Etapa de Construcción

In order to evaluate the safety measures, two experiments have been performed. The first one has been used to analyze the safety of the system and thus they have been carried out without real users. The second one involves real users in a controlled scenario.

In the following experiments, the passive measure was achieved reproducing a pre-learned trajectory and reproduced in a compliant mode, without including the tracking and low-level adaptation explained above. This was done for better evaluation of the active safety measures. Completely passive vs. partially passive safety

To perform this experiment a picture of a person opening the mouth has been fixed on a wood panel. This wood panel is strong enough to support the robot’s force without moving or bending. This experiment consists on the robot moving towards the picture with the same movement that it performs when entering the user’s mouth. However, in this experiment the robot will impact with the wood panel.

0 0 .1 0 .2 0 .3 0 .4 0 .5 0 .6 −30 −25 −20 −15 −10 −5 0 Time [s] Force [N]

Completely passive safety

Complete and partial passive safety Partially passive safety

Nothing

Figure B.1: Comparison of the force for the four setups in the perpendicular axis.

This experiment was performed by combining different setups including completely passive safety (compliant control) and partially passive safety by force limitation. The impact forces registered in the axis perpendicular to the user’s mouth are shown inFigure B.1. As it can be observed, the setups without a compliant controller decrease similarly and have the same impact force which is −5.8N . The partially passive safety by force limitation setup has an increase of force after its peak and remains in −1.6N as it enters the waiting position. On the other hand, the force in the setup without any passive security continues decreasing. The compliance setup

132 Safety in adaptive Physically Assistive Robots

Figure B.2: Successful execution of the feeding task

decreases slower reaching a peak of −5.1N . After this peak it remains in −4N as it is trying to reach the desired position. Finally, the compliance and force limited setup (complete and partial passive safety) has the slowest decrease reaching a peak of −4.8N . After 0.2s of applying a force between −4.8N and −4.5N , the force increases up to −1.6N and remains there as the robot has entered the waiting position.

With this results it is clear that both safety strategies offer a safe task performance as the peak forces never reach harmful levels. Therefore, setups with at least some degree of passive safety can be used.

However, during the majority of the experiments conducted with passive security there was food spilling and thus, the task could not be finished properly.

Passive safety offers a safer operation as the robot reaches lower forces. However, the difference of peak forces between the compliant and force limited is only of 1N , so it is not a determining factor. On the other hand, passive security does not increase the applied force over time which can discomfort the user. Moreover, there exist a great difference between compliant and non-compliant setups as the ones with compliance have less precision which causes food spilling.

Pilot user study on feeding safety

A prototype of the application was tested in 104 executions with 10 able-bodied participants. Each user was asked to perform some specific tests with anomalies and some free-form tests. An example of a successful execution can be observed inFigure B.2.

With the pilot study, we evaluate the different safety strategies available. First of all, we evaluate some preventive (active) safety. To do so, users were asked to look at a side and turn the head to compute the average reaction time of the robot to a change in the visual state of the user. In the head orientation experiment, we had an average reaction time of 0.46 seconds, with all the users’ movement detected correctly.

Then, the mouth openness detection was assessed in a similar way. In this case, the average response time was 0.44 seconds, although some users were not correctly detected by the face

B.2 Safety analysis for autonomous user feeding 133 0 5 10 15 20 25 30 −5 0 5 Time [s] Force [N]

Figure B.3: Force in the y-axis (perpendicular to the user) of the impact between the user face and the spoon (t = 2s) and the user retaining the spoon (t ∈ 15..25s)

landmarking library, which highlights the importance of having the low-level safety exposed above.

Finally, users agreed to perform tests to assess the forces involved in contacts occurred while feeding. We performed an impact experiment and also a spoon retention one. In the first one, the robot was impacting with the user’s face when entering the mouth. In the second, the user retained the spoon with their bite, not letting the robot perform the exiting motion. Note that the users were free to move away from the robot in case they felt threatened or potential harm was involved.

An example trace of the forces involved in this experiment is inFigure B.3. The first element is the impact, shown around the second 2 of the execution. In this case, the force reaches the −4.5N . Then, the robot remains in pre-feeding position and performs the motion again, this time entering the mouth (second 15). Around second 17, the robot tries to leave the mouth but feels the user retention so it enters the waiting position to avoid any harm, and retries until a successful exit motion can be performed (second 25). The higher peak in this case is of 5.7N when trying to leave the mouth.

