Semantic Obstacle Recognition for Socially Aware Robot Path Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous robots face challenges in navigating environments safely and socially, as existing technologies fail to effectively differentiate and respond to various obstacles, such as people and objects, leading to potential collisions and intrusion into personal space.
Innovation Solution
A system that uses a planning engine to identify semantic classes of obstacles and apply corresponding planning policies, optimizing the robot's path to avoid collisions and respect social considerations by adjusting speed and distance based on obstacle types, using sensors like cameras and LIDAR to detect and classify objects in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot uses a generic obstacle avoidance approach without semantic classification, then the navigation system is simpler and faster, but the robot cannot distinguish between different types of obstacles (people vs. objects) and thus cannot apply appropriate social considerations or varying buffer zones
Solution Approach 1:
The patent segments obstacles into different semantic classes (e.g., people, pets, furniture, plants) and applies distinct planning policies to each class. This segmentation enables the robot to differentiate between obstacle types and apply appropriate social considerations, such as larger buffer zones for people and smaller zones for inanimate objects, thereby resolving the contradiction between adaptability and complexity by organizing the problem into manageable categories
Solution Approach 2:
The patent pre-defines a library of planning policies for different semantic obstacle classes before the robot operates in the environment. Each policy contains pre-configured parameters such as buffer zone distances and speed constraints. This preliminary preparation allows the robot to quickly retrieve and apply appropriate policies during real-time navigation without performing complex classification computations on the fly, thus maintaining simplicity while achieving high adaptability
2Reliability
If the robot maintains large buffer zones around all obstacles to ensure safety, then collision risk is reduced, but the robot's mobility and efficiency are reduced due to excessive path deviations
Solution Approach 1:
The patent applies the principle of local quality by setting different buffer zone sizes and safety constraints for different spatial regions around different types of obstacles. For example, a large buffer zone is maintained around people to ensure social acceptability and safety, while a smaller buffer zone is sufficient for furniture or plants. This localized differentiation allows the robot to optimize its path by taking fewer unnecessary deviations, thereby improving mobility and efficiency while maintaining appropriate safety margins for each obstacle type
Solution Approach 2:
The patent dynamically changes navigation parameters such as buffer zone distance and maximum approach speed based on the semantic class of the detected obstacle. The planning engine retrieves appropriate parameters from pre-defined policies associated with each obstacle class and applies them in real-time. This parameter adaptation enables the robot to maintain safety for vulnerable obstacles while improving productivity by using smaller margins for less critical obstacles, thus resolving the contradiction between reliability and productivity
3Adaptability or versatility
If the robot reacts to obstacles in real-time without pre-defined policies, then the system is more flexible, but the response time and computational latency increase
Solution Approach 1:
The patent pre-computes and stores a library of planning policies for different semantic obstacle classes, including parameters such as buffer zones, speed constraints, and path deviation rules. During real-time operation, the robot simply retrieves the appropriate pre-defined policy based on the detected obstacle class and applies it immediately. This preliminary preparation eliminates the need for complex real-time computations, thereby reducing latency while maintaining flexibility through the ability to select from multiple pre-configured policies
Solution Approach 2:
The patent uses pre-defined parameter sets for each obstacle class that are optimized in advance. When an obstacle is detected, the system quickly switches between these pre-configured parameter sets based on the obstacle's semantic class. This parameter switching approach provides real-time adaptability without requiring complex calculations during navigation, thus reducing computational latency while maintaining the flexibility to respond appropriately to different obstacle types
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables autonomous robots to plan paths that minimize collisions and respect personal space, improving reactivity and efficiency by differentiating obstacles based on semantic classes, reducing latency in adapting to changing environments, and ensuring socially acceptable navigation.
Implementation Method 1
using sensors like cameras and LIDAR to detect and classify objects in real-time
Data Source
AI summary
Systems, methods, devices, and techniques for planning travel of an autonomous robot. A system identifies one or more obstacles that are located in proximity of at least a portion of a planned route for the autonomous robot. For each obstacle, the system: (i) determines a semantic class of the obstacle, including selecting the semantic class from a library that defines a set of multiple possible semantic classes for obstacles, and (ii) selects a planning policy for the obstacle that corresponds to the semantic class of the obstacle. The system can generate a trajectory along the at least the portion of the planned route using the selected planning policies. The robot can then initiate travel according to the trajectory.