Benchmarking the utility of maps of dynamics for human-aware motion planning

Abstract

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.

The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots

“…The top-level safety layer is the flow-aware motion planning using maps of dynamics (MoDs) that represent human motion patterns [11,21,25,26]. This layer creates (global) robot motions that aim to reduce the amount of human-robot interference by using learned long-term patterns of human motion.…”

“…Global Path Planning: We have presented three flowaware cost functions which can be used in the RRT * motion planning algorithm: two are based on CLiFF-maps (i.e., the CLiFF Extended Upstream Criterion [21] and Down-The-CLiFF cost [25]) and one is based on STeF-maps (the STeF Extended Upstream Criterion [26]). In CLiFF-RRT * , we generate a robot trajectory by adopting a biasing approach in RRT * [10]: we first generate a discrete path that selects mixtures at relevant locations from the map of dynamics, and then use those mixtures to bias the sampling and rewiring procedures in RRT * .…”

“…In DTC-RRT * we further extend the CLiFF-RRT * by also considering the mean speed encoded in the distribution and the information about uncertainty. Both strategies have been shown to improve the overall efficiency of robot operation while being flow-aware in such human-aware scenarios [26].…”

Survey of maps of dynamics for mobile robots

“…Recently three comparison papers have been published in this field. In their work, Swaminathan et al (2022) and Vintr et al (2020), analyse the impact of MoDs on motion planning. The core idea behind this work is to measure if the information stored in MoDs, when used during global motion planning will positively impact the robot’s performance.…”

“…Another approach to the problem of MoDs impact on motion planning presented (Swaminathan et al, 2022). In this work, the authors focused on the time the robot wastes while yielding to people.…”