After the experiments, the users were surveyed and all of them agreed in stating that the impacting and retaining forces were not harmful, and that they felt comfortable during the experiment. Therefore, this safety measures guarantee that in the case of an unavoidable impact, although not pleasant, it will not be harmful for the user. Moreover, it is also safe for the user to retain the spoon or even move it while it is inside the mouth.

C

ROSPlan’s Probabilistic Planning evaluation

This appendix extends Section 6.3 with an evaluation on the use of probabilistic planning in robotics domains. Hence, we demonstrate the usability of the proposed extension for probabilis- tic domains. We provide a comparison of the performance of the probabilistic anddeterministic

options on the same problem under different conditions followed by a discussion on the observed results. Note that this appendix does not intend to conclude whether any planning approach is better but rather to provide insights on when one may be more suitable than others.

TheROSPlanextension has been tested in a mobile robotics scenario where we have defined a challenging print-fetching domain where the robot is used as a service robot for fetching printed documents in an office (Figure C.1). HRIsupplements the lack of manipulation abilities of the used robot, thus allowing it to perform this task. A real-world evaluation is carried out in an environment with high uncertainty.

This evaluation is part of the work presented in [15].

Figure C.1: The scenario in which we test the proposed system is an office environment. A mobile robot, the TurtleBot 21 is used for the print-fetching service. When the robot gets a request for fetching prints, it decides from which printer to collect them. Since it is not equipped with an arm, it asks a random nearby person to put prints on it, and delivers them to the user.

C.1 Example System and Scenario

We have used theRDDLnodes in our example scenario, using the system architecture shown in

Figure 6.2. In this system, the RDDLKnowledge Baseloads theRDDLdomain and initial state. The Problem Interface requests the domain and state information to generate a RDDL problem instance. The Planner Interface and RDDL Plan Dispatch communicate through theIPPCserver

136 ROSPlan’s Probabilistic Planning evaluation P3 P1 P2 Kitchen Prof. Office PhD Area

(a) The layout of office environment where the robot is operating. The corridor is marked with the green color and printers are marked with yellow boxes. The orange boxes denote potential goal destinations.

P2 P3

P1

(b) A screenshot of the visualization tool RViz taken while performing experiments. It shows the map of the corridor and a green line indicating the robot’s current path.

Figure C.2: Map layouts of the proposed scenario description.

interface, as described above, suggesting and dispatching actions. The sensing interface is also being used to instantiate the predicates based on sensor data and update the state accordingly.

To demonstrate the effectiveness of the developed framework, we have tested it in a scenario in which a mobile robot fetches printed documents in a large office building. This scenario involves a high degree of uncertainty, since the environment is dynamic and humans can ob- struct the corridors and printers. The scenario also involves human-robot interaction, which is intrinsically uncertain.

Scenario description

The robot operates in a single-floor office environment with 16 offices shown in Figure C.2. There are three printers distributed along the corridor. The robot can trigger printing on any of these printers when a request is made. Since the mobile robot is not equipped with an arm, the robot can request human assistance to place the papers onto its tray. There are many employees working in this area, and the corridor is usually dynamic. The robot relies on the fact that someone will pass by and assist the robot upon request. However, it can happen that there is no one at the printer and the robot has to wait or go to another printer. Once the documents are on the carrier, the robot brings them to the person who made request. It is important to note that printers can be occupied, in which case the robot will have to wait. Moreover, the robot will know whether there is somebody there to assist or if the printer is busy until it has arrived to the printer. Figure C.1shows an example of the scenario.

This scenario could be well-suited to be modeled as aPartially Observable Markov Decision Process(POMDP), as there are fluents that cannot be known until observed, such as the presence or absence of people near the printer. Also, it could be modeled as anStochastic Shortest Path

(SSP) problem, given that the scenario is goal-oriented in that the robot has to deliver the printed papers to a specific location. However, given the lack of available out-of-the-box solvers

C.1 Example System and Scenario 137

for bothPOMDPsandSSPs, we have modeled the problem as anMDPwhere a positive reward is given only once the goal is reached.