Behavioral Conflict Avoidance Between Humans and Quadruped Robots in Shared Environments

2025-03-21

AI Summary

In human-robot shared environments, dynamic interactions between quadrupedal robots and moving humans frequently trigger safety conflicts; conventional obstacle avoidance often disrupts mission execution or causes excessive interference. Method: This paper proposes a trend-driven proactive behavior conflict avoidance framework. It introduces a novel dynamic potential field map integrating both static and dynamic obstacles, jointly optimized with a multi-objective evaluation function to select optimal evasion waypoints. A low-power hybrid solid-state LiDAR enables real-time human detection and vibration-robust tracking, facilitating accurate motion trend prediction, safe pausing, and autonomous task resumption—regardless of human stationary or moving states. Contribution/Results: Unlike goal-oriented aggressive avoidance, our paradigm balances safety with human activity continuity. Experimental results demonstrate a conflict avoidance success rate exceeding 96%, significantly reducing human intervention, eliminating deadlocks and forced detours, and enhancing both safety and task continuity in human-robot coexistence scenarios.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Nowadays, robots are increasingly operated in environments shared with humans, where conflicts between human and robot behaviors may compromise safety. This paper presents a proactive behavioral conflict avoidance framework based on the principle of adaptation to trends for quadruped robots that not only ensures the robot's safety but also minimizes interference with human activities. It can proactively avoid potential conflicts with approaching humans or other dynamic objects, whether the robot is stationary or in motion, then swiftly resume its tasks once the conflict subsides. An enhanced approach is proposed to achieve precise human detection and tracking on vibratory robot platform equipped with low-cost hybrid solid-state LiDAR. When potential conflict detected, the robot selects an avoidance point and executes an evasion maneuver before resuming its task. This approach contrasts with conventional methods that remain goal-driven, often resulting in aggressive behaviors, such as forcibly bypassing obstacles and causing conflicts or becoming stuck in deadlock scenarios. The selection of avoidance points is achieved by integrating static and dynamic obstacle to generate a potential field map. The robot then searches for feasible regions within this map and determines the optimal avoidance point using an evaluation function. Experimental results demonstrate that the framework significantly reduces interference with human activities, enhances the safety of both robots and persons.

Problem

• Avoiding human-robot conflicts in shared spaces
• Enhancing safety with proactive behavior adaptation
• Improving detection on vibrating robot platforms

Innovation

• Proactive behavioral conflict avoidance framework
• Low-cost hybrid solid-state LiDAR detection
• Potential field map for optimal avoidance