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Incremental graph search algorithms, such as D* Lite, reuse previous search efforts to speed up subsequent similar path planning tasks. These algorithms have demonstrated their efficiency in comparison with search from scratch, and have been leveraged in many applications such as navigation in unknown terrain. On the other hand, path planning typically involves optimizing multiple conflicting objectives simultaneously, such as travel risk, arrival time, etc. Multi-objective path planning is challenging as the number of Pareto-optimal solutions can grow exponentially with respect to the size of the graph, which makes it computationally burdensome to plan from scratch each time when similar planning tasks needs to be solved. This article presents a new multi-objective incremental search algorithm called Multi-Objective Path-Based D* Lite (MOPBD*) which reuses previous search efforts to speed up subsequent planning tasks while optimizing multiple objectives. Numerical results show that MOPBD* is more efficient than search from scratch and runs an order of magnitude faster than existing incremental method for multi-objective path planning.
This paper addresses a generalization of the well known multi-agent path finding (MAPF) problem that optimizes multiple conflicting objectives simultaneously such as travel time and path risk. This generalization, referred to as multi-objective MAPF
In this paper, we develop a new algorithm, called T$^{star}$-Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The T$^{star}$-Lite algorithm is a significantly faster version of the previously developed
Simulation to real (Sim-to-Real) is an attractive approach to construct controllers for robotic tasks that are easier to simulate than to analytically solve. Working Sim-to-Real solutions have been demonstrated for tasks with a clear single objective
Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF). However, existing communication based MAPF solvers focus on broadcast comm
Rapidly-exploring Random Tree Star(RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, o