Mobile Robot Speed Planning with Regional Path Constraints
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Solution Overview
Problem
Existing route planning approaches for autonomous or semi-autonomous robots are inefficient for long distances, fail to account for speed changes due to different environmental regions and dynamic obstacles, and lack extensibility for different drivetrain configurations, leading to suboptimal navigation and increased travel time.
Innovation Solution
Implementing regional path planning with Frenet coordinate discretization and using semantic information to dynamically adjust speed based on real-time perception, allowing for fine-grain path planning and obstacle avoidance, thereby generating efficient speed change decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If existing route planners search for routes covering long distances (1 kilometer or more), then the route can cover the required distance, but the processing of numerous possible routes becomes an intractable searching problem that cannot be solved in a short amount of time
Solution Approach 1:
The patent divides the long-distance route planning problem into multiple short-distance route planning segments. Each segment covers a limited distance (e.g., 5-10 meters) between consecutive waypoints. The global route is constructed by sequentially connecting these local segments, transforming an intractable long-distance search into multiple manageable short-distance searches that can be executed in real-time.
Solution Approach 2:
The patent pre-determines a sequence of waypoints along the global route from start to destination before executing local path planning. This preliminary action establishes the framework for分段 planning, allowing the system to break down the long-distance problem into predefined segments and process each segment independently with optimized local path planners.
2Device complexity
If existing route planners select routes without considering speed changes due to environmental regions and dynamic obstacles, then the route planning is computationally simpler, but the actual travel time increases because the robot must slow down when encountering regions of different types or dynamic obstacles
Solution Approach 1:
The patent applies different speed constraints to different portions of the route based on local environmental characteristics. Each route segment can have its own speed limits determined by the type of region (e.g., narrow corridors, open spaces, areas with dynamic obstacles). This allows the robot to optimize speed locally in each region while maintaining overall route efficiency, rather than applying a uniform speed constraint throughout the entire journey.
Solution Approach 2:
The patent introduces dynamic speed adjustment capabilities that allow the route planner to modify speed constraints in real-time based on detected dynamic obstacles and changing environmental conditions. The system can dynamically recalculate optimal speeds for upcoming segments when new information becomes available, enabling adaptive speed management that responds to real-time conditions while maintaining computational efficiency.
3Device complexity
If existing route planners use a single unified approach for all distances and environments, then the system is simpler to implement, but it lacks extensibility and adaptability to different drivetrain configurations and space representations
Solution Approach 1:
The patent creates a universal route planning framework that can accommodate multiple drivetrain configurations (differential drive, omnidirectional, tracked, etc.) and different space representations (2D grid, 3D space, navigation functions). The framework provides standardized interfaces and abstractions that allow different robot types and environments to use the same core planning architecture, achieving multi-functionality without increasing fundamental system complexity.
Solution Approach 2:
The patent enables adaptability to different drivetrain configurations and space representations by allowing parameter changes in the planning algorithms. The system can adjust planning parameters such as resolution, search depth, cost functions, and motion models based on the specific robot capabilities and environmental characteristics, providing flexibility without requiring fundamentally different planning approaches for each configuration.
Data Source
AI summary
In various examples, a technique for generating speed change decisions for a mobile robot includes identifying, using one or more maps of a physical environment, one or more obstacles associated with one or more portions of a path of the mobile robot in the physical environment. The technique also includes generating, based at least on the one or more obstacles, one or more speed constraints, each speed constraint specifying a speed limit for a respective portion of the path. The technique further includes generating one or more speed change decisions specifying actions to be performed by the mobile robot to cause a speed profile of the mobile robot to satisfy the one or more speed constraints.