：10.1016/j.asoc.2023.110090.png)
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摘要
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The mission planning problem has so far been solved by using temporal logic and classic planning approaches that have an exponential computational complexity and do not always account for the transition costs. Variants of the Vehicle Routing Problem can use meta-heuristics to give a near -optimal solution; however, have a limited representation capability. In this paper the problem of giving complex instructions (mission) to a robot in the form of an expression tree with AND, OR and THEN operators is studied. This allows users to instruct the robots for everyday tasks like getting a variety of items (modeled by AND) with a few choices (modeled by OR), the picks having temporal constraints (modeled by THEN). Preferences can be added to make the robot solve a sub-mission earlier than another sub-mission. The paper first proposes an algorithm to make a greedy solution by a single parse of the expression tree. Thereafter, the solution is optimized by using a Genetic Algorithm that optimizes the branch to be taken by the OR operator and the ordering of operations. Experimental results are demonstrated on the Pioneer LX robot programmed using the Robot Operating System. The experimental results further show that the proposed approach significantly beats a Genetic Algorithm solution and a Dynamic Programming greedy solution for problem sizes that cannot be solved by using the model verification and exhaustive search techniques. (c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Mission planning
Robot motion planning
Linear Temporal Logic
Planning-based agents
Agent-based systems
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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