The goal of this article is to investigate the use of communication robots as tools to increase the safety of first responders (specifically, law enforcement officers (LEOs)) during social interactions or first-response situations. This article functions as the first stage of our investigation into communication robots for use by first responders. The LEOs in our study were provided with a mobile communication robot to determine if such robots could be of use in the public domain for the benefit of both civilians and LEOs. As part of our study, LEOs participated in training sessions where they worked with nonweaponized teleoperated communication robots and completed pretest-posttest surveys. The surveys captured LEOs’ perceptions and attitudes towards the robot and examined the impact of robot design features on LEOs’ acceptance of the communication robot. This article discusses the significance and potential use of communication robots as tools in the future of law enforcement. Our future works include conducting additional studies involving affected stakeholders (e.g., citizen communities) and assessing/mitigating any potential negative impact of communication robots.

To assist and protect citizen communities and police officers, robots have been developed for situational responses (e.g., explosive ordinance disposal). However, the robots used by law enforcement are typically expensive, can be difficult to operate, and do not readily facilitate communication between individuals. Recent research by the authors examined how communication impacts trust between robots and humans in the context of law enforcement. Using a mobile communication robot, law enforcement officers (LEOs) reported high levels of trust because the robot provided near face-to-face interaction using screens, microphones, and speakers. This paper seeks to expand upon the previous findings by discussing public perception, privacy, safety, and ethical considerations as they pertain to communication robots utilized in law enforcement. In the following, the authors explain their primary research thrusts and provide their plan for expanded stakeholder involvement for future research. For the future work, the authors will work with stakeholders to develop ethically grounded communication robots and accompanying education programs that enhance communication, trust, transparency, and accessibility between LEOs and citizens communities.

Recent advances in robotics have accelerated their widespread use in nontraditional domains such as law enforcement. The inclusion of robotics allows for the introduction of time and space in dangerous situations, and protects law enforcement officers (LEOs) from the many potentially dangerous situations they encounter. In this paper, a teleoperated robot prototype was designed and tested to allow LEOs to remotely and transparently communicate and interact with others. The robot featured near face-to-face interactivity and accuracy across multiple verbal and non-verbal modes using screens, microphones, and speakers. In cooperation with multiple law enforcement agencies, results are presented on this dynamic and integrative teleoperated communicative robot platform in terms of attitudes towards robots, trust in robot operation, and trust in human-robot-human interaction and communication.

Closed-loop control of functional electrical stimulation coupled with motorized assistance to induce cycling is a rehabilitative strategy that can improve the mobility of people with neurological conditions (NCs). However, robust control methods, which are currently pervasive in the cycling literature, have limited effectiveness due to the use of high stimulation intensity leading to accelerated fatigue during cycling protocols. This paper examines the design of a distributed repetitive learning controller (RLC) that commands an independent learning feedforward term to each of the six stimulated lower-limb muscle groups and an electric motor during the tracking of a periodic cadence trajectory. The switched controller activates lower limb muscles during kinematic efficient regions of the crank cycle and provides motorized assistance only when most needed (i.e., during the portions of the crank cycle where muscles evoke a low torque output). The controller exploits the periodicity of the desired cadence trajectory to learn from previous control inputs for each muscle group and electric motor. A Lyapunov-based stability analysis guarantees asymptotic tracking via an invariance-like corollary for nonsmooth systems. The switched distributed RLC was evaluated in experiments with seven able-bodied individuals and five participants with NCs. A mean root-mean-squared cadence error of 3.58 ± 0.43 revolutions per minute (RPM) (0.07 ± 7.35% average error) and 4.26 ± 0.84 RPM (0.1 ± 8.99% average error) was obtained for the healthy and neurologically impaired populations, respectively.