Real-Time Energy-Optimal Path Planning for Electric Vehicles

The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only be reachable through an energy-optimal path: a route that consumes less energy than all other alternatives. The feasibility of such energy-efficient paths depends heavily on the accuracy of the energy model used for planning, and thus failing to account for vehicle dynamics can lead to inaccurate energy estimates, rendering some planned routes infeasible in reality. This paper explores the impact of vehicle dynamics on energy-optimal path planning for EVs. We develop an accurate energy model that incorporates key vehicle dynamics parameters into energy calculations, thereby reducing the risk of planning infeasible paths under battery constraints. The paper also introduces two novel online reweighting functions that allow for a faster, pre-processing free, pathfinding in the presence of negative energy costs resulting from regenerative braking, making them ideal for real-time applications. Through extensive experimentation on real-world transport networks, we demonstrate that our approach considerably enhances energy-optimal pathfinding for EVs in both computational efficiency and energy estimation accuracy.

Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics that asks us to compute collision-free paths for a team of agents, all moving across a shared map. Although many works appear on this topic, all current algorithms struggle as the number of agents grows. The principal reason is that existing approaches typically plan free-flow optimal paths, which creates congestion. To tackle this issue we propose a new approach for MAPF where agents are guided to their destination by following congestion-avoiding paths. We evaluate the idea in two large-scale settings: one-shot MAPF, where each agent has a single destination, and lifelong MAPF, where agents are continuously assigned new tasks. For one-shot MAPF we show that our approach substantially improves solution quality. For Lifelong MAPF we report large improvements in overall throughput.

Reducing Redundant Work in Jump Point Search

JPS (Jump Point Search) is a state-of-the-art optimal algorithm for online grid-based pathfinding. Widely used in games and other navigation scenarios, JPS nevertheless can exhibit pathological behaviours which are not well studied: (i) it may repeatedly scan the same area of the map to find successors; (ii) it may generate and expand suboptimal search nodes. In this work, we examine the source of these pathological behaviours, show how they can occur in practice, and propose a purely online approach, called Constrained JPS (CJPS), to tackle them efficiently. Experimental results show that CJPS has low overheads and is often faster than JPS in dynamically changing grid environments: by up to 7x in large game maps and up to 14x in pathological scenarios.

Scalable Rail Planning and Replanning with Soft Deadlines

The Flatland Challenge, which was first held in 2019 and reported in NeurIPS 2020, is designed to answer the question: How to efficiently manage dense traffic on complex rail networks? Considering the significance of punctuality in real-world railway network operation and the fact that fast passenger trains share the network with slow freight trains, Flatland version 3 introduces trains with different speeds and scheduling time windows. This paper introduces the Flatland 3 problem definitions and extends an award-winning MAPF-based software, which won the NeurIPS 2020 competition, to efficiently solve Flatland 3 problems. The resulting system won the Flatland 3 competition. We designed a new priority ordering for initial planning, a new neighbourhood selection strategy for efficient solution quality improvement with Multi-Agent Path Finding via Large Neighborhood Search(MAPF-LNS), and use MAPF-LNS for partially replanning the trains influenced by malfunction.

Enhanced Methods for the Weight Constrained Shortest Path Problem: Constrained Path Finding Meets Bi-objective Search

The classic problem of \textit{constrained path finding} is a well-studied but yet challenging topic in AI with a broad range of applications in various areas such communication and transportation. The Weight Constrained Shortest Path Problem (WCSPP), as the base form of constrained path finding with only one side constraint, aims to plan a cost optimum path whose weight/resource usage is limited. Given the bi-criteria nature of the problem (i.e., dealing with cost and weight of paths), methods addressing the WCSPP have some common properties with bi-objective search. This paper leverages the recent state-of-the-art A*-based techniques in both constrained path finding and bi-objective search and presents two exact solution approaches to the WCSPP, both capable of solving hard problem instances on very large graphs. We empirically evaluate the performance of our algorithms on a new set of large and realistic problem instances and show their advantages over the state-of-the-art algorithms in both time and space metrics. This paper also investigates the importance of priority queues in constrained search with A*. We show with extensive experiments on both realistic and randomised graphs how bucket-based queues without tie-breaking can effectively improve the algorithmic performance of exhaustive bi-criteria searches.

