Bi-objective trail-planning for a robot team orienteering in a hazardous environment

TL;DR

To search for the Pareto-optimal set of robot-team trail plans, bi-objective ant colony optimization is implemented, guided by both pheromone and problem-specific heuristics, finding that ant colony optimization outperforms or performs indistinguishably from a simulated annealing baseline.

Abstract

Teams of mobile [aerial, ground, or aquatic] robots have applications in resource delivery, patrolling, information-gathering, agriculture, forest fire fighting, chemical plume source localization and mapping, and search-and-rescue. Robot teams traversing hazardous environments -- with e.g. rough terrain or seas, strong winds, or adversaries capable of attacking or capturing robots -- should plan and coordinate their trails in consideration of risks of disablement, destruction, or capture. Specifically, the robots should take the safest trails, coordinate their trails to cooperatively achieve the team-level objective with robustness to robot failures, and balance the reward from visiting locations against risks of robot losses. Herein, we consider bi-objective trail-planning for a mobile team of robots orienteering in a hazardous environment. The hazardous environment is abstracted as a directed graph whose arcs, when traversed by a robot, present known probabilities of survival. Each node of the graph offers a reward to the team if visited by a robot (which e.g. delivers a good to or images the node). We wish to search for the Pareto-optimal robot-team trail plans that maximize two [conflicting] team objectives: the expected (i) team reward and (ii) number of robots that survive the mission. A human decision-maker can then select trail plans that balance, according to their values, reward and robot survival. We implement ant colony optimization, guided by heuristics, to search for the Pareto-optimal set of robot team trail plans. As a case study, we illustrate with an information-gathering mission in an art museum.

Figures (4)

• Figure 1: The bi-objective team orienteering in hazardous environments (BO-TOHE) problem. (a) A team of robots are mobile on a directed graph whose nodes offer a reward to the team if visited by a robot and arcs present a probability of destruction to robots that traverse them (tornado = 1/10 probability of destruction). Our task is to plan the trails of the robots to maximize the expected reward collected and the expected number of robots that survive. (b) Pareto-optimal and -dominated robot-team trail plans scattered in objective space, with two Pareto-optimal plans and one Pareto-dominated plan shown.
• Figure 2: Illustrating notation for a particular robot trail plan.
• Figure 3: Our TOHE problem instance. The directed graph represents a two-floor art museum with distinct rooms (nodes) connected by doorways or a stairway (arcs). The arcs are colored according to robot survival probabilities. The nodes are colored according to rewards offered to the team when a robot visits. The three robots (planes) initially reside at the base node.
• Figure 4: BO-ACO of our TOHE problem instance. (a) The quality of the Pareto-set of robot team trail plans, measured via the area indicator (yellow-highlighted area in (c)), as a function of the number of iterations of ACO, broken into ordinary ACO, ACO when the heuristics are not used, and ACO when the pheromone trails are not used. (b) The pheromone trails and distribution of the amount of pheromone on the arcs at the end of the ACO algorithm. (c) The [approximate] Pareto-front of robot-team team trail plans with four select plans shown.