Multi-Robot Motion Planning for Real-Time Collision Avoidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing motion planning technologies for multiple robots in shared workspaces face challenges in efficiently avoiding collisions, particularly in dynamic environments, with current methods either leading to low throughput or requiring extensive computational time, and often necessitating manual optimization and re-validation upon changes in the workspace.
Innovation Solution
A system and method for generating collision-free motion plans for multiple robots in real-time, using processor-executable instructions and hardware circuits to perform collision detection and optimization, allowing robots to dynamically adjust their paths based on the changing environment and the motions of other robots, without the need for extensive training or time-intensive path optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If task-level de-confliction is used to control multiple robots in shared workspace, then ease of operation is improved, but productivity deteriorates due to low work throughput and idle time
Solution Approach 1:
The system segments the high-dimensional joint space planning problem into lower-dimensional individual robot motion plans. Each robot's motion is planned independently in its own configuration space, rather than solving the full combined joint space problem. This segmentation enables real-time computation while maintaining collision-free operation through iterative collision detection and motion plan adjustment.
2Productivity
If offline planning in combined joint space is used to achieve higher work throughput, then productivity is improved, but loss of time worsens due to extremely time-consuming computation
Solution Approach 1:
The system implements dynamic motion planning that computes collision-free trajectories in real-time during task execution. Rather than relying on static offline planning, the motion plans are dynamically adjusted based on current robot positions and environmental conditions. This dynamic approach enables the system to adapt to changing conditions while maintaining high throughput through efficient real-time computation.
Solution Approach 2:
The system employs iterative collision detection that provides feedback between motion planning and execution. Motion plans are generated, executed, and monitored for collisions in real-time. When collisions are detected or predicted, the system adjusts subsequent motion plans based on this feedback, enabling adaptive collision-free operation without requiring extensive pre-computation.
3Reliability
If manual optimization and simulation are used to ensure collision-free operation, then reliability is improved, but loss of time worsens due to many hours of computation time
Solution Approach 1:
The system implements autonomous motion planning that automatically generates and adjusts collision-free motion plans without requiring manual optimization or extensive simulation. The iterative collision detection and real-time plan adjustment enable the system to self-correct and ensure collision-free operation autonomously, eliminating the need for time-consuming manual tuning while maintaining high reliability.
4Device complexity
If conventional motion planning algorithms are used for high DOF problems, then device complexity is reduced, but productivity deteriorates as problems appear too large to solve
Solution Approach 1:
The system segments the high-dimensional joint space planning problem into lower-dimensional individual robot motion plans. Each robot's motion is planned independently in its own configuration space, rather than solving the full combined joint space problem. This segmentation enables real-time computation while maintaining collision-free operation through iterative collision detection and motion plan adjustment.
Solution Approach 2:
The system transitions from solving the planning problem in the original high-dimensional joint space to solving it in reduced configuration spaces for each individual robot. By changing the dimensional perspective and solving lower-dimensional problems separately, the system makes the computation tractable while still addressing the full multi-robot coordination challenge through iterative collision detection.
Data Source
AI summary
Collision detection useful in motion planning for robotics advantageously represents planned motions of each of a plurality of robots as obstacles when performing motion planning for any given robot in the plurality of robots that operate in a shared workspace, including taking into account the planned motions during collision assessment. Edges of a motion planning graph are assigned cost values, based at least in part on the collision assessment. Obstacles may be pruned as corresponding motions are completed. Motion planning requests may be queued, and some robots skipped, for example in response to an error or blocked condition.