Human-Aware Path Planning

It can be assumed that the autonomy cycle of all AMRs follow the standard au- tonomy cycle structure, Fig. 2.1b. The data processing within each stage, as well as the interaction between each stage, is then modified for the selective purposes of each AMR. As mentioned in, Section 2.1.2, careful consideration must be made towards what type of dynamic agents the AMR will share the environment with. The movement of an AMR around humans is very important [217] and should reflect human-like motion [218]. To improve robot acceptance the AMR’s behaviour must include naturalness, comfort, and sociability, as defined in [105]. An AMR cannot simply plan global paths that find the shortest distance to the goal, using geometric local collision avoidance as the AMR moves. Instead it must plan global paths that are considered "human" [219], using proxemics influenced local collision avoidance that respect socially acceptable human personal boundaries [150]. To generate "leg- ible" robot behaviour, whereby humans can understand clearly what an AMR is doing, assumptions made in the literature include:

o1 Model human-like behaviour [139, 220, 221] o2 Generate stereotypical motions [220, 222] o3 Generate efficient motions [139, 221, 223]

o4 Take into account social constraints, human abilities, and preferences [224–226] o5 Robot motion must be as visible as possible [227, 228]

2.4 Human-Mobile Robot Interaction 59 o6 Add complementary motions (gestures) in order to clarify intentions (e.g. gaze,

pointing, use animation principles) [223, 229–231]

all of which can be constrained to the path planning stage, bar the final item which would be a bi-product of the planning stage. These all aim to make the AMR appear to move as humanly and therefore as predictably as possible, such as making the AMR move ‘stereotypically’. This elaborates on the requirements for human-aware navigation discussed in Section 2.1.2.2, List d.

2.4.2.1 Planning Paths

An AMR will often be designed to navigate within a specific type of environment and interact with a specific type of obstacle. For instance, the Kiva robot system [54] mentioned in Section 2.1.2.1 is designed to operate within a "Human-Exclusion Zone", and the environment is kept relatively certain as the path for each robot is calculated using a centralised architecture. For an AMR to navigate within a pedestrian environment an appropriate model must be designed, Section 2.3.2, in order to anticipate what any one pedestrian may do next. However, no model can predict with 100% accuracy and so an AMR must continually re-plan multiple paths, each belonging to a different homotopy class in order to aide diversity. This strategy is superior to a greedy shortest-path as it allows the AMR to consider multiple alter- native choices as well as interactions with neighbouring pedestrians [154]. Despite the AMR only executing a small fraction of each path, the remaining unexecuted path serves two important functions [232]: 1) To guarantee safety the planner needs to look beyond the AMR’s minimum stopping distance; 2) The remaining path can approximate what future paths may be, although there is no guarantee the next planned path will contain along the remaining current path.

In a complex and changing real-world it is not possible for a programmer to anticipate all the situations a robot may encounter [233]. Therefore, the pedestrian model and the robot’s behaviour would be limited to set scenarios. As discussed in, Section 2.3.3.1, in order for pedestrians to move fluidly their collision avoidance strategies must be collaborative, with both pedestrian taking on a role and then cooperating. As AMR human-aware path planning is akin to real human path planning, this is essential in minimising the confusion that could occur if more rudimentary collision avoidance was implemented. In more conservative planners a passive reaction is preferred, implementing a "stop-and-wait" procedure and allowing the pedestrian to move before the AMR continues [7]. However, within a crowded environment this may add to congestion as a "traffic jam" scenario could develop, as well as confusing pedestrians moving up behind the AMR.

2.4.2.2 Assuming Cooperation

The cooperation paradigm is an optimistic assumption whereby potential collisions will be mutually resolved between the robot and pedestrian as they get closer to- gether, via trajectory and velocity adjustments [67]. This assumption will constrain the uncertainty in the pedestrian model, as well as allowing paths to be predicted that simulate human paths and behaviour. The now more ‘predictable’ pedestrian paths will allow more potential space to become available for the AMR to predict its own path. By assuming pedestrians cooperate it will be more efficient for the AMR to continue along a currently occluded path than it will be choosing another [67].

For an AMR to move towards its goal, a global path must be planned from its current position to the goal, Section 2.2.2.2. When assuming cooperation between robot and pedestrian this becomes much more feasible in a crowded environment due to the increased available space, and so also eliminating the potential "freezing robot problem". However, within such a dynamic environment it may not be possible for the AMR to predict a collision free path, in which case the AMR should pick one that provides the least number of collisions. Even if collision free paths are available, it may be preferable for the AMR to select a less "comfortable" path, as long as the path length is considerably shorter than a path with minimal conflict, and as long as it does not interfere with a pedestrian early on [154]. As the AMR should only partially move along its path before recalculation, Section 2.4.2.1, there is no necessity for the path to be collision free in the relative future.