Multi-Target Search in Euclidean Space with Ray Shooting

The Euclidean shortest path problem (ESPP) is a well studied problem with many practical applications. Recently a new efficient online approach to this problem, RayScan, has been developed, based on ray shooting and polygon scanning. In this paper we show how we can improve RayScan by carefully reasoning about polygon scans. We also look into how RayScan could be applied in the single-source multi-target scenario, where logic during scanning is used to reduce the number of rays shots required. This improvement also helps in the single target case. We compare the improved RayScan+ against the state-of-the-art ESPP algorithm, illustrating the situations where it is better.

Bi-objective Search with Bi-directional A*

Bi-objective search is a well-known algorithmic problem, concerned with finding a set of optimal solutions in a two-dimensional domain. This problem has a wide variety of applications such as planning in transport systems or optimal control in energy systems. Recently, bi-objective A*-based search (BOA*) has shown state-of-the-art performance in large networks. This paper develops a bi-directional variant of BOA*, enriched with several speed-up heuristics. Our experimental results on 1,000 benchmark cases show that our bi-directional A* algorithm for bi-objective search (BOBA*) can optimally solve all of the benchmark cases within the time limit, outperforming the state of the art BOA*, bi-objective Dijkstra and bi-directional bi-objective Dijkstra by an average runtime improvement of a factor of five over all of the benchmark instances.

Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search

Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents. In this work, we show that one of the reasons why MAPF is so hard to solve is due to a phenomenon called pairwise symmetry, which occurs when two agents have many different paths to their target locations, all of which appear promising, but every combination of them results in a collision. We identify several classes of pairwise symmetries and show that each one arises commonly in practice and can produce an exponential explosion in the space of possible collision resolutions, leading to unacceptable runtimes for current state-of-the-art (bounded-sub)optimal MAPF algorithms. We propose a variety of reasoning techniques that detect the symmetries efficiently as they arise and resolve them by using specialized constraints to eliminate all permutations of pairwise colliding paths in a single branching step. We implement these ideas in the context of the leading optimal MAPF algorithm CBS and show that the addition of the symmetry reasoning techniques can have a dramatic positive effect on its performance - we report a reduction in the number of node expansions by up to four orders of magnitude and an increase in scalability by up to thirty times. These gains allow us to solve to optimality a variety of challenging MAPF instances previously considered out of reach for CBS.

Symmetry Breaking for k-Robust Multi-Agent Path Finding

During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by unexpected events. To address such situations recent work describes k-Robust Conflict-BasedSearch (k-CBS): an algorithm that produces coordinated and collision-free plan that is robust for up to k delays. In this work we introducing a variety of pairwise symmetry breaking constraints, specific to k-robust planning, that can efficiently find compatible and optimal paths for pairs of conflicting agents. We give a thorough description of the new constraints and report large improvements to success rate ina range of domains including: (i) classic MAPF benchmarks;(ii) automated warehouse domains and; (iii) on maps from the 2019 Flatland Challenge, a recently introduced railway domain where k-robust planning can be fruitfully applied to schedule trains.

Organising a Successful AI Online Conference: Lessons from SoCS 2020

The 13th Symposium on Combinatorial Search (SoCS) was held May 26-28, 2020. Originally scheduled to take place in Vienna, Austria, the symposium pivoted toward a fully online technical program in early March. As an in-person event SoCS offers participants a diverse array of scholarly activities including technical talks (long and short), poster sessions, plenary sessions, a community meeting and, new for 2020, a Master Class tutorial program. This paper describes challenges, approaches and opportunities associated with adapting these many different activities to the online setting. We consider issues such as scheduling, dissemination, attendee interaction and community engagement before, during and after the event. We report on the approaches taken by SoCS in each case, we give a post-hoc analysis of their their effectiveness and we discuss how these decisions continue to impact the SoCS community in the days after SoCS 2020.