The Resource Constrained Shortest Path Problem (RCSPP) seeks to determine a minimum-cost path between a start and a goal location while ensuring that one or multiple types of resource consumed along the path do not exceed their limits. This problem is often solved on a graph where a path is incrementally built from the start towards the goal during the search. RCSPP is computationally challenging as comparing these partial solution paths is based on multiple criteria (i.e., the accumulated cost and resource along the path), and in general, there does not exist a single path that optimizes all criteria simultaneously. Consequently, the search needs to maintain and explore a large number of partial paths in order to find an optimal solution. While a variety of algorithms have been developed to solve RCSPP, they either have little consideration about efficiently comparing and maintaining the partial paths, which reduces their overall runtime efficiency, or are restricted to handle only one resource constraint as opposed to multiple resource constraints. This paper develops Enhanced Resource Constrained A* (ERCA*), a fast A*-based algorithm that can find an optimal solution while satisfying multiple resource constraints. ERCA* leverages both the recent advances in multi-objective path planning to efficiently compare and maintain partial paths, and techniques from the existing RCSPP literature. Furthermore, ERCA* has a functional parameter to broker a trade-off between solution quality and runtime efficiency. The results show ERCA* often runs several orders of magnitude faster than an existing leading algorithm for RCSPP.

Assembly of large structures requires large fixtures, often referred to as monuments. Their cost and massive size limit flexibility and scalability of the manufacturing process. Numerous small mobile robots can replace these large structures and, therefore, replicate the efficiency of the assembly line with far more flexibility. An assembly line made up of mobile manipulators can easily and rapidly be reconfigured to support scalability and a varied product mix, while allowing for near optimal resource assignment. The challenge to using small robots in place of monuments is making their joint behavior precise enough to accomplish the task and efficient enough to execute subtasks in a reasonable period of time. In this paper, we describe a set of techniques that we combine to achieve the necessary precision and overall efficiency to build a large structure. We describe and demonstrate these techniques in the context of a testbed we implemented for assembling a wing ladder.

In this paper, we consider the problem of feasibly integrating new pick-up and delivery requests into existing vehicle itineraries in a dynamic, dial-a-ride problem (DARP) setting. Generalizing from previous work in oversubscribed task scheduling, we define a controlled iterative repair search procedure for finding an alternative set of vehicle itineraries in which the overall solution has been feasibly extended to include newly received requests. We first evaluate the performance of this technique on a set of DARP feasibility benchmark problems from the literature. We then consider its use on a real-world DARP problem, where it is necessary to accommodate all requests and constraints must be relaxed when a request cannot be feasibly integrated. For this latter analysis, we introduce a constraint relaxation post processing step and consider the performance impact of using our controlled iterative search over the current greedy search approach.