The IGPm, discussed in Section 2.3.3.2, implemented a "joint-collision avoidance" strategy for cooperation. During simulations the method claimed to execute paths that were ‘safer’ than its pedestrian counterparts. However, when implemented on a real-life AMR platform [41] the resultant motion did not replicate the results due to a different dynamic structure of the crowd, as well as pedestrians individual reactions to the robot itself. Cooperative strategies rely on pedestrians choosing similar nav- igation principles, which also only occur under more structured environments [193], such as moving past one another in a corridor. Lane-based pedestrian traffic flow is required for the IGPm to be effective, with cooperation occurring between pedestrian and AMR meeting head-on. As discussed when analysing the empirical evidence of individual pedestrian collision avoidance, Section 2.3.3.1, pedestrians will behave differently when approaching from different angles. Therefore, all reactions that occur due to different angles of approach must be considered when developing a cooperative path planner.

2.4.2.3 Avoiding Collisions

The IGPm fails as it does not consider all potential angles of approach between the AMR and pedestrian. To prevent an AMR colliding with a pedestrian many

2.4 Human-Mobile Robot Interaction 61 papers (e.g. [7, 65, 66]) claim that the best method is for the AMR to "stop-and- wait" until the pedestrian has passed before continuing along its path. Although this is practical in a one-on-one scenario, this is clearly not suitable to a free-flowing dense crowd. Although this method is considerate to an individual pedestrian, considerate braking should be extended to considerate motion so that the AMR can replicate empirical human collision avoidance, Section 2.3.3.1. Taken from various studies (e.g. [18, 195]), generally when two human subjects cross paths they assume a role of either a reactive or non-reactive agent. The pedestrian furthest from the hypothetical collision point will remain non-reactive, whilst the other will adjust their trajectory to move behind the other pedestrian as trajectory variations are preferable to speed changes.

For successful HRI in this context, the pedestrian’s acceptance towards the AMR plays an important role [140]. The acceptance of the AMR is influenced by: trust; anxiety; perceived usefulness; and perceived enjoyment [234]. As well as "perceived safety" [140]. Safety is a basic human need [235] and can be designated as a key requirement of HRI, with the definition of perceived safety as: "Perceived safety describes the user’s perception of the level of danger when interacting with a robot, and the user’s level of comfort during the interaction" [236]. Due to this it can be assumed that extra care and consideration be taken by the AMR, and so AMR- human collision avoidance strategies must vary slightly from human-human ones even if attempting to replicate human behaviour.

The ContextCost Costmap This algorithm [157] is implemented on an AMR for a 90◦ _{path crossing scenario with a single pedestrian. However, as it is only}

capable of slowing the AMR down and not alter its trajectory. This was a choice based on the author’s previous publication [139] whereby analysis of 10 different pedestrians crossing each other in the same scenario, over 4 trials, resulted in only velocity deviations. This is a contradiction to far more comprehensive studies, Sec- tion 2.3.3.1, which concludes there is more significant evidence for preferences of trajectory changes instead. As a result the paper states that a "robot using cost model ContextCost would not deviate from the straight line, but instead reduce it’s velocity" and therefore a "robot path direction and human direction are considered incompatible if the human and robot could frontally run into each other". This will limit the application of the costmap to the specific scenario chosen, and unless the context of the scenario is know by the pedestrian a priori if the robot stops then its navigational intentions cannot be inferred by the pedestrian.

The Layered Social Costmap This algorithm [7] also behaves in the same way for the same path crossing scenario. Similarly, when the resultant AMR’s behaviour is compared to a path predicted using a static costmap, the same erratic motions

are produced as the AMR tries to cross in front of the pedestrian. The social costmap incorporates a constant velocity prediction model that projects an elliptical proxemics distribution in front of the for several time-steps. As this clearly makes passing in front more costly, if the AMR simply moved behind the pedestrian no interference would occur. This would not only allow the AMR to maintain its speed it would reach its goal faster and more efficiently. This is again why it is preferable to change trajectory over speed, which is a more common pedestrian trait, and would allow the model to be applied to more varied scenarios.

Conclusion The "legibility" in path planning discussed at the start of this sub- section is so that another pedestrian can correctly interpret the AMR’s intentions. For a "Human-Robot Path Crossing Task" [140] a video of an AMR approaching a moving pedestrian from the front at 3 angles: right-diagonal; head-on; and left- diagonal trajectory, is evaluated. Using a "Human-Aware" path planner that predicts paths based on "social costs", the resultant behaviour taken by the AMR was per- ceived as more human than with a more traditional path planner. Although this reduced any confusion for the pedestrian, and it can be assumed that a ‘human-like’ collision avoidance was observed, no resultant paths the AMR took were provided. Two AMRs working in close proximity [67] also adapt their trajectories in similar ways to pedestrians, by both adjusting their trajectories early on. However, detailed analytical comparisons between empirical pedestrian-pedestrian collision avoidance, Section 2.3.3.1, and robot-pedestrian collision avoidance for multi-angle path cross- ing scenarios has not yet been undertaken. Only social collision avoidance strategies, Section 2.2.3.1, have been developed to replicate human behaviour, focusing on the AMR replicating pedestrian trajectories rather than evaluating an AMR’s interac- tion when directly crossing paths.