One of the original and most highly cited AMRs specifically designed to operate within a human environment began with the museum tour guide RHINO (‘97) [36], and its second generation incarnation MINERVA (‘98) [37]. These were deployed in Deutsches Museum Bonn, Germany, and Smithsonian’s National Museum of Amer- ican History, USA, respectively. Both proved to be very successful with RHINO claiming a success rate of ‘99.75%’, based on the ratio between number of re- quests (2,400) and number of collisions (6), and MINERVA claiming to be superior still. This was due to the added facial features of MINERVA [213], as the anthropo- morphism results in people preferring to interact co-operatively. The navigational success of these two projects was only to move to their required locations without colliding with pedestrians. Treating people as simple objects to locally avoid, rather than incorporating social dynamics into a global path planner, would allow effective collision avoidance, but does not address the mimicry of pedestrians.
Both RHINO and MINERVA used the DWA, Section 2.2.2.1, to navigate their environments , which is a method still implemented in current H-Mobile-RI systems such as seen in the STRANDS project [214]. It is long-term autonomy projects such as STRANDS that focus on simple collision avoidance approaches towards
pedestrians, involving the local DWA planner and a global costmap, that focus on the long-term dynamics of mapped space rather than behavioural navigation. The project operates at the Akademie für Alterforschung at the Haus derBarmherzigkeit in Vienna, and the AMR uses topological maps, Section 2.2.1.2, in order to evaluate the probability that someone will interact with the robot at a given location and time, based on these long-term observations, which allows the robot to adapt to working routines. This long-term autonomy is vital concerning the probability of success, as only over large time periods can all faults be detected.
This thesis focuses on developing a navigational strategy and path planner that results in an AMR employing considerate behaviour towards pedestrians. This is an area of research that is being developed in robots with much more short-term autonomy levels. In 2013 Obelix [40] travelled 3.2km in 1.5h from the Faculty of Engineering, University of Freiburg, to the Bertoldsbrunnen in the inner city, Frieiburg, Germany. However, despite Obelix being developed 17 years after RHINO there has been little change in how it interacts with pedestrians, and it moves much slower than any pedestrian it passes. Obelix does not employ any form of pedestrian model when it navigates through the streets, and it never needs to evaluate how it should respond to individual pedestrians as they move around the robot. Obelix relies on SLAM in order to navigate and employs an unknown collision avoidance strategy to avoid crashing. In addition to this, no moving pedestrians ever obstructed its path4, and so even its limited collision avoidance could not be evaluated.
In a more localised indoor environment [42] demonstrates that a simple micro- scopic pedestrian model is vital to recreate similar social movements. For a robotic navigation system to operate like a pedestrian within a crowd, the pedestrians them- selves must be appropriately modelled. This allows their movements to be predicted so the manner in which the robot should behave and move can be correctly designed. The social force model [185], is recalibrated from human-human to human-robot sit- uations. This adaptation allows the robot to use a human-like collision avoidance system in a real-world pedestrian environment. The robot recreates a collision avoid- ance strategy that can be perceived as safe and natural by humans, with 96.4% in agreement. The density of the crowd was however very low, with the robot only interacting with a sparse number of pedestrians head-on, and moving a significant distance of 8 metres before a collision potential. In a more crowded environment such an early divergence from its trajectory may cause problems by moving into the path of other pedestrians, as pedestrians only begin to path correct at an average distance of 0.38 to 0.86m [195]. The negative effects of the original social force model may then develop, where unnatural oscillation occur between neighbouring agents.
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2.4 Human-Mobile Robot Interaction 57 2.4.1.1 Vehicles
Aside from the aforementioned AMRs, which are human sized, larger autonomous vehicles (AVs) tend not to deal with the intricacies of social navigation as they are larger, move faster, and do so with more limited movements. Also, AVs are often confined to roads, e.g. self-driving cars, whilst pedestrians remain on pave- ments. Due to this the main interaction AVs have with pedestrians is when a person crosses the road (e.g. [65, 66]). Any form of collision avoidance occurs as a "stop- and-wait" policy, which is the only available method when an travels along a road lane. However, some human-aware path planners still employ this method in both path planning and collision avoidance strategies, discussed in Section 2.4.2.3 and Section 2.4.2.1, respectively. If the stop-and-wait policy was to be employed in a crowd this would increase congestion and cause movement oscillations, as observed in microscopic pedestrian models, Section 2.3.2.2. Therefore, AVs must be able to successfully predict if a pedestrian is to cross in front of it, and implement a navi- gation strategy that will ensure the safety of: the pedestrian crossing in front of the AV; the passenger of the AV (if there is one); and if the speed of the AV must be reduced or stopped, the safety of any vehicle moving along behind the AV.
To ensure the safety of anyone crossing the raod, the intention of the pedestrian must be evaluated to decipher if they are to cross or not. For methods that assume the intentions of pedestrians do not change, and that they aim to move toward their goal with a constant trajectory [65, 66], the AV will simply come to a stop if a pedestrian is on the road, and continue to move once they are on the opposite pavement. However, predicting when a pedestrian will cross at specified crossings require a fast and reliable process, as they can "perform instantaneous changes in motion behaviour following changes in intent." [215]. A "changepoint" must be iden- tified whereby "observed data better fits a new behaviour model than the current model to which it is being compared", as "agile dynamic agents such as pedestri- ans may exhibit new behaviours or mid-trajectory changes in intent". Although a stop-and-wait strategy is best, being able to detect a pedestrian’s crossing intent should prevent collisions by reducing the AV’s velocity before the pedestrian begins to cross.
The "Intention-Aware Online partially observable Markov decision process Plan- ning" method is used to autonomously drive a golf buggy AV through a sparse crowd [216]. An A* search, Section 2.2.2.2, is applied to find a minimum-cost path to the goal. The positions of all detected pedestrians are anticipated over a 2 time- step prediction horizon, and the path is planned using a simple 4-stage reward model:
n1 To ensure safety a large penalty is applied if any pedestrian gets within a small
n2 To encourage the AV to reach its goal the reward increases with a decreasing
distance to it.
n3 To encourage the AV to maintain a high speed, when safe to do so, a greater
penalty is applied the slower it moves.
n4 A small penalty is applied for any +/- acceleration in order to encourage
smooth driving.
The AV is able to move along relatively smooth trajectories, however the maximum speed of the AV is 1.5ms−1 and an emergency brake is triggered when a pedestrian
gets within 0.5m of the buggy. As a result the AV still stops and starts, however does not simply wait, but re-plots alternative paths.
For road bound AVs, the stop-and wait navigation strategy is not suitable to be used in pedestrian crowds. Also, for all of these AVs the physical limitations do not allow sufficient adaptability in order to manoeuvre like a human. However, the latter system [216] does employ a reward strategy that is used in a crowd, although with its increased speed it is suitable for sparse crowds rather than much denser crowds where the CPP is intended to be implemented.