Developing battery-aware operations for autonomous and unmanned last-mile delivery systems
The economically viable, environmentally efficient, and socially equitable growth potential of e-commerce has drawn a growing number of retailers that compete for the market through increasingly consumer-focused services, particularly just-in-time delivery. However, such consumer-focused services necessitate frequent less-than-truckload last-mile deliveries that endanger sustainability of urban freight. To this end, emerging technologies such as autonomous delivery robots (robots) and unmanned aerial vehicles (drones) can help leverage the opportunities and cope with the challenges associated with last-mile distribution. However, unexpected battery drainage leading to failed delivery, loss of control, drone/robot damage, etc., is one of the significant barriers to adoption. Yet, there is a severe dearth of knowledge on real-world implementation of autonomous and unmanned systems for last-mile distribution. Therefore, the objective of this work is to 1) develop empirically validated microscopic energy models for drones and robots to consequently 2) formulate battery-aware drone and robot strategies, and thereby 3) assess sustainability-related opportunities and challenges associated with deploying such battery-aware drones and robots in last-mile distribution. To do so, this work will 1) conduct empirical tests collecting real-time data on drone/robot telemetry, battery state-of-charge, and energy-use, 2) employ analytical procedures modelling efficient payload management plans, speed selection policies, route choice logic, etc. for drones/robots, and 3) assess case studies comparing economic, environmental, and equity-related implications of battery-aware drone/robot-based distribution strategies with conventional delivery methods. In doing so, this work supports large-scale adoption of emerging technologies in urban freight. Specifically, by developing empirically validated microscopic energy models, this work will facilitate energy demand prediction in autonomous and unmanned delivery systems. Further, through battery-aware operational strategies, this work will enable optimal deployment of drones and robots in last-mile distribution with extended autonomy and improved operational reliability. And finally, by comprehensively assessing sustainability of autonomous and unmanned last-mile distribution strategies, this work will equip stakeholders to make informed policies towards sustainable urban freight management. Collectively, these contributions advance Government of India’s commitment towards improvement in logistic performance as outlined in the National Logistics Policy. Nonetheless, beyond logistics, this work also supports use of autonomous and unmanned systems for humanitarian logistics such as in disaster relief, healthcare, and emergency response. These contributions, therefore, underscore the broader societal impact of this work towards reinforcing sustainability and resilience through autonomous and unmanned systems.