To Örebro University

Motion planning for mobile robots in dynamic environments shared with people is challenging. In a typical hierarchical planning framework, robots use a global planner for overall path-finding and a local planner for immediate adjustments to the path. Without considering the typical patterns of motion of dynamic entities, the global planner might generate paths that lead the robot into highly congested areas, where it is forced to wait, replan or manoeuvre around dynamic obstacles. Maps of Dynamics (MoDs) are a way to mitigate these issues. MoDs represent patterns of motion exhibited by moving entities in the environment, using probabilistic models. The use of MoDs in cost functions for motion planning enables a robot to plan motions that consider the motion patterns encoded in the MoDs. In previous work, it has been shown that the use of MoDs in the cost function helps generate more efficient paths, i.e., paths that lead to the robot and pedestrians spending less time waiting for each other. It has also been shown that using MoDs in the sampling step of sampling-based motion planning is beneficial to a mobile robot since it can result in reduced computation time by explicitly guiding the sampling process using information encoded in MoDs. However, existing work on the use of MoDs in the sampling process is limited. Correspondingly, an analysis of the performance of sampling heuristics for MoDs is also largely lacking. Since such an analysis is crucial to understand the effectiveness of MoDs in a practical setting, we ask the research question: can we obtain reasonably low-cost solutions using sampling-based motion planners that consider the flow of dynamic entities, in a reasonable amount of time. In this paper, we propose substantial improvements to two existing sampling heuristics: the Dijkstra-graph sampling (DGS), previously restricted to a specific type of MoD is extended to use any MoD; and the intensity-map (normalized number of observations of dynamic entities in each grid cell) is utilized more effectively by using importance sampling instead of rejection sampling. We show that an ellipsoidal heuristic can also be used with MoDs. We experimentally validate several sampling heuristics on two different sampling-based motion planners and present a comprehensive evaluation (52800 runs) of their performance on real-world data from densely populated environments. We conclude that reasonably cost solutions can be quickly obtained using a combination of the sampling heuristics within practically feasible time limits. Using the RRT* planner with our proposed MoD-aware, Dijkstra-graph-based heuristic yields approximate to 5%, %10 and 12% higher success rates after 2, 4 and 8 s of planning respectively, compared to uniform sampling, the baseline.

Current intralogistics services require keeping up with e-commerce demands, reducing delivery times and waste, and increasing overall flexibility. As a consequence, the use of automated guided vehicles (AGVs) and, more recently, autonomous mobile robots (AMRs) for logistics operations is steadily increasing.

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

In this extended abstract we present a novel dataset for benchmarking motion prediction algorithms. We describe our approach to data collection which generates diverse and accurate human motion in a controlled weakly-scripted setup. We also give insights for building a universal benchmark for motion prediction.

Dynamics is an inherent feature of reality. In spite of that, the domain of maps of dynamics has not received a lot of attention yet. In this book, we present solutions for building maps of dynamics and outline how to make use of them for motion planning. In this chapter, we present discuss related research question that as of yet remain to be answered, and derive possible future research directions. 

Change and motion are inherent features of reality. The ability to recognise patterns governing changes has allowed humans to thrive in a dynamic reality. Similarly, dynamics awareness can also improve the performance of robots. Dynamics awareness is an umbrella term covering a broad spectrum of concepts. In this chapter, we present the key aspects of dynamics awareness. We introduce two motivating examples presenting the challenges for robots operating in a dynamic environment. We discuss the benefits of using spatial models of dynamics and analyse the challenges of building such models.

The task of building maps of dynamics is the key focus of this book, as well as how to use them for motion planning. In this chapter, we present a categorisation and overview of different types of maps of dynamics. Furthermore, we give an overview of approaches to motion planning in dynamic environments, with a focus on motion planning over maps of dynamics. 

The shared feature of the flow of discrete objects and continuous media is that they both can be represented as velocity vectors encapsulating direction and speed of motion. In this chapter, we present a method for modelling the flow of discrete objects and continuous media as continuous Gaussian mixture fields. The proposed model associates to each part of the environment a Gaussian mixture model describing the local motion patterns. We also present a learning method, designed to build the model from a set of sparse, noisy and incomplete observations. 

The key idea of modelling flow of discrete objects is to capture the way they move through the environment. One method to capture the flow is to observe changes in occupancy caused by the motion of discrete objects. In this chapter, we present a method to model and learn occupancy shifts caused by an object moving through the environment. The key idea is observe temporal changes changes in the occupancy of adjacent cells, and based on the temporal offset infer the direction of the occupancy flow.